energy and environmental characterization of plug-in hybrid … · hybrid electric vehicles ricardo...
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Energy and environmental characterization of Plug-in
Hybrid Electric Vehicles
Ricardo Jorge Amaral Lopes
Thesis to obtain the Master of Science in
Mechanical Engineering
Examination committee
President: Professor Mário Manuel Gonçalves da Costa
Supervisor: Doctor Gonçalo Nuno Antunes Gonçalves
Co-Supervisor: Engineer Gonçalo Nuno de Oliveira Duarte
Member of the Committee: Doctor Carla Alexandra Monteiro da Silva
May 2013
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Acknowledgements
I would like to start to acknowledge my supervisor Doctor Gonçalo Gonçalves as well as my co-
supervisor Engineer Gonçalo Duarte. Your guidance and vision were very important for the
outcome of this work. I am sincerely grateful, for the opportunity to work and learn with both.
I would also like to thank to the colleagues from DTEA – Transports, Energy and Environment,
for the good working environment that you gave me. A special thanks to Doctor Patricia Baptista
and Professor Tiago Farias for the help and valuable suggestions.
I want to thank Toyota Caetano Portugal and Opel Portugal who lent the vehicles used in this
work, without them this work would not have been possible.
I would also like to thank all the friends I met during this journey at Instituto Superior Técnico, all
of you were important. Especially thanks to Carlos, Ricardo, Eduardo and André for the great
moments and mutual support in difficult times, you will never be forgotten.
Most importantly, I want to express my enormous gratitude to all my family. Especially to my
parents who were the most important persons during my development as an individual. I want to
thank them for all the opportunities they gave me. I will always be grateful for your efforts and
your belief in me, may this be your work as well.
Last but not least, thank you Marcella for your infinite patience, support and love. This work was
just possible thanks to you and your immeasurable enthusiasm.
To all, I express my sincere gratitude.
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Resumo
A Sociedade moderna enfrenta desafios relativamente à redução dos seus impactes
energéticos e emissão de poluentes, procurando alternativas sustentáveis. O sector dos
transportes enfrenta grandes desafios para reduzir emissões e custos, sendo os veículos
Híbridos Plug-in uma resposta da indústria automóvel a estas questões.
Neste trabalho foram estudados os dois veículos mais representativos desta tecnologia (Toyota
Prius Plug-in e Opel Ampera) relativamente ao impacte energético e emissão global de
poluentes, tendo sido efetuadas monitorizações em estrada, em condições reais de utilização
através de um laboratório portátil. Os resultados referentes aos dois veículos estudados foram
analisados para diferentes modos de condução, de acordo com a metodologia Vehicle Specific
Power.
Atendendo às dificuldades e aos riscos de medir diretamente os fluxos elétricos, foi
desenvolvida uma metodologia que permite estimar os consumos elétricos destes veículos,
assim como a autonomia elétrica. Quando comparado com dados medidos o erro máximo na
autonomia foi de 4,2% para o Toyota e de -0,2% para o Opel. No caso do consumo este teve
um erro máximo de -4,1%.
Simulando ambos os veículos com as metodologias desenvolvidas para condutores típicos,
verificou-se que o Toyota é mais eficiente em charge sustaining, mas não em charge depleting
comparando com o Opel. Este tem um comportamento muito próximo de um veículo elétrico
em charge depleting sendo pouco eficiente em charge sustaining. Verificou-se que o Toyota
tem uma dependência da agressividade com que é conduzido, podendo as reduções de
consumo de combustível atingir até 80% quando conduzido suavemente.
Palavras-chave: Veículos híbridos Plug-in, emissões globais, caracterização energética, Vehicle Specific Power, laboratório portátil de emissões, monitorização em estrada.
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Abstract
Modern society faces challenges in terms of energy impacts and pollutant emissions and should
search for sustainable alternatives. The transportation sector has several challenges for
emissions and transportation costs reduction. Plug-in Hybrid vehicles are one answer from the
automobile industry to this issue.
Through this work, the most representative vehicles of this technology (Toyota Prius Plug-in and
Opel Ampera) were studied in terms of their energy impact and pollutant emissions. For that,
on-road monitoring using a portable laboratory was performed. The results for both vehicles
were studied according with the Vehicle Specific Power methodology.
Taking into account the risks and the difficulties of obtaining electricity flows, a methodology that
allows calculating electricity consumption from these vehicles as well as estimating electric
range was developed. When compared to measured data, the maximum errors for the electric
range were 4.2% for the Toyota and -0.2% for the Opel. In terms of fuel consumption the
maximum error verified was -4.1%.
Applying the developed methodologies to both vehicles and for two drivers profile (Lisbon
metropolitan area and America driver), it was verified that the Toyota in charge sustaining mode
is more efficient then in charge depleting mode when compared to the Opel, which has a
behavior close to an electric vehicle in charge depleting, but it is not as efficient in charge
sustaining mode. It was observed that the Toyota consumption is highly dependent on the
aggressiveness with which it is driven; fuel consumption reductions can reach 80% when driven
softly.
Key-words: Plug-in Hybrid Vehicles, global emissions, energy characterization, Vehicle
Specific Power, portable laboratory for emissions, on-road monitoring.
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Content
1. Introduction .......................................................................................................................... 1
1.1. State of the art ............................................................................................................. 5
1.2. Objectives and Research Questions ....................................................................... 8
1.3. Structure of the thesis ................................................................................................ 9
2. Background concepts ...................................................................................................... 11
2.1. HEV and PHEV ......................................................................................................... 11
2.1.1. Different powertrain configurations .................................................................... 14
Series Hybrid ..................................................................................................................... 14
Parallel Hybrid ................................................................................................................... 15
Series / Parallel Hybrid .................................................................................................... 16
2.1.2. Charge Sustaining and Charge Depleting ........................................................ 16
2.2. Pollutant emissions, control and legislation .......................................................... 18
3. Methodology ...................................................................................................................... 29
3.1. Measuring apparatus and procedures................................................................... 29
Gas analyzer ..................................................................................................................... 30
OBD reader ....................................................................................................................... 31
GPS receiver ..................................................................................................................... 31
Laptop................................................................................................................................. 32
Roads and routes ............................................................................................................. 33
3.2. Data analysis – Vehicle Specific Power Methodology ........................................ 34
3.3. Methodology for electricity consumption prediction ............................................ 35
4. Results ............................................................................................................................... 41
4.1. Electricity consumption methodology .................................................................... 41
4.1.1. Validation with a Toyota Prius (HEV) ............................................................ 41
4.1.2. Comparison between methodology and measured data ............................ 42
4.2. Fuel consumption and pollutant emissions ........................................................... 44
4.2.2. Toyota Prius Plug-in ......................................................................................... 44
4.2.3. Opel Ampera ..................................................................................................... 57
4.3. Emissions due to electricity consumption ............................................................. 66
5. Case studies ...................................................................................................................... 71
5.1. Opel Ampera in a Real Trip .................................................................................... 71
5.2. Toyota Prius Plug-in in a Real Trip ........................................................................ 73
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5.3. Lisbon metropolitan area driver .............................................................................. 77
5.4. NCSU sample driver ................................................................................................ 80
6. Conclusions and future work .......................................................................................... 85
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List of tables
Table 1 - EURO standards for light duty vehicles since EURO1 to EURO6 ................... 23
Table 2 - Comparison between 1983 and a 2009 BMW .................................................... 25
Table 3 - Measuring range of different gases from Vetronix PX-A-1100 gas analyzer . 31
Table 4 - OBDKey OBD scanner specifications .................................................................. 31
Table 5- Vehicles Specifications ............................................................................................ 33
Table 6 - Distances and characteristics of monitored trips (Toyota Prius Plug-in) ........ 34
Table 7- Distances and characteristics of monitored trips (Opel Ampera) ...................... 34
Table 8 - Range of each VSP mode (18) .............................................................................. 35
Table 9 - Comparison between predicted and measured values for electricity
consumption .............................................................................................................................. 42
Table 10 - Delays tested and error of each estimation (Opel Ampera)............................ 61
Table 11 – Time average tested and error of each estimation (Opel Ampera) .............. 62
Table 12 - WTT Energy consumption and emission factors for the different energy
pathways considered (56) ....................................................................................................... 67
Table 13 - WTT Energy consumption and emission factors for the different power
mixes from 2003 to 2011 in gCO2/MJ ................................................................................... 68
Table 14 - Time distribution per VSP mode in a measured trip (Opel Ampera) ............. 71
Table 15 - Simulation results for a real trip in CD (Opel Ampera) .................................... 71
Table 16 - Simulation results for a real trip in CS (Opel Ampera) ..................................... 72
Table 17 - Time distribution per VSP mode in a measured soft trip (Toyota Prius Plug-
in) ................................................................................................................................................ 73
Table 18 - Simulation results for a real soft trip in CD (Toyota Prius Plug-in) ................ 73
Table 19 - Time distribution per VSP mode in a measured aggressive trip (Toyota Prius
Plug-in) ....................................................................................................................................... 74
Table 20 - Simulation results for a real aggressive trip in CD (Toyota Prius Plug-in) .... 74
Table 21 - Time distribution per VSP mode in a measured aggressive trip with CD and
CS (Toyota Prius Plug-in)........................................................................................................ 75
Table 22 - Simulation results for a real, aggressive trip in CD (Toyota Prius Plug-in) .. 75
Table 23 - Simulation results for a real, aggressive trip in CS (Toyota Prius Plug-in) ... 75
Table 24 - Simulation results for a real, aggressive trip accounting for CD and CS
contribution (Toyota Prius Plug-in) ........................................................................................ 75
Table 25 - Time distribution per VSP mode for a typical Lisbon metropolitan area driver
..................................................................................................................................................... 77
Table 26 - Simulation results for typical Lisbon metropolitan area driver in CD (Opel
Ampera) ..................................................................................................................................... 78
Table 27 - Simulation results for typical Lisbon metropolitan area driver in CS (Opel
Ampera) ..................................................................................................................................... 78
Table 28 - Simulation results for typical Lisbon metropolitan area driver in CD (Toyota
Prius Plug-in) ............................................................................................................................. 79
Table 29 - Simulation results for typical Lisbon metropolitan area driver in CS (Toyota
Prius Plug-in) ............................................................................................................................. 79
Table 30 - Time distribution per VSP mode for an American driver ................................. 80
Table 31 - Simulation results for an American driver in CD (Opel Ampera) .................... 81
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Table 32 - Simulation results for an American driver in CS (Opel Ampera) .................... 81
Table 33 - Simulation results for an American driver in CD (Toyota Prius Plug-in) ....... 82
Table 34 - Simulation results for an American driver in CS (Toyota Prius Plug-in) ....... 82
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List of Figures
Figure 1 - Evolution of the energy share from renewable sources in Portugal between
1999 and 2011 (with hydrological correction) (1) .................................................................. 1
Figure 2 - Total final consumption of oil by sector between 1971 and 2010 (Mtoe) (3) .. 2
Figure 3 - World transport energy use by mode, 1971-2006 (4) ......................................... 2
Figure 4 - Key crude oil spot prices in USD/barrel (3) .......................................................... 3
Figure 5 - Rotterdam oil product spot prices in USD/barrel (3) ........................................... 3
Figure 6 – Electricity prices in Portugal, France and EU27 (6) ........................................... 4
Figure 7 - Share of pollutant emissions from transport and non-transport sectors in
2010 (8) ........................................................................................................................................ 5
Figure 8 - Lohner-Porsche Mixte Hybrid (11) ....................................................................... 11
Figure 9 - Evolution of HEV sales in USA ............................................................................ 12
Figure 10 - Sales prediction for EV, PHEV, HEV, etc. by LMC Automotive .................... 13
Figure 11 - Energy density of batteries, liquid and gaseous fuels (4) .............................. 13
Figure 12 - Configuration of a series hybrid electric drive train (15) ................................. 15
Figure 13 - Configuration of a parallel hybrid electric drive train (25) .............................. 16
Figure 14 - Charge Depleting and Charge Sustaining of a range-extender PHEV ........ 17
Figure 15 - Charge Depleting of a blended PHEV .............................................................. 18
Figure 16 - Conversion efficiency for NO, CO and HC for a three-way catalyst as a
function of exhaust gas air/fuel ratio (34) .............................................................................. 22
Figure 17 - Tailpipe emissions from new passenger vehicles and future targets (41) .. 24
Figure 18 - Average CO2 emissions from 2000 until 2011 from different manufacturers
(41) .............................................................................................................................................. 25
Figure 19 - Schematics of a chassis dynamometer test ..................................................... 26
Figure 20 - Velocity profile of the NEDC ............................................................................... 26
Figure 21 - Main components of VE-LAB and typical installation places on light duty
vehicles ...................................................................................................................................... 30
Figure 22 - Vehicles measured. On the left is the Opel Ampera and on the right the
Toyota Prius Plug-in ................................................................................................................. 32
Figure 23 - Variation of SOC with VSP value (Toyota Prius Plug-in) ............................... 36
Figure 24 – Comparison between measured and predicted battery SOC (Toyota Prius
Plug-in) ....................................................................................................................................... 37
Figure 25 - Estimated battery SOC points compared to the measured values (Toyota
Prius Plug-in) ............................................................................................................................. 38
Figure 26 – Comparison between measured and predicted battery SOC (Toyota Prius
Plug-in) ....................................................................................................................................... 38
Figure 27 - Estimated battery SOC points compared to the measured values (Toyota
Prius Plug-in) ............................................................................................................................. 39
Figure 28 – Comparison between reported battery state of charge to the driver and
measured SOC by OBD, left: Toyota Prius Plug in, right: Opel Ampera ......................... 40
Figure 29 - Variation of SOC with VSP value (Opel Ampera) ........................................... 42
Figure 30 - Estimated battery SOC points compared to the measured values (Opel
Ampera) ..................................................................................................................................... 43
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Figure 31 - Estimated battery SOC points compared to the measured values (Opel
Ampera) ..................................................................................................................................... 43
Figure 32 - - Measured and predicted battery SOC (Opel Ampera) ................................. 44
Figure 33 - Time distribution per VSP mode in CS (Toyota Prius Plug-in) ...................... 45
Figure 34 - Percentage of time with ICE OFF in CS (Toyota Prius Plug-in) ................... 45
Figure 35 - Fuel consumption per VSP mode in CS (Toyota Prius Plug-in) ................... 46
Figure 36 - CO2 emissions per VSP mode in CS (Toyota Prius Plug-in) ......................... 46
Figure 37 - CO emissions per VSP mode in CS (Toyota Prius Plug-in) .......................... 47
Figure 38 - HC emissions per VSP mode in CS (Toyota Prius Plug-in) .......................... 47
Figure 39 - NOx emissions per VSP mode in CS (Toyota Prius Plug-in) ........................ 48
Figure 40 - Time distribution per VSP mode in CD (Toyota Prius Plug-in) ..................... 48
Figure 41 - Fuel consumption per VSP mode in CD depending on battery SOC (Toyota
Prius Plug-in) ............................................................................................................................. 49
Figure 42 - CO2 emissions per VSP mode in CD depending on battery SOC (Toyota
Prius Plug-in) ............................................................................................................................. 50
Figure 43 - CO emissions per VSP mode in CD depending on battery SOC (Toyota
Prius Plug-in) ............................................................................................................................. 50
Figure 44 - HC emissions per VSP mode in CD depending on battery SOC (Toyota
Prius Plug-in) ............................................................................................................................. 51
Figure 45 - NO emissions per VSP mode in CD depending on battery SOC (Toyota
Prius Plug-in) ............................................................................................................................. 51
Figure 46 - Fuel consumption per VSP mode in CS and CD (Toyota Prius Plug-in) ..... 52
Figure 47 - Percentage of time with ICE OFF in CD (Toyota Prius Plug-in) for an
aggressive driving ..................................................................................................................... 52
Figure 48 - Percentage of time with ICE OFF in CD (Toyota Prius Plug-in) for a non-
aggressive driving ..................................................................................................................... 53
Figure 49 - CO2 emissions per VSP mode in CS and CD (Toyota Prius Plug-in) .......... 54
Figure 50 - CO emissions per VSP mode in CS and CD (Toyota Prius Plug-in) ........... 54
Figure 51 - HC emissions per VSP mode in CS and CD (Toyota Prius Plug-in) ............ 55
Figure 52 - NO emissions per VSP mode in CS and CD (Toyota Prius Plug-in) ........... 55
Figure 53 - Electricity consumption per VSP mode in CD (Toyota Prius Plug-in) .......... 56
Figure 54 - Time distribution per VSP mode in CS (Opel Ampera) .................................. 57
Figure 55 - Fuel consumption per VSP mode in CS (Opel Ampera) ................................ 58
Figure 56 - CO2 emissions per VSP mode in CS (Opel Ampera) ..................................... 59
Figure 57 - CO emissions per VSP mode in CS (Opel Ampera) ...................................... 59
Figure 58 - HC emissions per VSP mode in CS (Opel Ampera) ....................................... 60
Figure 59 - NO emissions per VSP mode in CS (Opel Ampera) ...................................... 60
Figure 60 - Fuel consumption per VSP mode for different tested solutions (Opel
Ampera) ..................................................................................................................................... 62
Figure 61 - Variation of battery SOC per VSP value in CS (Opel Ampera) .................... 63
Figure 62 - Time distribution per VSP mode in CD (Opel Ampera) .................................. 64
Figure 63 - Electricity consumption per VSP mode in CD (Opel Ampera) ...................... 65
Figure 64 - Time distribution per VSP mode in Mountain mode ....................................... 66
Figure 65 - Fuel consumption per VSP mode in Mountain mode ..................................... 66
Figure 66 - Energy use (MJ) for different vehicles in different driving modes accounting
for the energy source on NEDC driving profile .................................................................... 68
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Figure 67 - WTW CO2 emissions in CD and CS for the Opel Ampera and Toyota Prius
Plug-in performing a NEDC. ................................................................................................... 69
Figure 68 - WTW CO2 emissions in CS and in CD for different power mixes (Opel
Ampera and Toyota Prius Plug-in) ......................................................................................... 69
Figure 69 - Effect of battery charge in terms of fuel consumption and CO2 emissions
(Opel Ampera) ........................................................................................................................... 72
Figure 70 - Effect of country power mix for global CO2 emissions reduction (100%
charge) (Opel Ampera) ............................................................................................................ 73
Figure 71 - Effect of battery charge in terms of fuel consumption and CO2 emissions
(Toyota Prius Plug-in in an aggressive driving) ................................................................... 76
Figure 72 - Effect of country power mix in terms of CO2 emissions (Toyota Prius Plug-in
driven aggressively) ................................................................................................................. 76
Figure 73 - Effect of driving behavior on fuel consumption reduction (Toyota Prius Plug-
in) ................................................................................................................................................ 77
Figure 74 - CO2 emissions in CD and in CS in a WTW basis for 2010 Portuguese mix
for a typical Lisbon driver (Opel Ampera) ............................................................................. 78
Figure 75 - CO2 emissions in CD and in CS in a WTW basis for 2010 Portuguese mix
for a typical Lisbon driver (Toyota Prius Plug-in) ................................................................. 80
Figure 76 - CO2 emissions in CD and in CS in a WTW basis for 2010 Portuguese mix
for an American driver (Opel Ampera) .................................................................................. 82
Figure 77 - CO2 emissions in CD and in CS in a WTW basis for 2010 Portuguese mix
for an American driver (Toyota Prius Plug-in) ...................................................................... 83
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List of acronyms
BEV Battery Electric Vehicle
CD Charge Depleting
CI Compressed Ignition
CS Charge Sustaining
CV Conventional Vehicle
EG Electric Generator
EM Electric Motor
EU European Union
EV Electric Vehicle
FTP Federal Test Procedure
GPS Global Positioning System
HEV Hybrid Electric Vehicle
ICE Internal Combustion Engine
IMA Integrated Motor Assist
LMA Lisbon Metropolitan Area
NEDC New European Driving Cycle
OBD On-Board Diagnosis
PHEV Plug-in Hybrid Electric Vehicle
SI Spark Ignition
SOC State of Charge
TTW Tank-To-Wheel
TWC Three Way Catalytic
UK United Kingdom
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USD United States Dollar
VSP Vehicle Specific Power
WLTP Worldwide harmonized Light vehicles Test Procedures
WTT Well-To-Tank
WTW Well-To-Wheel
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1. Introduction
In recent years, modern society has been facing several economic and environmental
challenges. Amongst these challenges there is the reduction of pollutant emissions as well as
improving the efficiency of energy use. Both these challenges are related to each other as most
of the world energy comes from burning fossil fuels, consequently emitting pollutants that can
be noxious for human health and for the environment. This situation has been changing
significantly in the last decade, due to the increase of energy production from renewable
sources, which is largely encouraged by governmental initiative.
In the Portuguese case, as can be seen in Figure 1 by Associação de Energias Renovaveis (1),
the percentage of electricity from renewable sources have been increasing, from around 30% in
1999 to around 50% in 2011.
Figure 1 - Evolution of the energy share from renewable sources in Portugal between 1999 and
2011 (with hydrological correction) (1)
This tendency is followed by several countries, namely in the European Union, with agreements
to achieve goals of renewable shares of 20% in total energy consumption by 2020 (2009/28/EC
(2)) but due to the recent economic crisis, the investment on these technologies may have a
significant reduction slowing the growth tendency of the renewable energy share.
The world´s energy use has been increasing significantly in the last decades, the transport
sector especially the road passenger sector is the biggest responsible for this growth, as can be
seen in Figure 2 and Figure 3.
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Figure 2 - Total final consumption of oil by sector between 1971 and 2010 (Mtoe) (3)
From 1971 to 2005, the energy use by the transport sector more than doubled. This tendency
can be seen of Figure 3 which represents the energy use by the sector transport subdivided in
its different modes.
Figure 3 - World transport energy use by mode, 1971-2006 (4)
In Figure 3, it can be seen the importance the road passenger transport has on the total energy
consumed by this sector. For this reason, in the transportation sector, the challenges faced are
the same as referred above: improve overall efficiency, reduce pollutant emissions and reduce
operational costs. Therefore the effort from the manufacturers is to produce more efficient
vehicles that can comply with regulations ( (5) for European Union), and are also able to provide
a cost effective way of transport (cost per km). The last issue is increasing in importance for
consumers as the oil prices increased significantly in the recent years. Figure 4 shows the
evolution of the crude oil prices since the 90’s until 2012. It can be seen that prices increased
from around 20 dollars per barrel to 100 dollars in 2012. This represents a growth of 500% in 22
years.
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Figure 4 - Key crude oil spot prices in USD/barrel (3)
Since fossil fuels like gasoline and diesel derive from crude oil, any fluctuation on its price will
be reflected on the end products, as it is the case of gasoline, diesel, jet fuel and kerosene
amongst other important products to our society especially in terms of energy.
Figure 5 provides an overview of several end product prices (heavy fuel oil, gasoil and gasoline)
presenting a strong relation with crude oil prices as can be easily verified.
Figure 5 - Rotterdam oil product spot prices in USD/barrel (3)
As seen in Figure 5 fuel prices increased significantly in the last decade but if we look to
electricity prices, their increase is not as significant. Figure 6 presents the tendency of electricity
prices in the last decade, according to EUROSTAT (6). Assuming this trend is maintained,
electricity powered vehicles will increase in their importance due to reduced operational costs in
terms of their energy consumption when compared to oil based fuel vehicles.
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Figure 6 – Electricity prices in Portugal, France and EU27 (6)
In order to reduce operational costs (costs per kilometer) and emissions from vehicles,
manufacturers started to adopt different solutions as it is the case of Hybrid technology in
automobiles, which refers to the use of two distinct power sources. The most common type of
Hybrid combines an internal combustion engine (ICE) with an electric motor (EM). For that,
besides having a gas tank to store liquid fuel to power the ICE, it also uses batteries to store
energy to power the EM. Hybrid electric vehicles will be further presented in section 2.1.
As oil consumption increases, pollutant emissions resulting from burning fossil fuels will also
increase. This is of huge importance in our society as the source of most greenhouse gases
result from burning fossil fuels and therefore, efforts have to be made in order to reduce energy
consumption from non-renewable sources and its associated emissions.
Anthropogenic emissions are the ones provoked by humans as in industrial processes or motor
vehicles (7). Associated with the burning process of any fossil fuel there are pollutants emitted
to the atmosphere. These pollutants can be classified as global or regional depending on where
their impact is more significant. Amongst the most relevant global level emissions are carbon
dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Regarding the ones that have impact on
a regional level, there are carbon monoxide (CO), unburned hydrocarbons (HC), nitrogen
oxides (NOx), sulfur dioxide (SO2) and particulate matter (PM) (7).
The transport sector has a very high share of the anthropogenic pollutant emissions. Regarding
road transport, efforts have been made in order to control and/or reduce such emissions by
introducing regulations and by vehicle manufacturer’s efforts, as will be discussed within this
section. Figure 7, authored by the European Environment Agency (8), presents the share of
emissions from the transport sector, being subsequently divided by road, railway, shipping and
aviation transport, in 2010.
As seen in Figure 7, NOx emissions from transport sector represent 58% of all NOx emissions,
which makes this sector the main source for this pollutant. The share of other pollutants by the
transport sector is very important.
0
0,05
0,1
0,15
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Pri
ce (€
/kW
h)
EU 27
Portugal
France
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Figure 7 - Share of pollutant emissions from transport and non-transport sectors in 2010 (8)
Due to energy and environmental concerns, new vehicle technologies, such as Plug-in Electric
Hybrid Vehicles, which are an intermediate solution between pure electric and Conventional
Hybrid Electric Vehicles, can be seen as a solution for some of the issues addressed in this
chapter, making use of renewable energy to charge the batteries.
1.1. State of the art
As awareness about the problems associated with fossil fuel consumption and consequent
pollutant emissions becomes greater, efforts to reduce both have also increased. For the road
transport, namely road passenger transport, efforts have been made in order to reduce
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consumption and enhance efficiency of engines. Hybrid electric vehicles (HEV) are a clear
example of such efforts to improve fuel consumption. The energy used by the electrical system
in these vehicles is dependent on the amount of energy that can be regenerated from downhill
and braking situations. The electric motor on HEV is mainly used on low power situations or to
assist the engine in point where it is less efficient.
Plug-in Hybrid Electric Vehicles (PHEV) are a natural evolution from HEV. These vehicles have
characteristics similar to HEV but have the possibility of being “plugged-in”, which allows them
to be externally charged, making it possible to increase full electric driving range, as well as the
number of situations in which the electric motor is solicited. PHEV are considered to be a good
solution to reduce CO2 emissions from the transport sector.
Studies like the one made by Doucette et al (9), in which CO2 emissions from electric and plug-
in Hybrid vehicles are modeled and compare with published values for CO2 emissions from
conventional ICE vehicles (CV), show the advantage of such solution for the future CO2
emissions reduction. The fact that PHEVs require less batteries than EVs and due to the more
efficient operation of their on-board ICE (generally) when compared to CV, allow the
theorization that given a certain power generation mix with different CO2 intensities, PHEVs
may emit less CO2 than both CVs and EVs (9). If the example of France is taken, where the
power generation has a low CO2 intensity, EVs were found to be the best option for reducing
CO2 emissions from automobile transport (9). For a mid-range CO2 intensity power generation
mix, as in the United States, PHEV are able to emit from 3 to 6 gCO2 per km less than an EV
during their electric-only mode (9). However, if the full driving range of the EV is taken into
account, these vehicles will be the most effective in reducing automotive CO2 emissions (9).
If the example of daily driving is considered, and if the range necessary for the vehicle is easily
fulfilled with a PHEV, these would be the best option in such kind of power generation. This can
be explained with the reduced weight compared to EV due to smaller batteries. The last case is
the one with a high CO2 intensity power generation mix, as for example in China, it was found
that PHEVs would emit less over their entire range than both a similar EV and CV (9). This
shows that, to take full advantage of the EV and PHEV capacity on reducing carbon emissions,
in highly CO2 intensive countries, can more effectively be achieved if decarburization of their
power generation is highly pursued (9). Studies like K. Yabe et al. (10) shows how much the
CO2 intensity of power generation affects the CO2 reduction rate by the introduction of EV and
PHEV vehicles.
The best choice of battery sizing depends critically on the distance that the vehicle will be driven
between charges. It was also seen that PHEV perform better when their batteries are sized
according to the driver charging patterns (11). Methods for evaluation battery size that best
adapt to driver’s needs include simulation tools, which can provide estimates of vehicle
performance (in terms of energy use and pollutant outcomes). Reference simulation tools
include Copert, MOVES and ADVISOR (12) (13) (14). However, in order to have significant
7
results from numerical tools, it is usually necessary to have on-road or laboratorial inputs of a
given vehicle technology. Regarding PHEV, there are still few studies that characterize Plug-in
Hybrids under real-world operation.
Data collecting from vehicle on-road monitoring can be analyzed with the Vehicle Specific
Power methodology. An example of the application of on-road monitoring and Vehicle Specific
Power (VSP) methodology was performed by Gonçalves (15) to characterize conventional ICE
vehicles running on different fuel blends, for example, in order to compare which will have the
highest amount of fuel consumption for the same power situations. A portable laboratory was
developed to monitor important parameters during vehicle driving as for example Speed, engine
RPMs, exhaust gases concentration, engine load and altitude amongst others.
Plug-in Hybrid Electric Vehicles were monitored using a portable laboratory by Frey et al (16).
The particularity of this study was that the vehicle used was altered from a conventional HEV,
meaning that this was not a series production PHEV and therefore not projected to work as one.
This same study provides some information about which things should be taken into account
when considering PHEV as for example the clear separation between charge sustaining and
charge depleting mode. For this reason PHEV cannot be considered as “black boxes” as it
happens when monitoring HEV (17)
This study uses VSP methodology to group points with similar power output due to vehicle
dynamics and road topography (18). This methodology developed by Jimenez-Palacios (19) will
be explained in more detail in section 3.2 and allows computing the necessary power to
maintain a certain driving situation. Dividing these power situations into modes allows
characterizing fuel consumption and pollutant emissions for similar power situation modes
allowing future simulation of those vehicles. For these simulations, information about the exact
road and conditions where the vehicle is driven is not necessary, being only necessary
information about the time spent on different VSP modes. Therefore, the VSP methodology is
independent of the route where the vehicle is driven. The use to this methodology for vehicle
simulation is widely used.
There is a lack of studies for Plug-in Hybrid Electric Vehicles (PHEV) developed from start to
work as such and which only started to be available to the consumer recently. Therefore, the
focus of this work is to perform an energy and environmental characterization under real-world
operation, of the most representative PHEVs available. For that reason a methodology to
characterize the working parameters of such vehicles should be developed in order to provide
the necessary information for the simulation tools as well as a systematic way of evaluating
future models yet to be released.
8
1.2. Objectives and Research Questions
The main objectives of this research work are:
1. Quantify fuel consumption and pollutant emissions for different operating modes based
on measured data;
2. Develop a methodology to estimate energy flows in and out of the battery based on
Vehicle Specific Power (VSP) and battery State Of Charge (SOC) information;
3. Develop a methodology to estimate autonomy in Charge Depleting (CD) mode based
on a given drive cycle;
4. Estimate fuel and electricity consumption as well as pollutant emissions from these
vehicles when driven by any driver based on drive cycle data.
In order to achieve this objective, the main questions that I would like to answer are the
following:
Is it possible to quantify Charge Depleting (CD) and Charge Sustaining (CS) fuel
consumption and associated emissions and electricity consumption using the Vehicle
Specific Power methodology (VSP) for PHEV?
Using a portable laboratory to perform on-road measurements while the vehicle is being
operated, fuel consumption and tailpipe emissions will be assessed in a second-by-second
basis. The experimental study will account for trips with different initial battery SOC levels and
routes with characteristics to account for a comprehensible range of driving conditions. This
way, this work pretends to perform a characterization of both CD and CS operation. Data from
battery SOC will be collected from OBD data in each second of driving.
Using battery State Of Charge (SOC) data from On-Board Diagnostics (OBD) interface is
it possible to indirectly measure battery energy flows and consequently CD driving
autonomy?
To estimate the electric autonomy it is necessary to address electric consumption according to
the driving demands. There are risks associated to measuring battery current and voltage
without the technical skills and deep knowledge of the vehicle electric system design. Therefore,
under the scope of this work a methodology will be developed to estimate the energy flows from
the battery to the wheels and from the wheels to the batteries, using only SOC and VSP. This
technique will allow to indirectly measure battery energy flows without the need to install
dedicated sensors. With this information, it will be possible to estimate CD driving range based
on driving cycle characteristics. This will be important because CD and CS driving will have
different repercussions on energy use and pollutant emissions. This will allow to correctly
estimate the impacts of any trip, without have to measure a given vehicle in a predefined route.
On top of these, it is intended to simulate “typical drivers” in order to provide a direct application
of the developed methodologies and results obtained.
9
1.3. Structure of the thesis
The present thesis is divided in six chapters each of them containing several subsections.
On Chapter 1, an introduction to the work is made in order to frame the present work with the
challenges faced by the transportation sector, more specifically the road transport.
Chapter 2 includes background concepts to better understand this work. An introduction to
Hybrid vehicles is done, starting by conventional Hybrid electric vehicles until the more recent
Plug-in Hybrid electric vehicles (PHEV). An explanation of different power train configurations is
done as well as presentation of different operational modes of PHEV (Charge depleting and
Charge sustaining). An introduction to the problem of pollutant emissions is also made, with an
overview of the most common ones followed by the description of imposed regulations on the
road transportation sector as well as procedures to verify vehicle compliance with those
regulations.
Chapter 3 is where the description of the methodology followed throughout this work is done. It
is explained the experimental procedures as well as used devices. The data analysis method is
explained in more detail as well as a developed methodology to indirectly measure energy flows
from these vehicles batteries.
In Chapter 4 the results of this work are presented. In the beginning of the chapter the validation
of the proposed methodology to predict electricity consumption is done. Further on the chapter,
the results for both vehicles measured are presented, starting by the Toyota Prius Plug-in in all
its driving modes and followed by the Opel Ampera also in all its driving modes. In the end of
the chapter considerations about emissions due to electricity consumption are also made in
order to assess CO2 emissions.
In Chapter 5 several case studies are presented in which the developed methodologies were
tested. These simulations include a typical driver in the Lisbon metropolitan area, a sample
driver collected in the United States and several other simulations of real trips measured on-
board in order to assess the accuracy and applicability of the developed methodologies.
In Chapter 6 the conclusion from this work are presented and discussed. In addition, possible
future work is included.
10
11
2. Background concepts
2.1. HEV and PHEV
A Hybrid vehicle is equipped with two or more different power sources for its propulsion (20).
The most common type of hybrid vehicles are the ones equipped with an Internal Combustion
Engine (ICE) as well as an Electric Motor (EM).
Even though the first hybrid vehicle was developed around 1900, as shown in Figure8, by the
famous Ferdinand Porsche (21), this technology didn’t reach mass production until the advent
of the first successful series produced hybrid vehicles. Ferdinand Porsche was a famous
automotive engineer that besides creating the first Hybrid vehicle, also created other famous
vehicles like the Volkswagen Beetle and the Mercedes-Benz SS/SSK, in addiction to all that, he
gave his name to one of the most famous car brands of our days (22).
In recent decades, due to growing environmental concerns and need to reduce gaseous
emissions, hybrid cars started to gain more expression on the automobile market. Models like
the Toyota Prius or Honda Insight and Honda Civic IMA (first 3 Hybrid electric vehicles sold
internationally), allowed the consumers to acquire such technology with the intent to have a
lower consumption and more “environmental friendly” car.
Consumers have been accepting Hybrid Electric Vehicles (HEV), as can be verified by the
continuous grow of sales of such vehicles. Not just consumers are accepting these
technologies; also the manufacturers have been investing in the production of new models of
hybrid vehicles. The number of models sold in the US, passed from 1 in 1999 (Honda Insight)
and from 2 in 2000 (Honda Insight and Toyota Prius) to 38 in 2011 (23). Figure 9 presents the
sales growth of HEV in the American market, from 1999 until 2011. This evolution shows the
acceptance of the public about these vehicles.
Figure 8 - Lohner-Porsche Mixte Hybrid (11)
12
Figure 9 - Evolution of HEV sales in USA
These Hybrid cars available to consumers are equipped with an ICE and an EM for propulsion
of the vehicle. On the Toyota Prius case, the EM is mainly used in low power situations, in
which the ICE is less efficient and therefore switched OFF. In such situation the car is in full
electric mode. In the Honda Insight, their Integrated Motor Assist (IMA) (24) helps the ICE on
the propulsion of the vehicle, but does not work independently from the ICE like in the Toyota
Prius. Both systems have the intent of reducing fuel consumption by “helping” the ICE when it is
running on its less efficient region.
On Hybrid Electric Vehicles, the energy stored on-board is provided by the vehicle means, such
as regeneration from downhill driving and/or braking situations (see for more information about
the regenerative (25), (26)) or using the ICE as generator. This means that the fuel savings and
CO2 emissions reductions are limited. Hybrid electric vehicles have a high fuel saving potential,
and that is what car manufacturers started to explore with the introduction of Plug-In Hybrid
Electric Vehicles (PHEV). One can say that these vehicles represent a “bridge” between
conventional HEV and Electric vehicles (EV), as they combine higher full electric driving
autonomy, due to their bigger battery pack (as in EV), with the extended driving range, due to
their ICE, as seen on HEV. The higher full electric driving range is achieved recurring to bigger
and heavier batteries that have a higher capacity. These batteries, when full, allow the vehicle
to be driven uniquely using energy stored in them. This way the car can be driven with zero1
emissions for longer distances than HEV. Examples of cars using this technology are the
Toyota Prius Plug-in and the Opel Ampera (Chevrolet Volt in some other markets as in the
American market). Even though both the referred vehicles are PHEV’s they are different in their
configuration, this fact will be made clear in section 2.1.1., when power-train configurations are
explained.
In the future it is expected that the sales of HEV, PHEV as well as EV will grow significantly. The
continuous grow of energy prices and increasing environmental concerns can explain this
tendency.
1 In full electric mode, emissions will be in fact zero at the time and local of energy use. This is not
completely truth at a global level as its emissions will depend on the energy mix of power generation from where the vehicle was charged.
0
100.000
200.000
300.000
400.000
1998 2000 2002 2004 2006 2008 2010 2012
Un
its
sold
Year
13
Figure 10 shows a prediction by LMC Automotive (27), for the global sales of these kinds of
vehicles (EV, PHEV and HEV). Electric Vehicles are referred to, in Figure 10, as battery electric
vehicle (BEV).
This prediction expects that the sales of these type of vehicles (HEV, PHEV, BEV (EV)) will be
in 2014 more than double of what was observed in 2011 (bellow 1 million) , being more than
four times by 2018 (over 4 million).
HEV, PHEV and EV are vehicles equipped with batteries, where electrical energy is stored in
order to power the EM on board. This does not occur in conventional ICE vehicles2 (CV), in
which gasoline or diesel are their only source of energy. Those batteries have increasing energy
capacity, sizes and consequently weight from HEV to EV being PHEV in between those,
typically resulting in an increase on the weight of the vehicle and consequently the power
required for propulsion. This weight increase is not only due to the battery weight but also from
the structural reinforcements required to support this weight (11). In Figure 11 a comparison
between the energy density of batteries with liquid and gaseous fuels can be found. It can be
seen that liquid fuels have more energy density then Lithium batteries, about 10 times more
energy per kilogram of material.
Figure 11 - Energy density of batteries, liquid and gaseous fuels (4)
2 In practice CV are equipped with a lead-acid battery. This batteries are just used to power auxiliary
equipment, as radio, power windows and also the starter motor amongst others.
Figure 10 - Sales prediction for EV, PHEV, HEV, etc. by LMC Automotive
14
In spite of the weight increase due to the battery, PHEV and EV are still very interesting and
efficient in terms of fuel consumption and CO2 emissions when compared with CV (9). This is
even more evident on short distance trips, as typical daily trips in Europe which in average have
34.5 km in 2007 (28).
Besides the power density problem, there is another challenge as the battery prices are still not
competitive for the complete electrification of road transport. For example, for a driving range of
500km (similar to CV with ICE) a battery will have to be of at least 75 kWh capacity and with the
expected near-term battery prices of approximately 500 USD/kWh, the battery alone would cost
between 35 000 to 40 000 USD (~27 000 – 31 000 Euros) (29).
2.1.1. Different powertrain configurations
HEVs and consequently PHEVs can be configured in several ways, more specifically in the way
the power-train is organized. As seen before, PHEV as well as HEV are equipped with 2
propulsion sources, an Electric Motor (EM) and an Internal Combustion Engine (ICE). This
originates different power-train configurations. The main challenge on the power-train design is
to manage this multiple energy sources, which depends highly on the driving cycle, ICE sizing,
battery sizing, motor sizing and battery management. The ultimate goal is to maximize the
efficiency of the whole driving system (30).
The power train configuration can be divided roughly in 3 groups, namely series hybrid, parallel
hybrid or split series/ parallel configurations (31). In the subsequent sections a brief explanation
of each system will be made in order to evidence their advantages and disadvantages.
Series Hybrid
A series configuration can be seen, for explanatory purposes, as an EV with an ICE to charge
the battery in order to extend its driving range. This is a solution to eliminate one of the biggest
drawbacks of EV which is their driving range (30). Figure 12 presents a representation of this
configuration, in which the energy flows can be seen as the traction power and battery charge
are represented trough arrows.
With this configuration, vehicle motion can be made exclusively using on-board batteries. When
they are “empty” or due to battery management strategies, the power for the traction motor can
be originated exclusively from the generator coupled to the ICE. It is also possible to feed the
traction motor from a combination of battery and generator.
This configuration is the one present, for example, in the Opel Ampera that will be used in this
work.
The biggest advantages of this configuration are as follows:
There is no mechanical connection between the ICE and the driven wheels; therefore
the ICE can be potentially operated at any point on its power map. This allows the ICE
15
to always be operated within its maximum efficiency region, by the design of some
components to maximize its efficiency in that region (32);
Electric motors have a torque-speed profile that is very close for ideal traction, for that
reason the drivetrain may not need multigear transmission. This can result in a much
simpler drivetrain. If two EM are used, one for each traction wheel, the mechanical
differential can be removed. This will allow the use of a similar function to the traction
control as both wheels are decoupled (32);
The control strategy in this drive train may be simple when compared to other
configurations due to the full decoupling between the ICE and the wheels (32).
Despite these advantages, this configuration also has some disadvantages as for example:
When driven recurring to the ICE, the energy changes its form twice before reaching the
EM, for that, the inefficiencies of the generator and motor may cause significant losses
(32);
The use of a generator introduces weight and cost (32).
Parallel Hybrid
For a parallel configuration, both the EM and the ICE provide torque to the wheels. The ICE can
apply a reverse torque to the EM, charging this way the battery. In Figure 13 it can be seen a
representation of this configuration, in which the energy flows can be seen as the traction power
and battery charge are represented trough arrows.
Figure 12 - Configuration of a series hybrid electric drive train (15)
16
Figure 13 - Configuration of a parallel hybrid electric drive train (25)
The main advantages of this configuration is that the ICE and the EM directly supply torque to
the driven wheels, so there is no need for energy conversion and therefore losses may be less;
another advantage is that it is a compact system because there is no generator attached and
the EM is smaller than in the Series configuration. Its major disadvantage is that the ICE cannot
be operated at its narrow higher efficiency region; also another disadvantage is the complexity
of the structure of control (25). Example of parallel hybrid vehicles are Honda’s Insight or Civic,
that use the IMA technology.
Series / Parallel Hybrid
A split series/ parallel configuration makes use of a planetary gear system for power split, that
combines an Electric Motor (EM), Electric Generator (EG) and an ICE to allow the ICE to
provide torque to the wheels and/or charge the battery through the generator. This system will
be more complex in terms of control. This configuration allows both series and parallel operation
of the ICE. It allows more flexible operation, but it is more complex in structure and costly in
price. An example of a vehicle using this configuration is the Toyota Prius (30).
2.1.2. Charge Sustaining and Charge Depleting
One of the biggest differences between HEVs and PHEVs is that in the last, the storage battery
has the capability of being charged from external sources This allows a PHEV to have a certain
driving range with energy that was provided from the grid, with all the implications in global
emissions, cost, etc. This considerable amount of energy provided by the electrical grid is
depleted while driving. Depending on the battery management of the PHEV, they can be divided
as range-extender or blended (31).
17
In a PHEV there are two driving modes, which are Charge-Depleting (CD) and Charge-
Sustaining (CS) (25). Charge-Depleting is an operating mode in which the battery state of
charge (SOC) may fluctuate, but, on average, decreases while driving. Charge-Sustaining
consists on an operation mode in which the battery SOC may fluctuate but, on average, is
maintained at a defined level while driving.
The range-extender PHEV is basically an EV in Charge Depleting mode (CD) only using the
battery as energy source and the EM as the propulsion unit until the battery State Of Charge
(SOC) reaches a certain low level from which it works in Charge Sustaining mode (CS) with a
combination of EM and ICE, as the usual behavior of a HEV.
Figure 14 shows the battery SOC evolution and RPM, from an Opel Ampera during normal use
of the vehicle. This vehicle is a range extender as can be seen by the two distinct situations CD
and CS (on in which RPMs are present).
In the blended PHEV, during CD the EM is the primary source of propulsion but the ICE is used,
if necessary, to provide extra power in higher power situations, as for example, high speed
situations or severe accelerations. This allows the EM to be less powerful and in high power
situations it uses the ICE under conditions in which it is more efficient. In the group of vehicles
that uses this strategy we will find the Toyota Prius Plug-in. Charge sustaining (CS) on these
vehicles will work as for the range extenders, working the EM and the ICE in combination.
Figure 15 shows the battery SOC evolution and RPM’s, from a Toyota Prius Plug-in during
normal use of the vehicle. This vehicle is a blended PHEV as can be seen by the RPM’s
present under CD mode.
Figure 14 - Charge Depleting and Charge Sustaining of a range-extender PHEV
18
Figure 15 - Charge Depleting of a blended PHEV
2.2. Pollutant emissions, control and legislation
This section presents information about pollutant formations, regulated emissions both for spark
ignition (SI) and compression ignition (CI) (commonly designated as Diesel engines). For ICEs,
the most common fuels used are gasoline and diesel (hydrocarbon fuels).
Considering the negative impacts of pollutants in human health, a brief explanation of the
formation and consequent impacts on health of the most important emissions from ICEs will be
presented. Nevertheless, and because the present work will be focusing on Plug-in Hybrid
Vehicles, some considerations about emissions from thermoelectric plants will be also
addressed as they are a consequence of electricity production from non-emissions free
sources.
Carbon Dioxide
Although Carbon dioxide (CO2) is not considered a toxic gas, its emissions are not irrelevant as
they contribute to one of the biggest challenges of our society nowadays, climate changes (33),
hence its emissions should be controlled and reduced. On ICEs, CO2 is the result of the
complete oxidation of the Hydrocarbon fuel. If it would be possible to allow the full oxidation of
the fuel the only emissions from an engine would be CO2 and water (H2O). This is not what
happens during combustion and even less in ICE. Not just due to incomplete combustion but
also due to additives and other substances included in the liquid fuel (34).
In recent years, vehicle emissions of CO2 per km have been reduced as a consequence of more
fuel efficient vehicles produced and sold. These more efficient vehicles can be seen as a
consequence of imposed regulations that created limits for some pollutants emissions, which
can be seen further in this chapter.
19
To avoid the continuous increase on the greenhouse gases (CO2 amongst others) in the
atmosphere, the use of new energy sources, especially renewable should have a higher
penetration. Hybridization of vehicles or using alternative fuels are steps taken towards that
goal.
Carbon Monoxide
Carbon monoxide (CO) emissions from ICE are controlled by the fuel air equivalence ratio (34).
As fuel air mixtures get leaner, CO emissions become smaller, whereas if the mixture is rich,
CO emissions will increase due to the lack of Oxygen to oxidize completely all the carbon atoms
(34) (35).
Spark Ignition engines operate under or very close to stoichiometric conditions; this means that
CO emissions are not negligible and should be controlled. Under these conditions, exhaust
gases treatment works on a very efficient range. On the other side, CI engines always operate
in lean conditions due to the excess air those engines work under. This fact makes CO
emissions on these engines to be low enough to be unimportant (34).
Carbon monoxide is a gas that is a poison for humans and for animals. This is due to the affinity
of CO with hemoglobin, about 210 times higher than oxygen, not allowing those to transport
oxygen (7). The bond created is so strong that normal body functions cannot break them (32).
This connection between CO and hemoglobin, converts hemoglobin into carboxyhemoglobin,
which provokes breathing difficulties and asphyxia and when this transformation reaches 50%
of hemoglobin converted and can cause death (7). Initially dizziness will be felt as first symptom
of CO poisoning and it leads to death rapidly (32).
Unburned Hydrocarbons
Hydrocarbon emissions in pre-mixed mixtures, are typically very low, with exception of ICEs (7)
and these emissions are a consequence of incomplete combustion of the hydrocarbon fuel (34)
(36). In ICE, unburned HC results mainly from the contact of fuel, or air-fuel mixture, with cold
surfaces which prevents oxidation of the fuel (37). This phenomenon, in which the flame
propagates until a certain distance from cold walls is called quenching (7). HC emissions can
also result, on diffusion flames (as present on CI engines), from areas where the mixture is out
of the flammability limits, meaning that the mixture is too rich or too poor not allowing its
combustion. Besides quenching, there is oil absorption of fuel during the compression stroke
and consequent release on the expansion stroke. This occurs in the thin lubricant film on the
cylinder walls (36) (34).
These unburned HC that are close to walls or in areas of the cylinder with a very reduced gap,
as is between the piston and cylinder wall, after the flame passage, HC emerge from these
gaps when the exhaust occurs and they are expelled to the outside of the engine. This problem
is particularly preponderant when the engine is cold. This happens because with cold cylinder
20
walls the quenching distance increases and on top of that, the mixture is slightly rich (during
warm up of the engine) which increases the HC emissions on SI engines.
Hydrocarbon emissions, for humans, are considered carcinogenic and also have narcotic
effects and provoke irritation on the eyes and lungs. When combined with NOx, HC originates
photochemical “smog”.
Nitrogen Oxides
The Nitrogen Oxides generically referred to as NOx, include Nitrogen Oxide (NO) and Nitrogen
Dioxide (NO2). NO is the predominant oxide of nitrogen produced inside ICE (34).Gasoline
contains negligible amounts of nitrogen (0.1%) and diesel, although it has more nitrogen then
gasoline, still does not have significant amounts of nitrogen(<1%). This leaves the atmospheric
nitrogen (N) as the main source of this gas (34) (35) (38).
NOx are formed due to high temperature and Oxygen presence. At high temperature areas, the
atmospheric N reacts with Oxygen and after leaving the engine cylinder, exhaust gases suffer a
fast temperature reduction which makes reactions involving NOx to stop and the reverse
equation does not occur (35). This causes higher concentrations of NOx when compared to the
expected concentration in equilibrium at the same temperature conditions (35).
The chemical reactions that lead to the appearance of NO close to stoichiometric conditions are
defined as the extended Zeldovich Mechanism (34) (7) (35):
Eq. 1
Eq. 2
Eq. 3
NO2 has the following reaction and reduction equations (35) (34):
Eq. 4
Eq. 5
On SI engines the NO2 / NO quotient is very low, making NO2 emission to be negligible, in CI
engines the same quotient can be around 0.1 to 0.3 and therefore not negligible (35).
Nitrogen Oxides have several effects on human health, amongst them are, reversible and
irreversible injuries on the bronchia and pulmonary alveoli, increase reactivity to natural origin
allergenic, in high quantities can provoke pulmonary edema or chronic bronchitis in lower
concentrations (7). For the environment its effect is not negligible provoking noxious effects over
vegetation (in high concentrations), on top of this it also provokes damage on polymeric material
both natural and synthetic (when atmospheric concentration is high) (7).
Sulfur Dioxide
Sulfur dioxide (SO2) emissions will occur if fuel with Sulfur content is burned. In ICEs these
emissions are reduced, as sulfur content is very small or negligible, due to efforts on reducing
21
sulfur concentrations in the fuel as the presence of sulfur in the fuel, reduces catalyst efficiency,
provoking higher emissions. For that reason, the main source of SO2 to the atmosphere is the
energy production sector as well as some industrial processes, in which fuel oil or coal are
burned, which have a higher sulfur content (7).
SO2 is a toxic gas highly soluble, and for that easily absorbed by the respiratory system (7). This
ability to be soluble provokes the generation of acid rains. Those have high impacts on soils,
plants and buildings, acidifying or damaging those. Acid rains are responsible for destroying or
degrading vast areas of arable lands, as well as corroding building facades. It can also, in high
concentrations, provoke alterations on metabolic processes of plants as for example the
reduction of growth rate (7).
Particulate matter
Particles are basically constituted by soot and ashes, and are considered one of the most
serious pollutants not just for human health but also for deterioration of materials. All
components that can be separated by a filter below 51.7 degrees centigrade are considered
particles (38). Nowadays, due to the use of unleaded fuel and catalytic converters on ICE,
particles emissions are negligible on SI engines but the same cannot be said about CI engines
(34). Particles emissions in SI engines will just be important in case of malfunction of the
engine, as when there is oil consumption or too rich mixtures (35).
In thermoelectric power plants that burn solid fuels, there is the production of ashes. The bigger
dimension particles deposit and have to be removed. The smaller particles are dragged by the
gaseous flow being emitted to the atmosphere. Every gaseous fuel can originate soot if locally
there is insufficient oxygen. On diffusion flames (Diesel engines as well) soot formation can
occur easily, especially if a sudden decrease of temperature on the exhaust does not allow the
carbon clusters to find oxygen at a high temperature.
Particles with smaller size tend to induce higher risks for human health (7). The finest particles
can transport toxics as sulfates, nitrates, heavy metals and hydrocarbons (Carcinogenic) to the
respiratory system increasing the effect of acid pollutants (7). The finest particles can penetrate
deeply into human or animal lungs and reach pulmonary alveoli, provoking respiratory
difficulties and irreversible damage in some situations (7). Particles of such size easily enter
buildings.
Actual solutions and regulations
Exhaust after treatment techniques have been introduced to control pollutant emission, which
are nowadays widely spread, such as catalytic converters (35) (39) and particle filters placed on
the exhaust line. Because the present work focuses in Plug-in Hybrid Vehicles which have an
Electric Motor (EM) and a Spark Ignition (SI) engine, catalytic converters for Diesel engines are
22
not addressed here. Further information on catalytic converters for CI engines can be found in
specialized literature (see for example (39), (34) and (35)).
Catalytic converters are placed on the exhaust flow from ICEs and are used to convert harmful
pollutants in other innocuous gases. On these catalysts, the exhaust gases are guided by a
metal casing through passageways of a ceramic monolith with a honeycomb structure. This
structure is used because it allows good mechanical and thermal strength as well as high
contact surface and low fluid dynamic losses (39).
As said before, SI engines operate with a stoichiometric mixture or close to it. This happens in
order to achieve at the same time both reduction of NO and NO2 and oxidation of CO and HC to
CO2 and H2O in a single catalyst bed (39). These catalyst are called three-way catalyst (TWC)
since they remove all 3 pollutant simultaneously (39). In Figure 16, it can be seen that the
highest conversion efficiency for the three pollutants is achieved if the mixture ratio is kept in a
narrow range (close to stoichiometric conditions).
Figure 16 - Conversion efficiency for NO, CO and HC for a three-way catalyst as a function of exhaust gas air/fuel ratio (34)
A detailed presentation of the chemical reaction present on a three-way catalytic converter can
be found in several specialized literature (see for example (39) and (40)).
Catalytic converters performance depends greatly on temperature. At high temperatures there
is the risk of active site sintering, with agglomeration of catalysts with reduction of reaction area.
On lower temperatures, the conversion efficiency is small, therefore it is important that the
converter has a low thermal inertia in order to quickly become effective (39). Engine malfunction
can be very harmful for the catalytic converter and is the main reason for its ageing. Ignition
failures and misfiring can overheat the catalyst by the excessive amount of unburned fuel on the
exhaust gases (39).
Catalytic converters have been very effective in reduction of pollutant emissions. Continuous
progress is made on those devices. Monoliths with thinner walls, to reduce thermal inertia and
enlarge number of channels per square centimeter and zeolite absorbers, which are able to
store pollutants during engine (and converter) warm-up and release them later when converter
23
temperature reached its optimal are just some examples of continuous improvements made on
these devices (39).
As a result of the noxious effects of pollutants that result from the burn of fossil fuels in vehicles,
not just for human health but also to the environment, governments started to develop
regulations in order to incentive manufacturers to reduce pollutant emissions. European
emission standards, known as EURO 1 up to EURO 6, set a limit for exhaust emissions from
vehicles sold in the European Union (EU). These standards control levels of NOx, CO, PM and
HC. EURO 1 came into action with EC Directive 91/441/EEC in 1991 and started to be applied
in 1992 for passenger vehicles. After Euro 1, subsequent updates and alterations were made,
with the introduction of EURO 2 in 1996 (Directives 94/12/EC and 96/69/EC), EURO 3 in 2000
(Directive 98/69/EC), EURO 4 in 2005 (Directives 98/69/EC and 2002/80/EC) and EURO 5 in
2009, which will be replaced by EURO 6 in September of 2014. An evolution of the limits
present on these standards from EURO 1 until EURO 6 for passenger cars is present in Table 1
for diesel and gasoline vehicles. Different limits exist for heavy duty vehicles or commercial
vehicles but due to the scope of the present work, only limits for light duty passenger vehicle are
presented.
Table 1 - EURO standards for light duty vehicles since EURO1 to EURO6
EURO
standard Date
CO HC HC+NOx NOx PM PN
g/km #/km
Compression Ignition (Diesel)
EURO 1* 1992.07 2.72
(3.16)
-- 0.97
(1.13)
-- 0.14
(0.18) --
EURO 2 (IDI) 1996.01 1.0 -- 0.7 -- 0.08 --
EURO 2 (DI) 1996.01a 1.0 -- 0.9 -- 0.10 --
EURO 3 2000.01 0.64 -- 0.56 0.50 0.05 --
EURO 4 2005.01 0.50 -- 0.30 0.25 0.025 --
EURO 5a 2009.09b 0.50 -- 0.23 0.18 0.005
f --
EURO 5b 2011.09c 0.50 -- 0.23 0.18 0.005
f 6.0x10
11
EURO 6 2014.09 0.50 -- 0.17 0.08 0.005f 6.0x10
11
Spark Ignition (Gasoline)
EURO 1* 1992.07 2.72
(3.16)
-- 0.97
(1.13)
-- -- --
EURO 2 1996.01 2.2 -- 0.5 -- -- --
EURO 3 2000.01 2.30 0.20 -- 0.15 -- --
EURO 4 2005.01 1.0 0.10 -- 0.08 -- --
EURO 5 2009.09b 1.0 0.10
d -- 0.06 0.005
e,f --
EURO 6 2014.09 1.0 0.10d -- 0.06 0.005
e,f --
* At the Euro 1..4 stages, passenger vehicles > 2,500 kg were type approved as Category N1 vehicles
† Values in brackets are conformity of production (COP) limits
a. until 1999.09.30 (after that date DI engines must meet the IDI limits)
b. 2011.01 for all models
c. 2013.01 for all models
d. and NMHC = 0.068 g/km
e. applicable only to vehicles using DI engines
f. 0.0045 g/km using the PMP measurement procedure
24
It can be seen in Table 1 that limits in the regulations have been decreasing significantly since
their introduction in 1992. As an example it can be seen that CO limits for SI vehicles in EURO
4 is only about 37 % from the limit imposed by EURO 1, or, in CI vehicles PM emissions where
limited at 0.14 g/km and on EURO 5 it was updated to 0.005 g/km, representing a decrease of
96% of these limit during those 16 years.
As referred previously, in one hand, pollutant emissions started to be controlled and limits were
introduced to homologate new vehicles (as EURO regulations), CO2 emissions, on the other
hand, that are a consequence of the amount of fuel consumed on combustion (and also its
efficiency), where not regulated as it is not toxic for human health. As stated in REGULATION
(EC) No 443/2009, for the EU (5): “The Commission adopted a Community Strategy for
reducing CO2 emissions from cars in 1995. The strategy was based on three pillars: voluntary
commitments from the car industry to cut emissions, improvements in consumer information and
the promotion of fuel-efficient cars by means of fiscal measures”. As an evolution of such
measures introduced in 1995 the European Parliament and the Council adopted Regulation
(EC) No 443/2009 for the EU, (5), in which mandatory limits of CO2 emissions are addressed for
new passenger cars with the intention of reducing CO2 emissions. These regulation sets a
target of 130g CO2/km by 2015 and as a long term target, it establishes a goal of 95g CO2/km
by 2020 (5) (41). In Figure 17 the referred targets are represented.
Figure 17 - Tailpipe emissions from new passenger vehicles and future targets (41)
As a result of these targets, manufacturers have been making efforts to make more efficient
cars that consequently consume less fuel and emit less CO2. In Figure 18 these efforts can be
seen as CO2 emissions from vehicles from several manufacturers have been consistently
dropping.
25
Figure 18 - Average CO2 emissions from 2000 until 2011 from different manufacturers (41)
As a practical example of how car manufactures have been improving the fuel efficiency of their
vehicles we can take BMW 3 series example, more specifically the 1983 BMW 323i and the
2009 BMW 325i. Table 2 shows how some parameters increased and how some others
decreased, due to the effect of technological advances introduced to improve, not just fuel
economy but also safety. Fuel consumption data presented on the previous table is based on
New European Driving Cycle (NEDC) explained in the next section.
Table 2 - Comparison between 1983 and a 2009 BMW
BMW 323i (1983) BMW 325i (2009)
Fuel consumption 10.3 l/100km 7.1 l/100km -31 %
Power output 102 kW 160 kW +57 %
Torque 205 Nm 270 Nm +32 %
Acceleration (0-100km/h) 9.2 s 6.7 s -27 %
Emission quality ECE R15-04 EU 4 +95 %
Weight 1080 kg 1505 kg +39 %
Drag 0.40 x 1.85 m2 0.27 x 2.17 m
2 -21 %
In order to control and assess the vehicles compliance with regulations, as EURO standards for
example between others, testing procedures are made with the vehicle on a chassis
dynamometer. These tests allow to measure and assess the vehicle fuel consumption as well
as emissions of several pollutants on a standard driving cycle. These emissions are collected
during vehicle runs on the chassis dynamometer and subsequently analyzed. Figure 19 shows
a schematic of the referred tests as well as measuring apparatus.
26
Figure 19 - Schematics of a chassis dynamometer test
During the tests, the vehicle has to perform a driving cycle, consisting on a speed profile that
aims to represent an average driving condition which is representative of a certain geographical
area, as for example Europe. In one hand in EU the driving cycle that is used is the New
European Driving Cycle (NEDC), following Rome and Paris driving profiles (42), on the other
hand in the United States the Federal Test Procedure (FTP) is used – resulting from real world
speed traces (42), whereas in Japan a 10-15 mode driving cycle is used. Figure 20 represents
the velocity profile of the NEDC.
Figure 20 - Velocity profile of the NEDC
The first 800 seconds from this cycle aim to represent urban driving with start and stop
situations and speed always bellow or equal to 50km/h. This part of the NEDC consists in 4
repetitions of the ECE 15 cycle. The last 400 seconds from the NEDC are an extra-urban
driving cycle (EUDC) which was designed to represent more aggressive driving, with higher
speeds. In the EUDC the speed never exceeds 120 km/h (43). As an effort to harmonize these
tests, the Worldwide harmonized Light vehicles Test Procedures (WLTP) is being developed by
experts from Japan, EU and India (44). The final version is expected to be released in 2013-
2014 (45).
In recent years solutions to reduce fuel consumption and pollutant emissions have been
researched and introduced on the market. The hybridization of vehicles is one of these
27
measures, which uses electric motors to help or make the propulsion of the vehicle especially in
situations in which ICEs are less efficient as is the case of low load and low speeds (for SI
engines). Other solutions researched are those to reduce emissions of cold start (46), or to
recover the energy present in the exhaust gases for other processes. The BMW Turbosteamer
system (47), is based on the last solution, and recovers heat from the exhaust gasses and
wasted heat by the motor and uses it for a Rankine cycle in order to produce work as in
Cogeneration plants. Studies with the same or similar intentions were made by Honda
Research & Development (48) and also by BMW Group Research and Technology (49). Honda
Research & Development study (46), investigate the possibility of emissions reduction by
means of pre-heating the engine. As seen above about catalytic converters have a lower
efficiency at lower temperatures and this warming-up phase performance should be therefore
improved.
28
29
3. Methodology
In the present work it was necessary to develop a methodology to study Plug-in Hybrid electric
vehicles. These vehicles differ from conventional vehicles due the complexity of their power-
train and the number of possible working situations.
A portable laboratory was used to collect several parameters during normal use of the two
PHEV vehicles studied. Parameters like altitude, speed, exhaust gases concentration and
battery state of charge are mandatory to fully characterize these vehicles in terms of energy and
environmental impact. A laboratory as used by Gonçalves (15), and described in section 3.1.
made possible to monitor the referred parameters. During these trips measured on-board, a
broad range of driving conditions should be addressed in order to fully characterize all the
power spectrum of the vehicle.
After gathering this data at 1 Hz, a subsequent data analysis was made using the VSP
methodology. This methodology groups points of similar power demands in modes in order to
characterize and assign to each mode the respective fuel consumption and pollutant emissions,
as described in section 3.2.
The electricity consumption of these vehicles is very important because of the capability of
relying exclusively on electricity stored on-board for long distances (more than HEV and less
than EV). For this reason and because measuring directly the energy flows on these vehicles is
dangerous due to high currents and voltages, it was necessary to develop a methodology to
indirectly measure the electricity consumption which is explained in more detail in section 3.3
and allows calculating the electricity consumption during charge depleting mode (in which
electricity consumption has a higher impact).
The VSP methodology has a particularity that allows simulating a certain trip only by knowing
the time spent on each VSP mode. This will allow the results to be validated simulating a real
trip-measured on board.Case studies are made in order to simulate the use of these vehicles by
“typical” drivers. This is done with the intention of showing the applicability of these results for
any type of driver.
3.1. Measuring apparatus and procedures
For the present work, the previously developed VE-LAB portable laboratory for vehicle on-board
monitoring was used following similar procedures to (15). The measurements were performed
during normal use of the vehicles on Portuguese roads and real time data was collected.
30
Figure 21 - Main components of VE-LAB and typical installation places on light duty vehicles
Several parameters were controlled, and for that, the following devices were used:
Gas analyzer
OBD reader
GPS receiver
Laptop running LabVIEW
Gas analyzer
To collect tailpipe emissions data, a Vetronix PXA-1100 gas analyzer was used. This device
allows measuring continuously CO2, O2, CO, HC and NOx during on-road tests. Communication
ports from this device allow it to send information to a laptop in order to monitor and store the
data for further analysis. The analyzer specifications are presented in Table 3.
The calibration of the device was performed using calibration gas (with concentrations between
the device limits) prior to the measurements. During road tests, zeroing was performed as
requested by the analyzer. The device and its necessary peripherals, added an extra weight to
the vehicle of around 6 kg.
This device measures tailpipe NOx concentrations and does not differentiate between NO and
NO2 (15), therefore all the results presented for mass emissions of NO or NOx are equivalent
and were calculated considering the molar mass of NO (30 g/mol) (15). To obtain the
corresponding NO2 emissions, one simply needs to multiply the obtained result by a factor of
46/30.
HC emissions are reported by the analyzer as hexane equivalent (C6H14) with the corresponding
molar mass of 86 g/mol (15). Data was acquired at 1Hz.
• Conventional technologies (ICE):
• Equipments used:
• OBD reader
• Gas analyzer
• GPS with barometric altimeter
OBD2 reader
GPS receiver with barometric altimeter
Gas analyzerLaptop running LabView
31
Table 3 - Measuring range of different gases from Vetronix PX-A-1100 gas analyzer
Gas Range Resolution Accuracy
CO2 0-20% 0.1% 0.5% greater than absolute or 5% of reading
O2 0-25% 0.01% 0.01% greater than absolute or 5% of reading
CO 0-10% 0.01% 0.06% greater than absolute or 5% of reading
HC 0-20000 ppm 1 ppm 6ppm greater than absolute or 5% of reading
NOx 0-4000 ppm 1 ppm 32 ppm between o-1000 ppm
60 ppm between 1001-2000 ppm
120 ppm between 2001-4000 ppm
OBD reader
OBD interfaces are standard connections that started to be mandatory for vehicles registered in
the EU from January 2001 for positive ignition vehicles and from January 2004 for vehicles
equipped with compressions ignition engines (50). Connecting to these ports and with adequate
devices and/or software, several parameters can be monitored in real time. Those parameters
are Speed, RPM, Intake manifold pressure, engine load, cooling temperature, battery SOC, just
to name a few. For the present work, an OBDKey OBD reader was used. This reader works with
all OBD-II compliant vehicles and supports OBD-II standards ISO9141, KWP2000, SAE J1850
VPEM and PWM, CAN Bus amongst others (51). The OBD scanner used sends information to
the laptop by a Bluetooth connection. Specifications of the OBD reader used can be found in
Table 4.
Table 4 - OBDKey OBD scanner specifications
Power Source 12 V DC from OBD socket
Communications Bluetooth (Serial port, 9600 bps to 19200 bps, depending on protocol)
Acquisition frequency ~7 request per second
Signal delay Negligible
GPS receiver
A GPS receiver was used due to its built-in barometric altimeter, and due to the possibility of
connecting it to a laptop. On top of that, it allowed to double check speed information received
through the OBD connection. The use of a barometric altimeter allows obtaining, in a more
accurate way, the altitude when compared to the same value from GPS data. This data is
important in order to determine the road grade during the tests to calculate the Vehicle Specific
Power (VSP), as seen in section 3.2. Because the barometric altimeter measure differences of
pressure at different altitudes, some care have to be taken, especially at high speeds, therefore,
32
throughout the tests, the windows were always closed to avoid severe pressure fluctuations
inside the vehicle due to air flow.
Laptop
A laptop use is mandatory not only for the after measurement data analysis, but also to store
the results of on-road measurements that are gathered at 1 Hz using a software purposely
designed. For the data analysis, a MatLab program was developed calculate several
parameters during on-road measurements as road grade and VSP amongst others. This
program reads all the measurement files from one vehicle, combines them and makes all the
necessary calculations. The use of this program allowed simulating several situations in order to
better understand vehicle’s behavior. Information about this program is present on the
attachments of this work.
Vehicles used
For this work, two vehicles were used and monitored, an Opel Ampera (Chevrolet Volt in some
markets), and a Toyota Prius Plug-In. These two vehicles were studied due to their differences
on the powertrain configuration and because they represent the majority of available PHEV.
Both these vehicles are Plug-In Hybrid vehicles (PHEV) meaning that both of them have an ICE
as well as an EM (see section 2.1.1). The fact that both are PHEV means that both can be
charged from the electric grid (from, for example, a home electric installation) allowing the
vehicles to increase its full electric range or the use of the EM instead of the ICE. Table 5
presents some specifications available from Opel and Toyota about these vehicles, (52) (53).
As seen in section 2.1.2, The Opel Ampera is a range extender PHEV whereas the Toyota
Prius Plug-in is a blended PHEV.
Figure 22 - Vehicles measured. On the left is the Opel Ampera and on the right the Toyota Prius Plug-in
33
Table 5- Vehicles Specifications (52) (53)
Opel Ampera Toyota Prius Plug-In
Electric Propulsion System
Power (kW) 111 60
Maximum torque (Nm) 370 207
Battery System
Type Lithium-ion battery Lithium-ion battery
Capacity (kWh) 16 4,5
Charging time (h) 4-6 (230V,15A - 10A) ~1.5 (240V)
Internal combustion engine
Displacement (cm3) 1398 1798
Power (kW@rpm) 63 @ 4800 73 @ 5200
Maximum torque (Nm@rpm) 130 @ 4250 142 @ 4000
Engine type 4L 16 valves 4L 16 valves
Emissions and fuel
Emissions standards Euro 5 Euro 5
Fuel gasoline gasoline
Weight (kg) 2000 1840
Roads and routes
The selected routes were chosen in order to allow the most possible driving conditions. All the
trips were performed in Lisbon metropolitan area, comprehending:
Urban driving
Highway driving
The selected routes were repeated several times with different driving characteristics in order to
have the most possible number of driving situations and measured points. The car was always
driven by the same driver (as suggested by Frey et al. (54)), in different days and consequently
different traffic conditions. Inside the car there were always the same two persons, as well as all
the measuring equipment that did not change throughout all this work, resulting in around 170
kg of load (accounting for the 2 passengers and equipment). Constant speed situations were
also performed in order to allow, the calculation of battery consumption per VSP level. Different
speeds were used in order to characterize the full VSP spectrum. This methodology is
explained in more detail in section 3.2.
34
In Table 6 and Table 7 it is present the characteristics of the trips performed with each vehicle
during the present work.
Table 6 - Distances and characteristics of monitored trips (Toyota Prius Plug-in)
Trip 1 2 3 4 5 6 7 8 9 10 11 12
Distance (km)
21.6 22.1 24.8 23.4 27.3 4.13 9.99 7.39 6.48 23.8 25.3 3.98
Time (s) 2022 1134 1732 1472 2159 1150 1331 954 1033 1420 2774 1159
Average speed (km/h)
38.5 70.2 51.5 57.2 45.5 12.9 27.0 27.9 22.6 60.3 32.8 12.4
Type U + H
H U + H
U + H
U + H
U U U U U + H
U U
Mode CS CS CS CD CD + CS
CD CD CD + CS
CS CS CS CS
U: Urban trip; H: Highway trip; CD: Charge Depleting; CS: Charge Sustaining
Table 7- Distances and characteristics of monitored trips (Opel Ampera)
Trip 1 2 3 4 5 6 7 8 9 10 11 12
Distance (km)
20.3 22.6 25.0 24.1 38.9 24.5 10.5 10.8 47.3 21.8 42.4 28.2
Time (s) 1859 1180 1579 1420 1646 1393 1278 884 2490 1812 1849 3031
Average speed (km/h)
39.3 68.9 57.0 61.1 85.1 63.3 29.6 44.0 68.4 43.3 82.6 33.5
Type U + H
U + H
U + H
U + H
H U + H
U U U + H
U + H
U + H
U
Mode CD CD CD + CS
CD + CS
CS CS CD CD CD + CS
CD CD + CS
CS
U: Urban trip; H: Highway trip; CD: Charge Depleting; CS: Charge Sustaining
The Toyota Prius Plug-in was driven for more than 200 km through more than 5 hours of on-
board measurements. The Opel Ampera, was driven for more than 316 km in a total of about 5
hours and 40 minutes of monitoring. Several combinations of speeds were performed as well as
different aggressiveness during driving in order to explore the power spectrum of both vehicles.
All gathered data was analyzed to assess its quality and some of it was not used due to lack of
information on battery SOC, for example.
The exact routes are not presented because one advantage of using the Vehicle Specific Power
methodology is that speed profile information is not necessary as it just depends on power. For
this reason it was necessary to explore all the power spectrum of these vehicles in order to fully
characterize their behaviors in terms of energy consumption and pollutant emissions.
3.2. Data analysis – Vehicle Specific Power Methodology
During on-road measurements it is hard to establish a correlation between what is happening in
terms of power demands and consequent energy consumption and emissions.
35
In order to characterize the power demand for the vehicle motion, a useful definition is the
Vehicle Specific Power (VSP) methodology which is a simplification of all the forces present on
the vehicle. This methodology is a road-load model which allows to estimate instantaneous
tractive power per unit vehicle mass, making it very useful to use VSP to perform energy and
environmental characterization of vehicles (19), (15).
With this methodology the power per unit of vehicle mass comes from a correlation of vehicle
dynamics as speed, acceleration, rolling and aerodynamic resistance with road grade.
Simplifications were performed for rolling resistance, aerodynamic resistance and for the effect
of manual transmission gear used, based on data ranging from compact to luxury-size vehicles.
The result of such simplifications is present in equation 6 which represents the power demand
at every second of driving during a trip of the vehicle and valid for light duty vehicles (19).
( ) Eq. 6
Vehicle Specific Power units are square meter per cubic second (m2/s
3) which is equivalent to
Watt per kilogram (W/kg). The variables that should be monitored under real-world operation in
a 1 Hz basis are, vehicle speed, , in m/s, acceleration, , in m/s2 and road grade (vertical
rise/slope length3 (m/m)), . The constant 9.81 represents the acceleration of gravity
(m/s2), 0.132 is the rolling resistance term and 0.000302 is present in order to take into account
the aerodynamic drag. The VSP value is computed at every second of data acquired during on
road measurements being subsequently grouped in modes that provide a homogeneous
distribution across the most common driving modes. The VSP range of each mode for light-duty
vehicles is present in Table 8.
Table 8 - Range of each VSP mode (18)
VSP Mode VSP Range (W/kg) VSP Mode VSP Range (W/kg)
1 VSP < -2 8 13 ≤ VSP < 16
2 -2 ≤ VSP < 0 9 16 ≤ VSP < 19
3 0 ≤ VSP < 1 10 19 ≤ VSP < 23
4 1 ≤ VSP < 4 11 23 ≤ VSP < 28
5 4 ≤ VSP < 7 12 28 ≤ VSP < 33
6 7 ≤ VSP < 10 13 33 ≤ VSP < 39
7 10 ≤ VSP < 13 14 39 VSP
3.3. Methodology for electricity consumption prediction
This section presents a methodology that has been developed to predict, based on vehicle
dynamics (as VSP value), battery SOC along a given trip during CD stage to estimate electrical
3 Rigorously cos(atan(grade)) should be used instead of grade, but the error of this approximation is small
(less than 1% relative error for grades below 11%) (19).
36
consumption of PHEV without direct measurements of electric components. The methodology
proposed can also be extrapolated to Electric Vehicles.
In order to develop this methodology, some of the parameters measured during normal driving
of the test vehicles include: battery SOC, speed and altitude. Speed and SOC were obtained
from the vehicle standard OBD connection, whereas altitude was measured using a GPS
receiver with a built in barometric altimeter.
During normal driving of the vehicle, constant speed and slope situations were measured at
different speeds, namely, 40, 50, 80, 100 and 120 km/h as well as constant speed in downhill
conditions to measure power regeneration conditions. This allowed the measurement of battery
consumption/regeneration at approximately constant power demands (VSP).
By measuring the battery SOC variation at any time interval of approximately constant VSP as
set by the above conditions, the battery consumption is characterized. Dividing this battery SOC
change by the time interval in which it happened, results in battery consumption per second
(%ΔSOC/s), for a mean VSP. This should be repeated for several speeds in order to
characterize the battery consumption in a larger VSP spectrum. Combining the constant speed
points (and consequently, constant VSP due to constant slope) and the battery consumption per
second at those specific points, a relation can be obtained. This can be seen in Figure 23 where
%∆SOC/s is plotted as a function of the value of VSP.
Figure 23 - Variation of SOC with VSP value (Toyota Prius Plug-in)
Figure 23 shows the battery consumption (positive VSP values) and some points obtained in
regeneration situations (negative VSP values). Battery consumption as a function of VSP
follows a linear tendency, increasing with VSP, as expected. The presented data was obtained
from the Toyota Prius Plug-in, but the same methodology was also applied to the Opel Ampera,
measured during this work.
y = -0.01093x - 0.0025
-0,3
-0,25
-0,2
-0,15
-0,1
-0,05
0
0,05
0,1
0,15
-10 -5 0 5 10 15 20 25
%∆SO
C/s
VSP value
y = -0.00069x2 – 0.013862x
37
After mapping this data, a linear regression was made to adapt to the data, establishing a
correlation between VSP value and %∆SOC/s. Previous works (17), about the Toyota Prius
(HEV), shows that during regeneration the variation on SOC with VSP is not linear and a
quadratic interpolation was used. Some points of positive VSP are included in the interpolation
to establish a tendency. SOC variation can then be calculated based solely on VSP, with a
linear variation for positive VSP modes and quadratic for negative VSP modes.
As seen in section 3.2, VSP is dependent on vehicle speed, acceleration and road grade.
Having these variables monitored during a driving profile, it is possible to calculate the battery
consumption at every second. Knowing the value of SOC at the initial second, subsequent SOC
values can be estimated by this method if the VSP distribution during time for the route is
known.
Figure 24 shows an example of this methodology applied to a trip measured in a Toyota Prius
Plug-in. During this trip the battery SOC was predicted over time using the calculated VSP value
at each second, after which the battery consumption and energy regeneration could be
estimated.
Figure 24 – Comparison between measured and predicted battery SOC (Toyota Prius Plug-in)
The coefficient of determination (R2) for the approximation between the estimated and
experimental points is 0.962. Figure 25 represents the estimated points, using the methodology,
as well as the measured points.
0 500 1000 1500 2000 2500 3000 35000
10
20
30
40
50
60
70
80
Time (s)
% S
OC
38
Figure 25 - Estimated battery SOC points compared to the measured values (Toyota Prius Plug-in)
This approximation reflects the difficulty of getting exact values, due to variations in the
assumed constant conditions during on-road measurements. Even though constant speed
situations tried to be achieved, small fluctuations may have happened. It should also be referred
that all these steps were approximations, therefore some deviation is expected.
With these considerations in mind, some manual adjustment was done (by refining the
interpolation) in order to obtain a better approximation between estimated and measured battery
SOC along time. With this approach a R2 of 0.998 between the approximation and the
measured battery SOC was achieved. This approximation is shown in Figure 26, with a much
higher level of superposition of the approximation with the measured data of battery SOC
acquired from OBD.
Figure 26 – Comparison between measured and predicted battery SOC (Toyota Prius Plug-in)
As said before, the represented simulation was made with data gathered from a Toyota Prius
Plug-in. That is why sometimes the ICE is used even with a SOC value higher than the
predefined CS battery SOC. These situations correspond to high power situations or above 100
y = 0.9293x + 6.2109 R² = 0.9964
35
40
45
50
55
60
65
70
75
35 40 45 50 55 60 65 70 75
Me
asu
red
bat
tery
SO
C (
%)
Estimated battery SOC (%)
0 500 1000 1500 2000 2500 3000 35000
10
20
30
40
50
60
70
80
Time (s)
% S
OC
39
km/h. Figure 27 shows that simulated values and effective battery SOC (from OBD) have a high
level of agreement.
Figure 27 - Estimated battery SOC points compared to the measured values (Toyota Prius Plug-in)
This result allows calculating the electrical consumption of the vehicle in each VSP mode as
made usually for conventional vehicles with fuel consumption. On top of that it allows computing
pollutant emissions under CD depending on the energy mix from the country where the vehicle
is charged.
The last step for this methodology is to assess the correspondence between percentage of
SOC variation and kWh. This is achieved by knowing battery SOC before and after charging the
vehicle and by measuring the energy consumption during charging.
Equation 8 is used to compute the correspondence of 100% of battery SOC by the following
expression:
Eq. 8
Where, , is the energy transferred from a power plug to the vehicle battery, in kWh and is the
.
This allows verifying the usable full capacity of the battery. Battery SOC read from OBD is a
percentage of the full battery capacity. This value should be computed because manufacturers
do not usually release this information and also if the battery is degraded this allows assessing
what represents 100% of charge.
y = 1.0309x - 1.8608 R² = 0.9974
35
40
45
50
55
60
65
70
75
35 40 45 50 55 60 65 70 75
Me
asu
red
bat
tery
SO
C (
%)
Estimated battery SOC (%)
40
It was verified that the value for the state of charge reported to the driver is not the same as
read by OBD. This probably happens because the battery is used between two limits (for
example: 24%-84%) in order to increase its lifetime. In Figure 28 it can be seen the difference
between what is reported to the driver and the actual battery SOC.
For the Opel Ampera, the manufacturer announces a battery capacity of 16 kWh and for the
Toyota Prius Plug-in of 4.5 kWh. By this method and using equation 8 it was verified that for the
Opel Ampera the full battery capacity was around 22.5kWh and for the Toyota Prius Plug-in 7.4
kWh. It should be referred that the usable range (range of SOC in which each vehicle works as
seen in Figure 28 of both batteries are 13.5 kWh and 3.4 kWh for the Opel and for the Toyota
respectively.
Figure 28 – Comparison between reported battery state of charge to the driver and measured SOC by OBD, left: Toyota Prius Plug in, right: Opel Ampera
0
20
40
60
80
100
0 20 40 60 80 100B
atte
ry S
OC
re
po
rte
d b
y O
BD
Battery SOC reported to the driver
0
20
40
60
80
100
0 20 40 60 80 100
Bat
tery
SO
C r
ep
ort
ed
by
OB
D
Battery SOC reported to the driver
41
4. Results This chapter includes the validation of the proposed methodology to predict electricity
consumption, the presentation of the results for fuel consumption and pollutant emissions both
under CD and CS mode and electricity consumption for both vehicles used. In the end of the
chapter considerations about global CO2 emissions due to electricity consumption of these
vehicles will be presented.
4.1. Electricity consumption methodology
There are difficulties associated with measuring electrical components while performing on-road
measurements, namely:
In this particular work the vehicles were lent by the manufacturers and cannot be
altered in any form;
It is necessary to have access to a workshop and service manuals to acquire battery
current and voltage signals using manufacturers connectors;
High voltages and currents of the electric power system imply risks for the operator.
Two different processes were used to validate this methodology, comparing the prediction by
the methodology with current flows measured on a HEV and comparing between predictions
and measured battery SOC during on-road measurements for a different vehicle.
For the first process of validation it was necessary to use data from previous measurements on
a conventional Toyota Prius (HEV) (17) when battery current flows were measured to and from
the battery pack of that HEV vehicle. This data was acquired at 1 Hz following similar
procedures to the ones presented on this work, but also on (15).
For the second validation process, data from on-road measurements preformed during this work
was used to compare those with results from this methodology.
4.1.1. Validation with a Toyota Prius (HEV)
Table 9 shows a comparison between predicted parameters and measured equivalent
parameters. For the prediction of ΔSOC (%) two regressions are used, a linear regression for
positive VSP and a quadratic for negative VSP adapted for the vehicle studied, using the raw
data from previous works (17).
From Table 9 it can be seen that there is a bigger error for VSP equal to approximately -7, all
the other values are predicted with accuracy better than 9%. This is a good result considering
that we are dealing with approximate values, during on-road measurements and in changing
conditions. Due to the fact that this vehicle is a HEV and was not tested following the
procedures of this methodology, it created some problems on the data acquisition for this
validation. The fact that the conventional Toyota Prius has a smaller capacity battery, reduces
42
the possibility of operating the EM on a full electric mode, which makes it more difficult to get
on-road data points where only the EM is operated with a constant VSP.
Table 9 - Comparison between predicted and measured values for electricity consumption
Predicted Measured
Mean VSP ΔSOC
(%) Wh/s W Current (A) W error
-6.90 0.275 3.58 12881 47.9 9658 33.4%
-3.88 0.248 3.22 11584 57.1 11504 0.7%
-3.53 0.237 3.08 11102 54.6 11010 0.8%
2.08 -0.156 -2.03 -7290 -36.5 -7355 -0.9%
2.27 -0.164 -2.13 -7681 -39.8 -8014 -4.2%
3.99 -0.243 -3.16 -11365 -52.2 -10516 8.1%
The following section 4.1.2 presents the application to a PHEV, which is the scope of this work,
however was important to first verify its applicability.
4.1.2. Comparison between methodology and measured data
As said above, the same methodology was applied to an Opel Ampera which has a different
power train configuration; in this case, the Opel Ampera is considered a range-extender vehicle.
In the Opel case, the ICE is never turned ON until a certain battery SOC is achieved (CS level).
The ICE is always working as an electricity generator. Therefore it does not have a connection
with the wheels, in opposition with the Toyota Prius Plug-in. This means that the vehicle
behaves as an EV until a certain battery SOC is achieved. This is important because if the
presented methodology works for this vehicle, it gives a very positive indication about being
suitable also for EV vehicles.
With the procedures presented in section 3.3, some constant VSP points were selected and
plotted against %ΔSOC/s as shown in Figure 29.
Figure 29 - Variation of SOC with VSP value (Opel Ampera)
By applying this methodology, the resulting approximation has a R2 of 0.979. This was achieved
only using a linear regression both for positive and negative VSP values. This approach results
y = -0.00355x
-0,15
-0,1
-0,05
0
0,05
0,1
-20 -10 0 10 20 30 40
%∆SO
C/s
VSP value
y = -0.0000525x2 – 0.00364891x + 0.0088 0,013862x
43
in some error on the prediction, especially for negative VSP values (regenerative part). The
deviation between the measured data and the estimated values can be seen in Figure 30.
Although the regression in Figure 30 provides an approximation, some values could be more
closely estimated.
Figure 30 - Estimated battery SOC points compared to the measured values (Opel Ampera)
A quadratic regression for negative VSP was introduced and this enabled to achieve a
coefficient of determination equal to 0.998 between predicted values and measured values. The
result for this approximation is present in Figure 31.
Figure 31 - Estimated battery SOC points compared to the measured values (Opel Ampera)
In order to show a real case in which battery SOC is predicted during a trip Figure 32 is shown,
for the Opel Ampera.
y = 1.0183x - 0.4556 R² = 0.998
20
30
40
50
60
70
80
90
20 30 40 50 60 70 80 90
Me
asu
red
bat
tery
SO
C (
%)
Estimated battery SOC (%)
y = 1.0342x - 2.0975 R² = 0.999
20
30
40
50
60
70
80
90
20 30 40 50 60 70 80 90
Me
asu
red
bat
tery
SO
C (
%)
Estimated battery SOC (%)
44
Figure 32 - Measured and predicted battery SOC (Opel Ampera)
Figure 32 shows that during the trip, the predicted battery SOC (only by knowing instantaneous
VSP) follows well the measured values. These two validation processes show that the
methodology adapts to real cases, and therefore makes it possible to know the battery
consumption at each second of driving without having to measure any electrical current (which
can be very dangerous due to high voltages), only by knowing the VSP value.
4.2. Fuel consumption and pollutant emissions
During road tests, data was collected at 1Hz in order to be analyzed afterwards. This data was
subsequently treated in a MatLab program developed during this work. This program facilitated
calculations and provided an automatic and fast way of analyzing road tests data for future
works, not just for PHEV, but also for CV, HEV and EV. This program automatically reads all the
measurements files, makes all the necessary calculations, such as road grade, emissions in g/s,
fuel consumption in g/s, VSP per second, makes a modal analysis to group points of similar
power demand, amongst others. The use of this program enabled the testing of different
situations in order to better understand the vehicles in a fast way.
The results of the two vehicles will be presented separately, starting by the Toyota Prius Plug-in
and followed by the Opel Ampera. These results will be subdivided for each vehicle as: CS, CD
and CD high power for the Toyota Prius Plug-in and CS, CS high power, CD and Mountain
Mode for the Opel Ampera.
4.2.2. Toyota Prius Plug-in
4.2.2.1. Charge Sustaining
Charge Sustaining, as seen on section 2.1.2, is the mode in which a Plug-in Hybrid vehicle
enters when battery SOC reaches a predefined low level. For the Toyota Prius Plug-in, using
on-road data, this value was observed to be around 30% of battery SOC. With the help of the
45
MatLab program, it is possible to group all the data for which the battery SOC is lower than
30%. This data is also grouped in modes depending on the power demanded at that instant,
following the VSP methodology as explained in section 3.2. After this separation, the results
achieved can be plotted, generating the graphs that will be presented under this chapter. All the
following figures that include error bars are for a confidence interval of 95%. First of all, time
distribution of on-road tests per VSP Mode can be seen in Figure 33.
Figure 33 - Time distribution per VSP mode in CS (Toyota Prius Plug-in)
Modes 12, 13 and 14 have fewer points 89, 78 and 75 seconds respectively, all bellow one and
a half minute of gathered data on each of these modes. Since on-road tests are performed in
public roads, it is hard to obtain a great amount of data under high power demands. On top of
this, and as an indication, it was verified that during CS the ICE was sometimes off, resulting in
the vehicle being moved only by the EM. The amount of time this occurred is presented in
Figure 34.
Figure 34 - Percentage of time with ICE OFF in CS (Toyota Prius Plug-in)
The results found on Figure 34 are very similar to the conventional Toyota Prius (HEV) (17);
leading to the conclusion that under CS conditions the Toyota Prius Plug-in behaves like a
Conventional Toyota Prius but with more weight due to the bigger battery. Figure 35 presents
0
500
1000
1500
2000
2500
3000
3500
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Nu
mb
er o
f p
oin
ts
VSP Mode
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Tim
e w
ith
ICE
OFF
(%
)
VSP Mode
46
the fuel consumption of the vehicle in CS as a function of VSP mode. As expected the fuel
consumption increases with the increase in power demand.
Figure 35 - Fuel consumption per VSP mode in CS (Toyota Prius Plug-in)
This vehicle shuts OFF the engine in deceleration and when stopped. The graph shows that the
value of consumption, for the first three modes (decelerations and stopping), is very low, almost
unnoticeable on the graph when compared to others. Under CS, the vehicle was treated as a
black-box, with only one power source like an HEV, hence accounting both ICE ON and OFF
operation in the fuel use and pollutant emission.
On section (4.2.2.2) a comparison between these results for CS will be made with the results for
CD consumption to clarify the behavior of the vehicle in both modes.
As expected and as can be seen in Figure 36, CO2 emissions follow a very similar tendency to
the fuel consumption due to the fact that these are a result of fuel combustion.
Figure 36 - CO2 emissions per VSP mode in CS (Toyota Prius Plug-in)
The CO emission distribution as shown in Figure 37 has a maximum in mode nine, where it is
above 0,015g/s.
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Fuel
(g/
s)
VSP Mode
0
2
4
6
8
10
12
14
1 2 3 4 5 6 7 8 9 10 11 12 13 14
CO
2 (
g/s)
VSP Mode
47
Figure 37 - CO emissions per VSP mode in CS (Toyota Prius Plug-in)
HC emissions are presented in Figure 38, and show very low emissions of this gas, except in
mode twelve where a peak is verified.
Figure 38 - HC emissions per VSP mode in CS (Toyota Prius Plug-in)
The behavior of NO emissions, shown in Figure 39, shows a rise on the highest VSP mode,
which is the one with higher RPM’s and higher load (higher temperature), leading to the
consequent higher NO formation due to reduced amount of time to allow recombination.
0
0,005
0,01
0,015
0,02
0,025
0,03
1 2 3 4 5 6 7 8 9 10 11 12 13 14
CO
(g/
s)
VSP Mode
0
0,0005
0,001
0,0015
0,002
0,0025
1 2 3 4 5 6 7 8 9 10 11 12 13 14
HC
(g/
s)
VSP Mode
48
Figure 39 - NOx emissions per VSP mode in CS (Toyota Prius Plug-in)
4.2.2.2. Charge Depleting
For the Toyota Prius Plug-in, fuel consumption and emissions tendency will be shown
depending on battery SOC, because the vehicle uses the ICE even on CD mode. Afterwards a
weighted average will be presented for the entire CD mode independent of the battery SOC.
Having the results dependent on the battery SOC is interesting in order to be able to compute or
simulate in more detail the fuel consumption and emissions in the CD mode, which can be
useful for example for a simulation tool like ADVISOR (14). For the scope of the present work
the weighted average will be more useful, because it fits better in the VSP concept, used for
instance in MOVES model (13).
During on road-measurements 6031 seconds, or about 1 hour and 40 minutes, of usable data
on CD for this vehicle was collected. The time distribution per VSP Mode is present in Figure
40.
Figure 40 - Time distribution per VSP mode in CD (Toyota Prius Plug-in)
The modes with less data are 12, 13 and 14 just have 56, 35 and 45 seconds of data
respectively.
0
0,0001
0,0002
0,0003
0,0004
0,0005
0,0006
0,0007
1 2 3 4 5 6 7 8 9 10 11 12 13 14
NO
(g/
s)
VSP Mode
0
200
400
600
800
1000
1200
1400
1600
1800
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Nu
mb
er o
f p
oin
ts
VSP Mode
49
Figure 41 shows the fuel consumption per VSP mode for several battery SOCs, considering
both ON and OFF engine operation. The 20% to 30% columns represent the CS mode whereas
all the others are from the CD mode but with different states of charge at those instants. Points
below 20% of SOC are not considered, because these were never observed during the tests.
Charge Sustaining is represented here as a comparison, to verify the decrease of consumption
due to a higher battery SOC. A full charge of the Toyota Prius Plug-in was read as 73% of SOC
from OBD. The following graphs just consider a battery SOC till 70% as the remaining 3% are
expend on the first seconds of driving and are therefore not representative.
Figure 41 - Fuel consumption per VSP mode in CD depending on battery SOC (Toyota Prius Plug-in)
It was verified that with the increase of battery SOC the fuel consumption on each mode
decreases. This is more evident on middle range VSP modes, where ICE OFF operation is
more dominant within higher SOC levels. On high VSP modes, the fuel consumption difference
is not as evident; because the ICE is solicited for those modes. Especially on the highest
modes, the values for the consumption are practically coincident, which is coherent with the
electric power available and vehicle mass (~32 W/kg – VSP mode 12).
As expected, CO2 emissions follow the exact same tendency as the fuel consumption. This was
expected because CO2 emissions depend only on fuel consumption and on the quality of the
combustion. Figure 42 represents the tendency for the CO2 emissions per VSP mode.
0
0,5
1
1,5
2
2,5
3
3,5
4
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Fuel
(g/
s)
VSP Mode
20%-30% 30%-40% 40%-50% 50%-60% 60%-70%
50
Figure 42 - CO2 emissions per VSP mode in CD depending on battery SOC (Toyota Prius Plug-in)
For the CO emissions case, they are very low practically at all range. This can be seen in Figure
43.
Figure 43 - CO emissions per VSP mode in CD depending on battery SOC (Toyota Prius Plug-in)
On VSP mode 12 and with a battery SOC from 50% to 60% and from 60% to 70% two peaks
are verified. The reason for these two peaks is the fact that, for high VSP modes on CD mode,
the Toyota Prius Plug-in turns the ICE on. Due to the vehicle power/weight ratio, the triggering
of ICE ON conditions under CD mode typically occurs at VSP mode 12. Because ICE
solicitation coincides with high power situations, it operates under high loads and high RPM’s
mostly under cold engine conditions. As a consequence, as seen in chapter 1, the CO
conversion in the catalytic converter is less efficient due to the lower working temperature.
For HC emissions, which are represented in Figure 44 a similar result occurs.
0
2
4
6
8
10
12
1 2 3 4 5 6 7 8 9 10 11 12 13 14
CO
2 (
g/s)
VSP Mode
20%-30% 30%-40% 40%-50% 50%-60% 60%-70%
0
0,05
0,1
0,15
0,2
0,25
0,3
1 2 3 4 5 6 7 8 9 10 11 12 13 14
CO
(g/
s)
VSP Mode
20%-30% 30%-40% 40%-50% 50%-60% 60%-70%
51
Figure 44 - HC emissions per VSP mode in CD depending on battery SOC (Toyota Prius Plug-in)
The reason for this situation is the same as for CO emissions. The points were measured under
high loads and with a cold engine, it is expected that emissions on those transient conditions
are very high in comparison with stationary and warm conditions.
Figure 45 shows the results for NO emissions depending on VSP mode and on battery SOC.
Figure 45 - NO emissions per VSP mode in CD depending on battery SOC (Toyota Prius Plug-in)
These emissions are also very low independently from the VSP mode with exception of mode
12. This has probably the same explanation as for CO and HC emissions, as explained before.
On mode 14, the behavior is normal; this has to do with NOx formation mechanisms, explained
in chapter 1. This behavior is in line with other studies (15) and is in line with the behavior
verified on CS.
The presented results so far, are very important to understand the vehicle behavior on CD
depending on its battery SOC. Despite this, for the scope of this work, it would be very
convenient to have an average consumption and emissions during CD mode, independently of
0
0,005
0,01
0,015
0,02
0,025
1 2 3 4 5 6 7 8 9 10 11 12 13 14
HC
(g/
s)
VSP Mode
20%-30% 30%-40% 40%-50% 50%-60% 60%-70%
0
0,0002
0,0004
0,0006
0,0008
0,001
0,0012
0,0014
0,0016
1 2 3 4 5 6 7 8 9 10 11 12 13 14
NO
(g/
s)
VSP Mode
20%-30% 30%-40% 40%-50% 50%-60% 60%-70%
52
SOC level to better adapt to VSP methodology for estimate fuel and pollutant emissions. This
will allow to, in an easier way, simulate emissions and consumption for any certain trip, knowing
VSP distribution.
The referred average of the consumption as well as of the emissions is present in Figures 44,
47, 48, 49 and 50. These figures also represent the values for CS in order to establish an easy
visual comparison between the two driving modes.
Fuel consumption can be seen and compared in Figure 46.
Figure 46 - Fuel consumption per VSP mode in CS and CD (Toyota Prius Plug-in)
As expected, on higher modes the fuel consumption rates are almost coincident in CD and CS,
especially from mode 10 to 14. On the middle range modes (4 to 10 for example), which are the
ones used more often, the graph shows the effect of battery operation and full electric mode
(ICE OFF) on the average fuel consumption reduction. This presented fuel consumption in CD
is weighted taking into account the time distribution with ICE OFF. This time distribution is
present in Figure 47.
Figure 47 - Percentage of time with ICE OFF in CD (Toyota Prius Plug-in) for an aggressive driving
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Fuel
(g/
s)
VSP Mode
CS CD
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14
% t
ime
wit
h IC
E O
FF
VSP Mode
53
This time distribution was obtained during on-road measurements with rather aggressive
driving. The behavior found for the percentage of time with ICE OFF can be explained by the
fact that the battery capacity of this vehicle is not very high. Therefore the vehicle powertrain
management will prefer saving battery in higher power solicitations (higher VSP modes (9 to
14)), to use it in lower power solicitations (lower VSP modes (1 to 8)).
If this vehicle is driven with a more “soft”, less aggressive behavior, this distribution will be
considerably different. For an urban situation without aggressive driving, also measured on-
board, this same distribution was computed and adjusted for the fuel consumption measured.
That time distribution of engine OFF for the same vehicle but with a “soft” driving behavior is
present in Figure 48.
Figure 48 - Percentage of time with ICE OFF in CD (Toyota Prius Plug-in) for a non-aggressive driving
As Figure 48 shows, when the driver behavior is less aggressive the vehicle will privilege the full
electric driving turning OFF the ICE. It is expected that most Toyota Prius Plug-in users will
have a more soft behavior to potentiate the electric driving and reduce consumption. All the
following figures will be presented taking into account the “more aggressive” driving behavior
because that was the characteristic driving behavior during on-road tests.
Figure 49 shows CO2 emissions per VSP Mode on CS and CD mode.
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14
% t
ime
wit
h IC
E O
FF
VSP Mode
54
Figure 49 - CO2 emissions per VSP mode in CS and CD (Toyota Prius Plug-in)
The trend is exactly the same as the fuel consumption for the same reasons, as CO2 emissions
derives from the fuel consumption.
A comparison for the CO emissions on the referred two cases is present in Figure 50.
Figure 50 - CO emissions per VSP mode in CS and CD (Toyota Prius Plug-in)
For the HC emissions, those can be seen in Figure 51.
0
2
4
6
8
10
12
14
1 2 3 4 5 6 7 8 9 10 11 12 13 14
CO
2 (
g/s)
VSP Mode
CS CD
0
0,005
0,01
0,015
0,02
0,025
0,03
0,035
1 2 3 4 5 6 7 8 9 10 11 12 13 14
CO
(g/
s)
VSP Mode
CS CD
55
Figure 51 - HC emissions per VSP mode in CS and CD (Toyota Prius Plug-in)
The graph shows that, from mode 7 until mode 12 HC emissions in CD are higher. This can be
explained with the fact that in CD the engine is always more cold that when in CS. This happens
because the ICE is solicited in high loads while it is still cold.
The emission of NO is represented in Figure 52 and shows a peak on CD and VSP mode 14.
Figure 52 - NO emissions per VSP mode in CS and CD (Toyota Prius Plug-in)
This is explained by the solicitation of the ICE with very high loads and very high RPM’s while it
is still cold. This happens because the EM does most of the vehicle’s motion not turning ON the
ICE and consequently not allowing it time to warm-up.
On CD mode, although some fuel consumption was observed, due to power source
management by the vehicle controlling system, the main consumption on this mode will be of
energy from its battery to supply the EM. For this reason, is important to address this
consumption in order to assess the electric autonomy.
0
0,0005
0,001
0,0015
0,002
0,0025
0,003
1 2 3 4 5 6 7 8 9 10 11 12 13 14
HC
(g/
s)
VSP Mode
CS CD
0
0,0002
0,0004
0,0006
0,0008
0,001
0,0012
0,0014
0,0016
1 2 3 4 5 6 7 8 9 10 11 12 13 14
NO
(g/
s)
VSP Mode
CS CD
56
To achieve this it was mandatory to use the methodology developed during the present work,
which is fully explained on section 3.3. With the application of that methodology it was possible
to get the electrical consumption of this vehicle in CD and depending only on VSP Mode.
In Figure 53 it can be seen the distribution of battery consumption depending on VSP Mode.
Figure 53 - Electricity consumption per VSP mode in CD (Toyota Prius Plug-in)
As noticeable, mode 14 does not have any bar for electricity consumption; this is due to the fact
that no points were measured on mode 14 only relying on the battery. On mode 14 the ICE was
always ON. For mode 12 and 13, despite the fact the graph shows a bar, the number of points
were very small, having just four and two seconds respectively accounting for 4% and 6% of all
the point measured in these modes in CD. This shows that in those modes the ICE will be
solicited to provide the necessary power. This result can also be seen in Figure 46 where fuel
consumption in CS and in CD for those modes is practically coincident.
4.2.2.3. Charge Depleting high power
On the Toyota Prius Plug-in both the EM and the ICE are connected to the wheels by a
planetary gear. This allows the ICE to be either the only propulsion source, to work in
cooperation with the EM or the EM being the only propulsion source. This allows the power
management of the vehicle to choose from which source (battery or fuel tank) it will get the
power demanded by the driver. This is the reason why even when the car is in CD mode, the
Toyota Prius Plug-in can use the ICE for some isolated situations. This is what happens in high
power situations, such as high speeds, violent accelerations or high slopes. ICE is also turned
ON when vehicle speed is over 100 km/h, independently of VSP. Therefore it is important to
study these situations in order to compute the amount of power delivered by both the ICE and
the EM.
-5
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Elec
tric
ity
con
sum
pti
on
(W
h/s
)
VSP Mode
57
Figure 46 shows that for high VSP modes the fuel consumption in CS is very similar to CD. This
lead to the hypothesis that, due to the battery capacity of about 4.5 kWh4, the ICE provides all
the necessary power for the vehicle motion on those modes. Although the ICE contribution is
high, it was verified that in CD the EM will still assist the ICE. This battery consumption was
verified to be very reduced but existent. To assess its value the same methodology present in
section 3.3 was used but this time when the ICE was turned on. The equation of the
interpolation for the measured points in these conditions is:
Eq. (9)
4.2.3. Opel Ampera
4.2.3.1. Charge Sustaining
The Opel Ampera is a series PHEV. This means that its power management is quite different
from the Toyota Prius Plug-in, which is a parallel/series PHEV. Regarding the Opel Ampera,
because the ICE does not directly power the wheels, the car is maintained in CD mode with ICE
OFF and just using energy stored in the battery, until it reaches a certain battery SOC. It was
verified that the Opel Ampera enters CS mode when its battery SOC reaches a value of
approximately 24% of SOC (measured from OBD). Throughout this work this value of battery
SOC will be considered as the transition point from CD to CS. For the CS analysis, all the points
with a battery SOC below 24% were grouped. The number of points, measured on each VSP
mode for CS conditions, is shown in Figure 54.
Figure 54 - Time distribution per VSP mode in CS (Opel Ampera)
It can also be verified that during road testing it is hard to maintain high power demands (high
VSP Mode), the higher the VSP Mode, fewer seconds are measured. During on-road testing
6549 seconds were collected as usable data, approximately 1 hour and 50 minutes in CS for
4 As reported by the vehicle manufacturer
0
200
400
600
800
1000
1200
1400
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Nu
mb
er o
f se
con
ds
VSP Mode
58
this vehicle. For those measured seconds fuel consumption distribution, per VSP mode was
computed and the result is shown in Figure 55.
Figure 55 - Fuel consumption per VSP mode in CS (Opel Ampera)
The bigger deviation for VSP Mode 12, 13 and 14 can be explained by the smaller amount of
data in comparison to the other modes. For example Mode 12 has approximately half (105 s) of
the amount of seconds of Mode 11 (209 s).
When the behavior of the fuel consumption with VSP Mode is analyzed, Modes 12 to 14 have a
“uncommon5” behavior in comparison to conventional vehicles or even when compared with
Figure 35 from the Toyota Prius Plug-in case. It could be expected that the consumption would
follow the same tendency as the one verified on the Toyota case. However, the ICE on this
vehicle is not connected to the wheels and is used as a generator being consequently projected
to work under those conditions, maintaining approximately at full load from mode 10 onwards.
Considering the ICE maximum power data from the manufacturer and vehicle weight this
corresponds to a maximum of 36 W/kg or VSP Mode 13.
To better understand and explain these high VSP Modes behavior, subsequent analysis was
performed which are presented and explained in the next section.
CO2 emissions, as expected, follow a similar tendency as the fuel consumption. In Figure 56
this graph is shown.
5 On section 4.2.3.2 will be shown that the behavior is not uncommon, the word is used here in order to make a statement.
0
0,5
1
1,5
2
2,5
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Fuel
(g/
s)
VSP Mode
59
Figure 56 - CO2 emissions per VSP mode in CS (Opel Ampera)
As expected CO2 emissions follow exactly the same tendency as fuel consumption as these
emissions derive from fuel consumption.
For both fuel consumption and CO2 emissions, the value does not change much form mode 10
to mode 14; this cannot be said about the CO emissions. CO emissions maintain bellow 0.1g/s
until VSP Mode 9. From this mode on, it appears to have an exponential growth. This tendency
can easily be verified in Figure 57.
Figure 57 - CO emissions per VSP mode in CS (Opel Ampera)
The fact that most of these high VSP modes may have been achieved right after the end of CD
mode can explain this behavior. For high VSP modes the ICE has to work at high loads and
high RPM, resulting in less time for the oxidation of the CO on CO2. Also the fact that the engine
and the catalytic converter are cold makes the CO conversion less efficient.
Figure 58 shows the HC emissions for this vehicle on CS during on-road measurements.
0
1
2
3
4
5
6
7
1 2 3 4 5 6 7 8 9 10 11 12 13 14
CO
2 (
g/s)
VSP Mode
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
1 2 3 4 5 6 7 8 9 10 11 12 13 14
CO
(g/
s)
VSP Mode
60
Figure 58 - HC emissions per VSP mode in CS (Opel Ampera)
In VSP Mode 3 the HC emissions are low as expected due to the stop-start system, which turns
the ICE off when the vehicle is stopped.
For the NO case these emissions are kept low, only on VSP Mode 14 it shows a value
approximately 4 times bigger than all the others. This behavior is shown in Figure 59.
Figure 59 - NO emissions per VSP mode in CS (Opel Ampera)
This is a normal behavior due to the high loads and RPM in this mode, which allows less time
for the NO to recombine. This behavior is also verified for the Toyota Prius Plug-in.
As said before, some explanations for the behavior of the consumption with high VSP Modes
will be studied in more detail in the next section. This will be done in order to better understand
and explain the reason for the apparent “constant” consumption.
0
0,0002
0,0004
0,0006
0,0008
0,001
0,0012
0,0014
0,0016
1 2 3 4 5 6 7 8 9 10 11 12 13 14
HC
(g/
s)
VSP Mode
0
0,0001
0,0002
0,0003
0,0004
0,0005
0,0006
0,0007
1 2 3 4 5 6 7 8 9 10 11 12 13 14
NO
(g/
s)
VSP Mode
61
4.2.3.2. Charge Sustaining High Power
When results started to be studied, it was verified that in CS and on high power situations, from
VSP mode 11 to 14, fuel consumption does not keep increasing with VSP, as expected, but it
stays approximately constant. This can be verified in Figure 55. This is not a usual behavior for
conventional vehicles as can be seen on (17) or (15) for example, where VSP methodology is
also used. Further studies were necessary in order to understand this behavior.
Several possible causes were addressed and studied to verify their plausibility. Initially, and due
to its series configuration, the possibility of a delay between the power solicitation and the
response from the ICE to those solicitations was verified. Due to its high capacity battery pack
(16 kWh6) it was assumed that it could work as a power buffer, supplying the necessary power
until the start of the ICE. For this reason the delay possibility was considered plausible. Several
delays were studied from one to eight seconds delay, with the intention to account for a possible
delay of the ICE response. Applying a delay to the ICE response, consists of considering the
values of the consumption, d seconds after a generic situation. This will create the following
response: VSP(x)↔ CONSUMPTION(x+d) where x is a generic time point and d is the delay
considered in seconds.
Cases from d=0 until d=8 were tested even though above a delay of 5 seconds it is hard to
conceive a physical correspondence with the vehicle behavior verified under on-road conditions.
These tests consisted of both simulating the fuel consumption for all measured CS points and
compare these results with the measured values for fuel consumption during on-road testing.
The deviation between these two values is presented in Table 10. The negative sign of the error
shows that the estimations were below the real value. In addition to this, the sum of the
estimated consumption on each VSP Mode was also computed for each delay tested; these
results are also present on the referred table.
Table 10 - Delays tested and error of each estimation (Opel Ampera)
delays
tests 0s 1s 2s 3s 4s 5s 6s 7s 8s
error -4.8% -4.8% -4.7% -4.7% -4.7% -4.6% -4.6% -4.6% -4.5%
Sum of consumption 14.79 15.18 15.45 15.61 15.68 15.73 15.73 15.73 15.66
A second test was done, based on the assumption that, in a generic time point, the fuel
consumption would be an average of the following seconds, including the second of the generic
point. Two time frames were used, three and five seconds, in order to treat the propulsion
system as a black box. A correspondence like: VSP(x) ↔ (CONSUMPTION(x) +
CONSUMPTION(x+1) +…+ CONSUMPTION(x+n)) / (n+1). In which n was equal to 2 and 4
6 Manufacturer data (43), (49).
62
respectively was made. In Table 11 the results for these tests are presented. The presentation
of the results follows the same logic as the ones for the delays and explained above.
Table 11 – Time average tested and error of each estimation (Opel Ampera)
average
tests 3s 5s
error -4.8% -4.7%
Sum of consumptions 15.14 15.34
It was verified that with the introduction of a delay on the engine response, or the average value
of the consumption, results in fuel consumption transfer from low VSP Modes to high VSP
Modes. Figure 60 shows a clearer visualization of this behavior.
On the five cases represented in Figure 60, it is possible to verify the effects of the delay
introduction. Fuel consumption on VSP mode 1 and 2 became smaller, and from VSP mode 7
on, it increases.
A decrease on the error was verified with the increase of the delay, but as said before, values
above 5 seconds don’t have any correlation with reality. With the introduction of the average
values, the error reduction is negligible and therefore this explanation was abandoned. With that
being said and because the error reduction from delay of 0 seconds to delay of 5 seconds is just
0,2%, was decided to present on the previous section the direct application of the VSP
methodology, in other words, the 0 seconds delay.
After the previous explanation was abandoned, the possibility of battery energy consumption
under CS conditions on these high VSP modes was verified. This was simply derived from the
fact that if the ICE consumption is approximately constant, producing consequently,
approximately the same amount of work, the necessary energy to fulfill the power demand on
0
0,5
1
1,5
2
2,5
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Fue
l (g/
s)
VSP Mode delay 0s delay 5s delay 8s average 3s average 5s
Figure 60 - Fuel consumption per VSP mode for different tested solutions (Opel Ampera)
63
high VSP modes have to come from elsewhere. The only way the vehicle could get this extra
necessary power is by taking it from the battery.
Data was analyzed in more detail and with the same methodology present in section 3.3 in
order to assess for the battery consumption.
Points on CS and with similar VSP values were found and battery consumption was measured.
Battery consumption was verified in %∆SOC depending on VSP value, these results are
present in Figure 61.
Figure 61 - Variation of battery SOC per VSP value in CS (Opel Ampera)
The difficulty of getting this number of points resided in the fact that, during road tests this
possibility was not considered, and consequently forcing the existence of points in these
conditions was not an objective. Also, during on-road tests it was not possible to maintain high
VSP Modes for a long period of time, in order to maintain security not just for the measurement
crew but also for the other road users.
The result presented on Figure 61 show that the necessary energy for high VSP modes comes
from two sources. Part of it comes from the energy generated by the ICE and the rest comes
from the energy stored left in the vehicle batteries. This happens because the vehicle enters on
CS mode with a battery SOC of around 24% leaving still some energy stored for such high
power situations. The display of the vehicle shows that the battery is empty, even though the
OBD information shows that the vehicle still has 24% of SOC.
The value for the electrical consumption in these situations is about half of the value of the
consumption when in CD mode for the same VSP. For example with VSP≈35, in CS the battery
consumption is 0.065 %∆SOC/s and for the same VSP value in CD the battery consumption is
approximately 0.134 %∆SOC/s.
-0,095
-0,085
-0,075
-0,065
-0,055
-0,045
-0,035
-0,025
-0,015
-0,005 15 20 25 30 35 40 45 50 55
Bat
tery
co
nsu
mp
tio
n (
%∆SOC)
VSP
Mode 9 Mode 10 Mode 11 Mode 12 Mode 14 Mode 13
64
Considering the ICE maximum power data from the manufacturer and vehicle weight,
corresponds to a maximum of 36 W/kg or VSP MODE 13, the remaining power is, therefore,
supplied by the battery.
During the timeframe of the present work, it was not possible to assess, for the case when
power solicitation is maintained on high VSP for a long period of time, under CS modes, if the
vehicle available power will be highly decreased. This was not experienced on the road and
therefore cannot be discussed.
4.2.3.3. Charge Depleting
Due to its configuration as a series hybrid as it is explained in section 2.1.1, it was verified that
when in CD the Opel Ampera never uses the ICE, depending exclusive on the energy stored in
the vehicle battery. This fact makes it behave like an EV during this driving mode.
The developed methodology presented and explained in section 3.3 allowed the full
characterization of this driving mode without having to directly measure electric current. The
time distribution per VSP Mode measured on CD is present in Figure 62
Figure 62 - Time distribution per VSP mode in CD (Opel Ampera)
A total of 10.115 seconds of usable measured data was acquired. This corresponds to
approximately 2 hours and 50 minutes of on-road measurements on CD mode. This measured
data combines the vehicle usage in Normal and Sport Mode in order to make it more
representative.
With those measured situations and using the developed methodology, explained in section 3.3,
it was possible to compute the electrical consumption of the vehicle per VSP Mode. These
results are present in Figure 63.
0
500
1000
1500
2000
2500
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Nu
mb
er
of
po
ints
VSP Mode
65
Figure 63 - Electricity consumption per VSP mode in CD (Opel Ampera)
The negative values for Electrical consumption are present to account for the regenerative
capacity of the vehicle. This means that when the vehicle is in these modes it stores energy,
which will permit a reduction on global energy usage.
With the present results and taking into account the real7 battery capacity, it is possible to
compute the total electric autonomy of the vehicle, knowing for example the VSP distribution
representative of a certain trip. During CS mode and when the ICE is OFF, these results are
also applicable for when the ICE is switched OFF
4.2.3.4. Mountain mode (CD/CS)
In this particular mode, CD has a similar behavior to the one on section 4.2.3.3. The main
difference between this mode and the presented previously is the battery SOC from which it
enters on CS which is about 46% and not 24% as in Normal and Sport mode. As stated in (55) :
“This mode maintains a reserve electrical charge of the high voltage battery to provide better
grade climbing performance”. This supports what was said before on section 4.2.3.2 about the
battery consumption in high VSP modes and in CS. The statement said before simply means
that when high VSP modes are expected, is necessary to have a bigger amount of energy
stored (higher battery SOC) in order to support the power generated by the ICE, which may not
be enough. This is consistent with what was verified in practice for high VSP mode in Normal
and Sport Mode.
All the points in CS in Mountain mode were grouped, and their time distribution is as follows in
Figure 64.
7 The word real is used to distinguish between the battery capacity value available from manufacturer and the value that can be charged on the battery.
-10
-5
0
5
10
15
20
25
30
35
40
1 2 3 4 5 6 7 8 9 10 11 12 13 14Elec
tric
ity
Co
nsu
mp
tio
n (
Wh
/s)
VSP Mode
66
Figure 64 - Time distribution per VSP mode in Mountain mode
During on-road measurements, 2297 seconds of data were gathered on this mode, about 40
minutes.
The fuel consumption in this mode, and per VSP mode, was computed and is showed in Figure
65.
Figure 65 - Fuel consumption per VSP mode in Mountain mode
As shown, when compared with Figure 55 for CS on Normal mode, these consumptions are
much higher. This can be explained by the use of the ICE always at high loads in order to
generate the most amount of energy to keep not just powering the wheels but also to store
energy on the battery. This is made in order to keep having a power buffer for high VSP modes.
This driving mode is not further studied as most drivers will spend the biggest amount of their
driving time on the Normal or Sports mode.
4.3. Emissions due to electricity consumption
As was discussed in the introduction of this work (chapter 1), electricity generation is not a
carbon free process for the majority of countries, since fossil fuels are used to produce
0
100
200
300
400
500
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Nu
mb
er
of
po
ints
VSP Mode
0
0,5
1
1,5
2
2,5
3
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Fue
l (g/
s)
VSP Mode
67
electricity. The electrification of the transportation sector, by the use of electricity powered
vehicles namely Plug-in Hybrid Electric vehicles (PHEV), will not help to solve carbon emissions
if policies and measures are not taken in terms of decarbonizing power generation (9).
For this reason in this section, an estimation of carbon emissions at a global level due to the
use of PHEV is presented, more concretely the studied Toyota Prius Plug-in and the Opel
Ampera, when used in CD and exclusively consuming electricity from their batteries.
The Well-to-Tank (WTT) refers to the expended energy or emissions from bringing an energy
vector form its source up to the delivery to the vehicle fuel tank or battery. As a result, a
correlation between electric energy (Wh) consumed in the vehicle and WTT energy and CO2
emissions is required. For the Portuguese case (where these vehicles were tested), the data by
Baptista (56) was used, as is presented in Table 12.
Table 12 - WTT Energy consumption and emission factors for the different energy pathways
considered (56)
Energy source Pathway
WTT
Energy MJexp/MJ
CO2 g/MJ
8
HC g/GJ
CO g/GJ
PM g/GJ
NOx g/GJ
Gasoline EU mix 0.14 13 220 5.1 2.2 43
Diesel EU mix 0.16 14 100 4.6 1.2 37
Electricity 2010 mix 1.05 100 - - 21 218
This same analysis was made for other countries with different energy mixes, in order to
observe the influence that different electricity mixes have on the CO2 emissions. Table 13
presents equivalent CO2 emissions per MJ of electricity produced in a WTT basis. The
presented data considers the period between 2003 and 2011 in order to analyze the behavior
that some countries have had over the last years, in terms of carbon emissions from electricity
generation.
The current calculated data is based on information from Eurostat (Eurostat, 2013. Environment
and Energy, EUROPA Eurostat – Data Navigation Tree) regarding energy mixes of the several
countries. The countries were chosen in a way that the most diverse situations could be
addressed, from low carbon emission power mix, such as France, to high emissions power mix,
such as Ireland and Poland for example. Middle range situations are also presented in countries
such as Portugal and Spain.
Having assessed the energy consumption of the vehicles in their Tank-to-Wheel (TTW) stage,
especially in the quantification of their electric consumption, it is possible to simulate global
carbon emissions of a certain trip as well as total energy consumption. Figure 66 shows the
energy consumption from several vehicles performing a NEDC driving cycle in a TTW analysis.
8 CO2 emissions correspond to the CO2 equivalent emissions since they incorporate methane, perfluorocarbons, and
nitrous oxide emissions. (50)
68
Table 13 - WTT Energy consumption and emission factors for the different power mixes from 2003
to 2011 in gCO2/MJ
GEO\TIME 2003 2004 2005 2006 2007 2008 2009 2010 2011
European Union (27 countries) 129 125 126 126 126 123 119 115 115
New Member States (10 countries) 195 192 193 194 196 190 186 190 189
Ireland 206 205 205 196 190 185 175 178 170
Spain 123 131 142 131 134 121 108 85 99
France 20 19 21 19 19 19 20 22 18
Poland 270 268 267 268 268 265 264 260 257
Portugal 149 172 197 156 146 150 138 94 112
United Kingdom 169 171 168 175 175 177 162 167 160
Figure 66 - Energy use (MJ) for different vehicles in different driving modes accounting for the
energy source on NEDC driving profile
For the Toyota Prius (HEV), the information was obtained from previous works on this area (17).
For the Nissan Leaf this information comes from the certification results for this vehicle and
publicized by the manufacturer (57).
As seen in Figure 66 the Opel Ampera in CD has comparable energy consumption to the
Nissan Leaf, behaving as an EV. The Toyota Prius Plug-in under CS mode and the
conventional Toyota Prius show a similar energy use performing NEDC. The Toyota Prius Plug-
in under CD mode has lower energy consumption than in CS but has higher energy
consumption than the Opel Ampera in CD. When the Opel Ampera is used in CS it shows the
highest energy use comparing to the other vehicles presented.
Using the electrical consumption per VSP mode, presented on section 4.2.3.3 for the Opel
Ampera and present on section 4.2.2.2 for the Toyota Prius Plug-in, a comparison is made
0,0
50,0
100,0
150,0
200,0
Prius Plug-in CS Prius Plug-in CD Ampera CS Ampera CD Prius (HEV) Nissan Leaf
Ener
gy (
MJ)
Fuel (MJ) Electricity (MJ)
69
between the global emissions to perform a NEDC in CD and in CS. These results are present in
Figure 67 for both vehicles and in the Portuguese power mix present in Table 12. For the
Toyota Prius Plug-in, and because the NEDC is a soft driving cycle, the time distribution with
ICE OFF from Figure 48 will be considered.
Figure 67 - WTW CO2 emissions in CD and CS for the Opel Ampera and Toyota Prius Plug-in
performing a NEDC.
Figure 67 shows that in terms of CO2 emissions, driving in CD has advantages to reduce CO2
emissions for the Opel Ampera, CO2 emissions in CS are 2.6 times higher than in CD. For the
Toyota Prius, CO2 emissions in CS are about the same than in CD, in the Portuguese power
mix when driven in CD, accounting 20% due to the fuel consumed and the remaining due to
electricity consumed. This happens due to the use of the ICE at speeds higher than 100km/h or
high VSP modes. When different power mixes are taken into account, the differences verified in
Figure 67, can be even bigger. CO2 emissions for both vehicles, in CD and CS, in different
power mixes, results on the graph presented in Figure 68.
Figure 68 - WTW CO2 emissions in CS and in CD for different power mixes (Opel Ampera and
Toyota Prius Plug-in)
0
20
40
60
80
100
120
140
160
Opel Ampera Toyota Prius Plug-in
CO
2 e
mis
sio
ns
(g/k
m)
CD CS
0
50
100
150
200
250
Opel Ampera Toyota Prius Plug-in
CO
2 em
issi
on
s (g
/km
)
CS CD - Poland CD - UK CD - EU (27) CD - Portugal CD -France 100% renweable
70
Charge sustaining emissions are considered constant independent of the power mix, whereas
CD emissions change depending on the carbon intensity of the power mix.
Emissions in CD for the Toyota Prius Plug-in are always above the ones verified for the Opel
Ampera (when comparing the same power mix). This is due to the fact that the ICE is used in
CD on the Toyota, and on the Opel that does not happen. The results in Figure 68 show that for
the Opel Ampera is always beneficial to drive in CD, when compared to the CS case. For the
Toyota Prius Plug-In, one can say that that is not valid because depending on the power mix, it
can be more beneficial in terms of CO2 emissions to drive in CS, which is the case of Poland
and the UK. The Toyota Prius Plug-in when driven in CS will always have lower emissions than
the Opel Ampera driven in the same mode. This comparison allows addressing which vehicle is
more suitable for a certain country and trip. If it is also taken into account the availability of
charging spots the decision with the lowest global impact can be choose.
This can even be useful to generate a power management for future vehicles in which the trip
that is going to be done is introduced on the system, and taking into account the power mix of
that country and the trip, the vehicle chooses which driving mode will represent a lower impact.
This can be done for global impact in terms of CO2 emissions or in terms of the price of the trip.
In the next chapter this results will be applied to real situations in order to estimate emissions
and consumptions from both this studied vehicles.
71
5. Case studies
During the present work, the main goal was always kept in mind, which was to be able to predict
what will be the energy consumption and emissions from the measured vehicles when they are
used by different users in different regions.
Therefore four case studies were established, including a typical driver in Lisbon metropolitan
area (Portugal), a NCSU sample driver, and two real trips also in the Lisbon area, one with an
Opel Ampera and another with the Toyota Prius Plug-in.
To achieve the proposed goal, time distribution per VSP mode was used in order to predict full
electric range as well as average fuel consumption and pollutant emissions for the several
cases. Real driving profiles from on-road measurements were used in order to compare
predicted with measured electric range for both the vehicles. Electric range through this chapter
represents the driven distance until the start of CS mode depending on SOC in the beginning of
the trip.
5.1. Opel Ampera in a Real Trip
The developed methodologies were put into test by simulating a real trip that was measured on
board on the Opel Ampera. The objective behind this case study was to compare results for
electrical autonomy and emissions from the estimations by the developed methodologies and
from real values measured.
For this trip, a time distribution per VSP mode was performed and is represented on the
following Table 14.
Table 14 - Time distribution per VSP mode in a measured trip (Opel Ampera)
VSP mode
1 2 3 4 5 6 7 8 9 10 11 12 13 14
% of time
20.5 7.2 18.6 8.8 10.5 9.2 6.0 3.7 3.9 3.3 3.4 2.7 1.5 0.6
Simulating the full electric autonomy of this vehicle considering the time distribution of VSP
provides the results presented in Table 15.
Table 15 - Simulation results for a real trip in CD (Opel Ampera)
State of charge 100% 75% 50% 20% 10%
CD driving (km) 55.4 41.5 27.7 11.1 5.5
Electricity consumption (kWh/km) 0.244
WTW CO2 emissions9 (g/km) 88
On this trip, the vehicle was driven for about 55.5 km in reality until CS mode was reached.
When this value is compared with the obtained value for 100% of battery charge it can be seen
9 These results are for the Portuguese energy mix from 2010.
72
that the approximation is very good with an error of about -0.2%. All pollutant emissions,
excluding CO2, are equal to zero. This happens because under CD mode this vehicle only uses
the energy stored in its battery.
If this trip was all made in CS mode, its average fuel consumption would have been 5.7l/100km
and the average CO2 emissions would have been 151g/km as presented in Table 16.
Table 16 - Simulation results for a real trip in CS (Opel Ampera)
Fuel consumption 5.7 l/100km
CO2 emissions 151 gCO2/km
If the same trip was made with different battery states of charge, the resulting emissions and
fuel consumption will be different. Considering the 2010 Portuguese mix, the results for fuel
consumption and CO2 emissions for 4 distinct SOC levels are shown in Figure 69. A fully
charged vehicle, half charged and with 20 percent of charge was simulated and compared to
the 0% charge one (CS mode).
Figure 69 - Effect of battery charge in terms of fuel consumption and CO2 emissions (Opel Ampera)
This comparison shows that in terms of emissions and fuel consumption, charging the vehicle
has advantages in the reduction of its impact per km in this 55.5 km trip. There is a reduction of
40% on the CO2 emissions from the CS case to the fully charged case. In terms of fuel
consumption, the fully charged case almost doesn’t use fuel according to the simulations; as
can be seen in Figure 69. This shows the advantages of adopting the use of this vehicle, fully
charged, for this trip in terms of global CO2 reductions prospects.
A comparison of the results for this same trip on the fully charged case but charged with
different power mixes from different countries can be seen in Figure 70.
0
50
100
150
200
0
1
2
3
4
5
6
100% charge 50% charge 20% charge 0% charge
g/km
l/1
00
km
Fuel (l/100km) CO2 (g/km) (Portugal) CO2 (g/km) (EU(27))
73
Figure 70 - Effect of country power mix for global CO2 emissions reduction (100% charge) (Opel
Ampera)
It can be seen that depending on the energy mix of the countries, their Global CO2 emissions to
perform this trip with a fully charged vehicle will change. For countries with low carbon intensity
like France, this vehicle is a very good choice in terms of reducing carbon emissions from
transport sector. Even in countries like Portugal or Spain, when driven in CD mode, this vehicle
emits less CO2 that when in CS.
5.2. Toyota Prius Plug-in in a Real Trip
The developed methodologies were put into test by simulating a real trip that was measured on
board the Toyota Prius Plug-in.
For this trip, a time distribution per VSP mode was calculated and is represented in Table 17.
Table 17 - Time distribution per VSP mode in a measured soft trip (Toyota Prius Plug-in)
VSP mode
1 2 3 4 5 6 7 8 9 10 11 12 13 14
% of time
15.7 10.7 34.2 19.8 11.0 4.4 2.4 1.2 0.4 0.2 0.1 0.1 0.0 0.0
This trip is clearly an urban trip, characterized by lower power demands. On this case, more that
99% of the time is spent on VSP modes lower then mode 9. This is a driving context where this
vehicle will be at its best because it will use more the EM that the ICE for the propulsion of the
vehicle. For that reason, and because VSP distribution has more than 99% of its points below
VSP mode 9, a soft driving behavior was considered, with the percentage of points with ICE
OFF present in Figure 48.
For this trip the results obtained for electric range and consumption are present in Table 18.
Table 18 - Simulation results for a real soft trip in CD (Toyota Prius Plug-in)
State of charge 100% 75% 50% 20% 10%
CD driving (km) 18.9 14.2 9.5 3.8 1.9
Electricity consumption (kWh/km) 0.172
Fuel consumption (l/100km) 0.7
0
50
100
150
200
250
EU (27) Poland UK Portugal Spain France
Glo
bal
CO
2 e
mis
sio
ns
(g/k
m)
Countries
74
For this trip it was estimated an electric range of almost 19 km at full charge while the measured
value of the electric range was 18.2 km, which means that the estimation has an error of 4.2%.
For the fuel, a consumption of approximately 0.70 l/100km was estimated during this trip. The
measured value was 0.76 l/100km which represents an error of -4.1%. The soft driving behavior
described by Figure 48 resulted from this trip.
It was decided to simulate another trip that was measured on-board. This trip had a time
distribution of VSP as present in Table 19.
Table 19 - Time distribution per VSP mode in a measured aggressive trip (Toyota Prius Plug-in)
VSP mode
1 2 3 4 5 6 7 8 9 10 11 12 13 14
% of time
20.8 8.5 15.9 11.2 8.8 6.1 6.5 4.8 3.3 4.3 4.0 2.3 1.5 2.2
By analyzing Table 19 it can be seen that the time passed in VSP modes higher than 8 is
higher than 17%. This represents a more aggressive driving in opposition to the “softer” driving
presented in Table 17. This trip had an average speed of 61.3 km/h and corresponded to 35.1
km driven. Making the simulation of this trip considering an aggressive driving behavior, the
results obtain are present in Table 20.
Table 20 - Simulation results for a real aggressive trip in CD (Toyota Prius Plug-in)
State of charge 100% 75% 50% 20% 10%
CD driving (km) 34.6 25.9 17.3 6.9 3.4
Electricity consumption (kWh/km) 0.098
Fuel consumption (l/100km) 3.7
WTW CO2 emissions (g/km) 131
TTW CO emissions (g/km) 0.12
TTW HC emissions (g/km) 0.015
TTW NOx emissions (g/km) 0.005
The average electric consumption shows a lower value than in the previous due to the high VSP
modes of this trip during a considerable amount of time hence, the ICE was responsible for
most of the power delivery, making the average electrical use through the entire trip lower. The
estimate for the electric range for this trip has an error of -1.5% and fuel consumption resulted in
an error of -4.3%. In terms of pollutant emissions, the validation was only made for this trip
because this was an aggressive one and the results obtained for the averaged emissions in CD
include the influence of this aggressive driving. In this simulations CO emissions were estimated
with a 6.6% error whereas HC and NOx emissions have 5.0% and 33.2% of deviations
respectively when compared to measured values.
The final test performed, was a simulation that included a CD part as well as a CS one. In this
case the continuation of the last trip tested was considered. For this longer trip, the time
distribution per VSP mode is presented in Table 21.
75
Table 21 - Time distribution per VSP mode in a measured aggressive trip with CD and CS (Toyota
Prius Plug-in)
VSP mode
1 2 3 4 5 6 7 8 9 10 11 12 13 14
% of time
19.2 9.4 14.2 14.6 11.8 7.9 6.5 4.3 2.7 3.0 2.7 1.6 1.0 1.4
As seen in Table 21, similarly to the previous case, this trip can also be considered more
aggressive and, therefore, the correspondent driving behavior was considered for this
simulation. This considered trip had a length of 47.82 km with an average speed of 55.3 km/h
and a total fuel consumption of 1308 g (~1.8 liters of fuel).
The results for the simulated electric range and average consumption in CD are present in
Table 22.
Table 22 - Simulation results for a real, aggressive trip in CD (Toyota Prius Plug-in)
State of charge 100% 75% 50% 20% 10%
CD driving (km) 29.4 22.1 14.7 5.9 2.9
Electricity consumption (kWh/km) 0.116
Fuel consumption (l/100km) 3.1
If all this trip was made in CS, the results for average fuel consumption is present in Table 23.
Table 23 - Simulation results for a real, aggressive trip in CS (Toyota Prius Plug-in)
Fuel consumption 4.7 l/100km
CO2 emissions 118 g/km
As can be seen in Table 23, the value calculated for the electric range with a full charge is
below the total length of the trip. For that reason, was necessary to take into account not just
CD driving but also a certain length driven in CS. Thus, the calculation for the average fuel
consumption was considered 29.4 km driven in CD and the remaining distance, until the
47.8km, driven in CS. The average fuel consumption during CD mode was as seen, 3.1l/100km.
whereas for CS this average was 4.7l/100km. Making the weighted average by the traveled
distance on each mode, the result for consumption for all the length of the trip is present in
Table 24.
Table 24 - Simulation results for a real, aggressive trip accounting for CD and CS contribution
(Toyota Prius Plug-in)
Fuel consumption 3.7 l/100km
CO2 emissions 117 g/km
76
For this simulation the error on the fuel estimation was -1.1% when compared with the results
from on-road measurements.
Following what was done for the Opel Ampera, CO2 emissions will be calculated depending on
the vehicle state of charge in the beginning of that trip. This last trip will be considered in order
to have a trip with a longer length, comparable to the length considered on the Opel case. The
results for this simulation are present in the following Figure 71. Four cases were considered,
namely, a fully charged vehicle, a half charged, a vehicle with 20 percent of charged and the 0%
charged case, which means that the vehicle is in CS mode.
Figure 71 - Effect of battery charge in terms of fuel consumption and CO2 emissions (Toyota Prius
Plug-in in an aggressive driving)
In Figure 68 it can be seen that the fuel consumption decreases with the increase of the state of
charge whereas the global CO2 emissions remain practically constant in the Portuguese power
mix for this vehicle. Fuel consumption decreases about 21% when a fully charged vehicle is
used in comparison with the 0% of charge case (CS). For this trip, if different power mixes
are considered to charge the battery, the results for CO2 emissions for the 100% charged case
will behave as it can be seen in Figure 72.
Figure 72 - Effect of country power mix in terms of CO2 emissions (Toyota Prius Plug-in driven
aggressively)
0
20
40
60
80
100
120
0
1
2
3
4
5
100% charge 50% charge 20% charge 0% charge
CO
2 (
g/km
)
Fuel
(l/
10
0km
)
Fuel (l/100km) CO2 (g/km) (Portugal) CO2 (g/km) EU(27)
0
20
40
60
80
100
120
140
160
180
EU (27) Poland UK Portugal Spain France
CO
2 (
g/km
)
Countries
77
Carbon Dioxide emissions will decrease in countries where the power mix is low in CO2
emissions, as France for instance. This decrease is not very evident because in an aggressive
driving the ICE will be used for long periods. This results in a higher share on the CO2
emissions from the burn of the fuel, with the fully electric operation having a low expression in
aggressive driving.
In order to address the fuel economy when this vehicle is driven with a softer behavior, a
comparison between fuel economy between CS and fully charged vehicle was made for both
driving behaviors. The results for this comparison are present in Figure 73.
Figure 73 - Effect of driving behavior on fuel consumption reduction (Toyota Prius Plug-in)
It should be referred that the results presented are from simulation of different trips. One trip
was chosen in order to be more aggressive than the other. The results presented in Figure 73
for the fully charged and half charged case are only valid during electric range of the vehicle.
From that point on the vehicle will consume according to the CS bar. Figure 73 shows that when
driven in an economical way, as most of the drivers that buy a Toyota Prius Plug-in will, it can
have fuel savings of 80% (comparing fully charged with 0% charge), whereas when driven in an
aggressive way, fuel savings will just represent around 20% (comparing fully charged with 0%
charge).
5.3. Lisbon metropolitan area driver
For a driver on the Lisbon metropolitan area (LMA), a typical time distribution per VSP mode
can be seen in Table 25.
Table 25 - Time distribution per VSP mode for a typical Lisbon metropolitan area driver
VSP mode
1 2 3 4 5 6 7 8 9 10 11 12 13 14
% of time
12.4 10.5 22.0 12.8 9.0 6.7 5.0 4.6 4.7 4.2 3.1 1.9 1.3 1.6
This data resulted from monitoring 49 drivers during one week each. It represents almost 500
hours of driving with an average speed of 52.7km/h, which represented a total of 26209 km
0
1
2
3
4
5
Fully Charged 50% SOC 0% SOC (CS)
Fuel
co
nsu
mp
tio
n
(l/1
00
km)
Agressive Soft
78
driven (58). This data was used to simulate the use of both vehicles with a driving profile typical
from this geographical area.
Opel Ampera
For the Opel Ampera, full electric range (in Normal and Sport Mode) was calculated and the
results are present in Table 26:
Table 26 - Simulation results for typical Lisbon metropolitan area driver in CD (Opel Ampera)
State of charge 100% 75% 50% 20% 10%
CD driving (km) 44.2 33.1 22.1 8.8 4.4
Electricity consumption (kWh/km) 0.306
WTW CO2 emissions (g/km) 110
TTW CO emissions (g/km) 0
TTW HC emissions (g/km) 0
TTW NOx emissions (g/km) 0
Fully charged condition, is translated by 100% of charge although it does not correspond to
battery SOC of 100%. This 100% of charge corresponds to around 84% of SOC. In these cases
0% of charge corresponds to the start of the CS mode, which happens around 24% of battery
SOC.
At CS, the vehicle starts to use fuel, which does not happen during CD. The results obtained for
the average consumption in CS for a driver in LMA and its consequent WTW emissions are
present in Table 27.
Figure 74 provides a visual comparison between the average global CO2 emissions per km in
CS (from fuel consumption) and in CD (just from electrical consumption and with 2010
Portuguese mix). Both The results are in a WTW basis.
Table 27 - Simulation results for typical Lisbon metropolitan area driver in CS (Opel Ampera)
Fuel consumption 5.8 l/100km
CO2 emissions 154 g/km
CO emissions 3.1 g/km
HC emissions 0.03 g/km
NOx emissions 0.0035 g/km
Figure 74 - CO2 emissions in CD and in CS in a WTW basis for 2010 Portuguese mix for a typical
Lisbon driver (Opel Ampera)
0
100
CO
2 e
mis
sio
ns
(g/k
m)
CO2 emissions CD CO2 emissions CS
79
The left bar in Figure 74 is valid during the vehicle full electric range. From that point, the valid
value for its emissions are the ones of the right bar in the same figure.
Toyota Prius Plug-in
Simulation on this vehicle was not as straightforward as for the Opel Ampera case in which CS
and CD are clearly separated. This vehicle during CD can use only the EM, only the ICE or it
can combine both. For this reason, the contribution of each consumption factor should be
addressed during CD. For this time distribution of VSP, the results for consumptions, pollutant
emissions and electric range in CD mode for this vehicle are present in Table 28.
Table 28 - Simulation results for typical Lisbon metropolitan area driver in CD (Toyota Prius Plug-
in)
State of charge 100% 75% 50% 20% 10%
CD driving (km) 27.0 20.2 13.5 5.4 2.7
Electricity consumption (kWh/km) 0.125
Fuel consumption (l/100km) 3.8
WTW CO2 emissions (g/km) 145
TTW CO emissions (g/km) 0.13
TTW HC emissions (g/km) 0.016
TTW NOx emissions (g/km) 0.004
Electric range here refers to the amount of km driven until battery SOC reaches about 30%
(entrance in CS), this does not mean that the ICE is not used, as happened on the Opel
Ampera. The fuel consumption during CD mode, present in Table 28 illustrates that fact. This
mode (CD) will happen during the distances present in Table 28 depending on its state of
charge.
After spending all the energy contained on the battery, the vehicle enters CS mode. Average
values for fuel consumption and emissions if vehicle is driven in CS are present Table 29.
Table 29 - Simulation results for typical Lisbon metropolitan area driver in CS (Toyota Prius Plug-
in)
Fuel consumption 5.4 l/100km
CO2 emissions 136 g/km
CO emissions 0.3 g/km
HC emissions 0.01 g/km
NOx emissions 0.002 g/km
Figure 75 provides a visual comparison between the average global CO2 emissions per km in
CS (from fuel consumption) and in CD (from electrical consumption with 2010 Portuguese mix
as well as fuel consumption during high VSP modes). Both The results are in a WTW basis.
80
Figure 75 - CO2 emissions in CD and in CS in a WTW basis for 2010 Portuguese mix for a typical
Lisbon driver (Toyota Prius Plug-in)
It should be mentioned that this CO2 emissions in CD is valid just during full electric range of the
vehicle, as seen present in Table 28. After that, CO2 emissions will have the same value as the
present in Table 29 during CS.
CO2 emissions during CD for this VSP distribution are higher than in CS. This is due to the
considered power mix (Portuguese) and the use of the ICE even in CD.
5.4. NCSU sample driver
Using a sample of driving profiles collected in North Carolina State University, in United States
of America, was possible to calculate a time distribution per VSP mode for this sample
American driver, presented in Table 30:
Table 30 - Time distribution per VSP mode for an American driver
VSP mode
1 2 3 4 5 6 7 8 9 10 11 12 13 14
% of time
19.8 8.0 24.2 11.2 9.7 7.8 6.3 4.6 3.1 2.5 1.5 0.7 0.4 0.3
This distribution was obtained from previous works from Professor Christopher Frey from the
Department of Civil, Construction and Environmental Engineering, North Carolina State
University, and his students.
It can be seen that the time distribution per VSP mode of this sample of drivers differs greatly
from the Lisbon driver, especially in the higher power modes. This one spends 8.5% of the time
in the top 5 VSP modes (which represents the highest power modes) whereas the Lisbon driver
spends 16.8% of the time in those same modes, almost double the amount of time.
If just the top 3 modes (VSP Mode 12, 13 and 14) are taken into account, this sample spend
less than 1.5% of the time in those modes while in the Lisbon case it spends 4.8%, which
represents more than 3 times more time spend in those high power modes.
This fact will influence the full electric range of both vehicles in both situations.
0
100
200
CO
2 e
mis
sio
ns
(g/k
m)
CO2 emissions CD CO2 emissions CS
81
Opel Ampera
The simulation of the Opel Ampera (Chevrolet Volt in the American market) on a typical VSP
distribution for the NCSU sample driver results on the following data, present in Table 31, for all
electrical autonomy depending on the state of charge.
Table 31 - Simulation results for an American driver in CD (Opel Ampera)
State of charge 100% 75% 50% 20% 10%
CD driving (km) 82.9 62.2 41.4 16.6 8.3
Electricity consumption (kWh/km) 0.163
WTW CO2 emissions (g/km) 59
TTW CO emissions (g/km) 0
TTW HC emissions (g/km) 0
TTW NOx emissions (g/km) 0
When compared to the LMA driver, the American driver has an increase on full electric
autonomy. This is basically due to the fact that the American driven spends more time in
negative VSP values, being able to regenerate more energy.
A LMA driver spends 22.9% of the time in the first 2 modes, which are the regenerative ones,
whereas the American driver spends 27.8% of the time in the same conditions. The American
driver spends about 20% more time in these modes than the Lisbon driver. If we just consider
Mode 1, where the regenerative capacity is the highest, the LMA driver spends 12.4% of the
time on these modes whereas the American driver spends 19.8%, this represents about 60%
more time on this mode for the American driver. The estimation of the energy regenerated can
be slightly overestimated, which can be explained by the few modes on negative VSP values
which should be more refined due to regeneration to study this type of vehicles.
When in CS mode, and for the same typical driver, the vehicle will have the results present in
Table 32, for fuel consumption and emissions.
Table 32 - Simulation results for an American driver in CS (Opel Ampera)
Fuel consumption 5.2 l/100km
CO2 emissions 139 g/km
CO emissions 2.2 g/km
HC emissions 0.03 g/km
NOx emissions 0.0025 g/km
The result for CO2 emissions in CD and CS are compared in Figure 76.
82
Figure 76 - CO2 emissions in CD and in CS in a WTW basis for 2010 Portuguese mix for an
American driver (Opel Ampera)
It can be seen that with this power mix there is a reduction on CO2 emissions when driven in CD
of more than 50% of the CS emissions.
Toyota Prius Plug-in
If the Toyota Prius Plug-in is simulated for this sample of American drivers, the results present
in Table 33 for electric range and consumption are achieved.
Table 33 - Simulation results for an American driver in CD (Toyota Prius Plug-in)
State of charge 100% 75% 50% 20% 10%
CD driving (km) 27.9 20.9 13.9 5.6 2.7
Electricity consumption (kWh/km) 0.121
Fuel consumption (l/100km) 2.0
WTW CO2 emissions (g/km) 106
TTW CO emissions (g/km) 0.10
TTW HC emissions (g/km) 0.012
TTW NOx emissions (g/km) 0.0027
Because the NCSU sample driver has a less aggressive behavior, and because time
distribution per VSP mode is more close to the one verified for soft driving behavior, for this
simulation the time distribution with ICE OFF presented in Figure 48 was used for a more soft
driving behavior.
A small increase on electric range and a decrease on average fuel consumption during CD
were verified when compared to the LMA driver. This is explained by the reason stated on the
previous section for the Opel Ampera case and because this driver has a less aggressive
driving. If the same American driver drives this vehicle in CS, the average fuel consumption and
emissions are as shown in Table 34.
Table 34 - Simulation results for an American driver in CS (Toyota Prius Plug-in)
Fuel consumption 4.0 l/100km
CO2 emissions 100 g/km
CO emissions 0.3 g/km
HC emissions 0.008 g/km
NOx emissions 0.001 g/km
0
50
100C
O2 e
mis
sio
ns
(g/k
m)
CO2 emissions in CD CO2 emissions in CS
83
The result for CO2 emissions in CD and CS are compared in Figure 77.
Figure 77 - CO2 emissions in CD and in CS in a WTW basis for 2010 Portuguese mix for an
American driver (Toyota Prius Plug-in)
For the considered VSP distribution, and for the Portuguese power mix, the emissions will be
almost coincident. This means that, to take the best out of this vehicle in terms of CO2
emissions reduction, decarburization of the electricity generation is needed otherwise its global
impact is almost the same (If not worse) as for a conventional Toyota Prius.
0
50
100
CO
2 e
mis
sio
ns
(g/k
m)
CO2 emissions in CD CO2 emissions in CS
84
85
6. Conclusions and future work
The objective of this work was to assess the energy consumption and associated emissions
from the use of Plug-in Hybrid Electric Vehicles (PHEV). It was necessary to establish if the
Vehicle Specific Power methodology (VSP) was a valid methodology to characterize these
vehicles and also to estimate the electricity consumption and electric driving range, for which
indirect battery energy flows methodologies were developed and validated.
Two different PHEV where monitored with the use of a portable laboratory in order to perform
an energy and pollutant emissions characterization. Operational points were divided into CD
(Charge Depleting) and CS (Charge Sustaining), which represent the two main driving
conditions. A methodology to predict battery consumption was developed and validated by two
different means (comparison between estimated and measured current in a conventional
Toyota Prius with a maximum error of 8.1% for positive VSP, and comparison between
measured and predicted battery SOC for an Opel Ampera, resulting in an approximation with R2
equal to 0.998), allowing accurate calculations to be made on battery consumptions only
depending on the VSP. This methodology (which is fully explained in section 3.3) allowed
calculating the electricity consumption of each vehicle in charge depleting mode. This is
important in order to predict CD driving range as well as global impact in terms of CO2
emissions and total energy consumption.
The battery consumption prediction methodology provides a solution to estimate electricity
consumption without having to measure current flows, a possibly dangerous procedure, due to
the high voltage present in these systems.
The VSP methodology proved to be an adequate tool to characterize these vehicles, being the
only problem of it the lack of VSP modes for negative VSP. These modes are the ones where
these vehicles regenerate energy to its batteries. The fact that there are only 2 modes for
negative VSP can originate overestimations of the regenerative capacity as it groups a large
amount of points with different characteristics. For it to be more accurate, the introduction of
more modes on negative VSP should be done in order to better characterize the regenerative
capacity of Plug-in and, most probably, electric vehicles.
A simulation was done for each vehicle, in order to compare the results predicted by the
methodologies developed with on-road test data. Errors in electricity consumption, fuel
consumption and full electric range prediction were always found to be lower than 4.2%. During
this work it was possible to verify that the Toyota Prius Plug-in is a very efficient vehicle when
driven in CS but not as efficient in CD (strongly influenced by driver behavior), whereas the
Opel Ampera under CD mode is efficient (comparable to an EV) but in CS is less efficient then
the Toyota Prius Plug-in.
To perform the NEDC, in CD the Opel Ampera uses 59.6 MJ/100km which is similar to an EV,
(which consumes 49.9 MJ/100km, using Nissan Leaf official data). The Toyota Prius Plug-in
86
under CS mode has a similar energy use when compared to the conventional Toyota Prius
(HEV), 121.3 MJ/100km and 120.7 MJ/100km respectively. During CD mode the Toyota Prius
Plug-in showed lower energy use than under CS mode (again strongly dependent on driver
behavior), 91.25 MJ/100km in CD and 121.3 MJ/100km in CS, which is above the energy use of
the Opel Ampera in CD. The Opel Ampera showed a higher energy use than all the other
vehicles under CS mode, about 186.1 MJ/100km This can be explained by the extra weight of
the batteries and the energy losses due to double conversion of energy (from ICE to generator,
and from the generator to the EM). The energy use of the Toyota Prius Plug-in under CD
conditions depends greatly on the driver’s behavior. When driven by an aggressive driver,
during the CD phase of the trip a reduction of 20% of fuel was observed when compared to CS,
whereas when it is driven by a softer driver, a fully charged vehicle can reach savings of 80% in
fuel consumption when comparing CD with CS.
A LMA and a NCSU sample driver were simulated by the developed methodologies in terms of
energy consumption and pollutant emissions. The emissions of CO2 were calculated in a WTW
analysis because their impact is in a global scale whereas the other pollutants were simulated in
a TTW analysis because their impact is local.
For the LMA driver and when the vehicles were fully charged in the beginning of the trip, an
electric range of 44.2 km and 27.0 km was verified for the Opel Ampera and Toyota Prius Plug-
in respectively (In the case of the Toyota Prius this distance is not purely electric but the
distance the vehicle runs until reaching CS mode). This represents an average electricity
consumption of 0.305 kWh/km for the Opel and 0.125 kWh/km for the Toyota. This difference is
due to the use of the ICE in the Toyota case with an average fuel consumption of 3.8 l/100km
under CD mode whereas in the Opel the ICE is always OFF during CD resulting in zero fuel
consumption and zero pollutant emissions except for CO2 emissions which will depend on the
power mix where the vehicle is charged. If the same typical driver uses both vehicles in CS, it
will have a fuel consumption of 5.8l/100km and 5.4l/100 km for the Opel Ampera and Toyota
Prius Plug-in respectively. In terms of associated CO2 emissions in the 2010 Portuguese power
mix, the Opel will emit 110g/km in CD and 154g/km in CS. In the Toyota case, and during CD
mode it will emit 145 g/km whereas under CS mode it will emit 136 g/km.
For the American driver, for a fully charged vehicle an electric driving range of 82.8 km in the
Opel and 27.94km in the Toyota was verified. This represents an electricity consumption of
0.163 kWh/km in the Opel and 0.121 kWh/km for the Toyota under CD conditions; the Toyota
uses the ICE at high speeds or high VSPs which introduces a fuel consumption during CD
mode, this average fuel consumption is 2.0/100km under CD mode. If this driver uses both
vehicles in CS, their average fuel consumption will be 5.2l/100km and 4.0l/100 km for the Opel
and Toyota respectively. The associated emissions for this driver will also depend on the driving
mode (CD o CS, using 2010 Portuguese mix). For the Opel those are 58g/km in CD and
139g/km in CS. The Toyota for the same driver will emit in CD about 106g/km and about
100g/km in CS. The VSP methodology proved to be an adequate analysis tool for these
87
vehicles, as it allowed characterizing both vehicles in CD and in CS as well as computing full
electric range depending on time distribution per VSP mode.
These results show that using to the VSP methodology is possible to simulate the energy
impact and pollutant emissions from both the vehicles tested when driven by any type of driver.
For both simulated drivers (LMA and NCSU driver) the pollutant emissions are low and always
bellow European regulations, even EURO 6. This is just not verified in terms of CO emissions
for the Opel Ampera which can be explained with the fact that most of the high VSP points in
CS were obtained with a cold engine running at high loads, and also, because on normal use all
VSP range can be achieved, what does not happened during certification. This will provoke
higher CO emissions due to the use of the entire VSP spectrum. For these results to be more
accurate, more driving time under CS should be performed for this vehicle because most of the
measured trips during this work were under CD mode.
Future work includes addressing situations that need to be better characterized during on-road
testing. For the Opel Ampera, high VSP modes should be maintained for long periods in order
to verify the vehicle behavior in terms of battery consumption and available power, as it was
verified that in CS conditions the available power from the ICE is not sufficient to maintain high
power demands; the Mountain mode in this vehicle should also be better in order to understand
the ICE use; for the Toyota Prius Plug-in, situations with speeds above 100 km/h should be
maintained for longer time periods in CD mode in order to better characterize this situations in
terms of battery consumption and ICE use; a clearer distinction between what is considered an
aggressive or soft driving behavior for this vehicle should be addressed.
88
89
References
1. Associação Portuguesa de Energias Renováveis,
http://www.apren.pt/dadostecnicos/index.php?id=272&cat=266 (Last access May 2013)
2. THE EUROPEAN PARLIAMENT AND THE COUNCIL OF THE EUROPEAN UNION, DIRECTIVE
2009/28/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 23 April 2009 on the
promotion of the use of energy from renewable sources and amending and subsequently
repealing Directives 2001/77/EC and 2003/30/EC, 2009
3. International Energy Agency, Key World Energy STATISTICS, 2012
4. International Energy Agency, Transport, Energy and CO2 - Moving Toward Sustainability,
2009
5. EUROPEAN PARLIAMENT AND THE COUNCIL, REGULATION (EC) No 443/2009 OF THE
EUROPEAN PARLIAMENT AND OF THE COUNCIL, 2009
6. Eurostat,
http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&plugin=0&language=en&pcode=te
n00115 (Last access May 2013)
7. P. Coelho, M. Costa, Combustão, Edições Orion, 2007
8. European Environment Agency, The contribution of transport to air quality - TERM 2012:
transport indicators tracking progress, Copenhagen, 2012
9. R. T. Doucette, M. D. McCulloch, Modeling the prospects of plug-in hybrid electric vehicles
to reduce CO2 emissions, Applied Energy, Volume 88, issue 7 , p. 2315-2323, 2011
10. K. Yabe, Y. Shinoda, T. Seki, H. Tanaka and A. Akisawa, Market penetration speed and
effects on CO2 reduction of electric vehicles and plug-in hybrid electric vehicle in Japan, Energy
Policy, Elsevier, vol. 45(C), pages 529-540, 2012
11. N. Shiau and Ching-Shin, Impact of battery weight and charging patterns on the economic
and environmental benefits of plug-in hybrid vehicles, Energy Policy 37, 2653–2663, 2009
12. COPERT, http://www.emisia.com/copert/ (Last access May 2013)
13. United States Environmental Protection Agency,
http://www.epa.gov/otaq/models/moves/index.htm (Last access May 2013)
14. ADVISOR® Advanced Vehicle Simulator, Bigladder software,
http://bigladdersoftware.com/advisor/ (Last access May 2013)
15. Gonçalo Gonçalves, Energy and Environmental Monitoring of Alternative Fuel Vehicles,
PhD Thesis, 2009
90
16. H.C. Frey, H.W. Choi, E. Pritchard and J. Lawrence, In-Use Measurement of the Activity,
Energy Use, and Emissions of a Plug-in Hybrid Electric Vehicle, Paper 2009-A-242-AWMA,
Proceedings 102nd Annual Conference and Exhibition Air & Waste Management, 2009
17. R. A.Varella, G. O.Duarte, G. A. Gonçalves and T. L. Farias, On Road Monitoring of a Full
Hybrid Vehicle: An Environmental and Energy Characterization of the Powertrain, IV Encontro
de Ciência e Tecnologia do Gama, 2012
18. H.C Frey, A. Unal, J. Chen, S. Li and C. Xuan, Methodology for Developing Modal Emissions
Rates for EPA's Multi-scale Motor Vehicle Equipment emission System, Prepared by North
Carolina State University for Office of Transportation and Air Quality, EPA, Ann Arbor, 2002
19. J. L. Jimenez-Palacios, Sensing, Understanding and Quantifying Motor Vehicle Emissions
with Vehicle Specific Power and TILDAS Remote, PhD Thesis, Massachusetts Institute of
Technology, 1999
20. Society of Automotive Engineers, SAE J1715 Information Report - Hybrid Vehicle (HEV) and
Electric Vehicle Technology, Society of Automotive Engineers, 2007
21. http://en.wikipedia.org/wiki/Lohner-Porsche_Mixte_Hybrid (Last access May 2013)
22. http://en.wikipedia.org/wiki/Ferdinand_Porsche (Last access May 2013)
23. US Department of Energy, Alternative Fuels Data Center,
http://www.afdc.energy.gov/data/tab/all/data_set/10301 (Last access May 2013)
24. Honda Motor Coorporation, http://world.honda.com/automobile-technology/IMA/ima03
(Last access May 2013)
25. M. Ehsani, Y. Gao, A. Emadi, Modern Electric, Hybrid Electric and Fuel Cell Vehicles, Boca
Raton, CRC Press, 2010
26. S. R. Cikanek, K. E. Bailey, Regenerative Braking System For A Hybrid Electric Vehicle, Ford
Research Laboratory, 2002
27. http://umtri.umich.edu/content/Mike.Omotoso.LMC.PS21.2012.Presentation.pdf (Last
access May 2013)
28. European Environment Agency Service, Enerdata Research, 2007
29. International Energy Agency, Technology Roadmap - Electric and plug-in hybrid electric
vehicles, Paris, International Energy Agency, 2011
30. Edward W C. LO, Review on the Configurations of Hybrid Electric Vehicles, The Hong Kong
Polytechnic University, 2009
31. T.H. Bradley, A.A. Frank, Design, demonstrations and sustainability impact assessments for
plug-in hybrid electric vehicles, Renewable and Sustainable Energy Reviews 13, 115-128, 2009.
32. M. Ehsani, Y. Gao, A. Emadi, Modern Electric, Hybrid Electric and Fuel Cell Vehicles -
Fundamentals, Theory, and Design, CRC Press, 2010
91
33. F. Schäfer, R. van Basshuysen, Reduced Emissions and Fuel Consumption in Automobile
Engines, SAE International, 1995
34. John B. Heywood, Internal Combustion Engines Fundamentals, McGraw-Hill, 1988
35. Jorge Martins, Motores de Combustão Interna, Publindústria, Edições Técnicas, 2011
36. J. M. M. Barata, Motores Térmicos, Instituto Superior Técnico, 1992
37. E. Sher, Handbook of air Pollution from Internal Combustion Engines : Pollutant Formation
and Control, Academic Press, 1998
38. R. Basshuysen, F. Schäfer, Internal Combustion Engines Handbook - Basic, Components,
Systems and Perspectives, SAE International, 2004
39. Giancarlo Ferrari, Internal Combustion Engines, Società Editrice ESCULAPIO, 2011
40. H. J. Kwon, J. H. Baik,Y. T. Kwon, I. Nama, S. H. Oh, Detailed reaction kinetics over
commercial three-way catalysts, ELSEVIER, 2007
41. European Environmental Agency, CO2 emissions performance of car manufacturers in
2011, 2011
42. E. Tzirakis, K. Pitsas, F. Zannikos, S. Stournas, Vehicle Emissions and Driving Cycles:
Comparison of the Athens Driving Cycle (ADS) with ECE-15 and European Driving Cycle (EDC),
Global NEST Journal Volume 8, 2006
43. European Comission Community, Council Directive 88/76/EEC of 3 December 1987
amendig Directive 70/220/EEC on the approximation of the laws of the Member States relating
to measures to be taken against air pollution by gases from the engines of motor vehicles,
1991
44. http://en.wikipedia.org/wiki/World_Light_Test_Procedure (Last access May 2013)
45. http://www.dieselnet.com/standards/cycles/wltp.phppara (Last access May 2013)
46. M. Gumus, Reducing cold-start emission from internal combustion engines by means of
thermal energy storage system, Istanbul, Automotive Division Department of Mechanical
Education, Marmara University, 2008
47. A. Obieglo, J. Ringler, M. Seifert, W. Hall, (BMW Group Research and Technology), Future
EfficientDynamics with Heat Recovery, DEER 2009 –High Efficiency Engine Technologies, 2009
48. T.Endo, S. Kawajiri, Y. Kojima, K. Takahashi, T. Baba, S. Ibaraki, T. Takahashi, M. Shinohara,
Study on Maximizing Exergy in Automotive Engines, SAE International, 2007
49. T. A. Horst, H.S. Rottengruber, M. Seifert, J. Ringler, Dynamic heat exchanger model for
performance prediction and control system design of automotive waste heat recovery
systems, Munich, BMW Group Research and Technology, 2012
92
50. Z. Samaras, D. Tsinoglou, G. Mellios, A. Xanthopoulos,G. Koltsakis,E. Iakovou, Impact
assessment On Board Diagnostics (OBD) systems for passenger cars, 2005
51. OBD Key, http://www.obdkey.com/vehiclediagnostics.asp#VehicleInfo (Last access May
2013)
52. GM Portugal, Opel Ampera – Dossiê de Imprensa, 2011
53. Toyota Portugal, www.toyota.com/prius-plug-in/specs.html (Last access May 2013)
54. H. Frey, A. Unal, J. Chen, Recommended Strategy for On-Board Emission Data Analysis and
Collection for the New Generation Model, Prepared for Office of Transportation and Air
Quality, U. S. Environmental Protection Agency, 2002
55. General Motors, Vauxhall Ampera Owner's Manual, 2012
56. P. C. Baptista, Evaluation of the impacts of the introduction of alternative fuelled vehicles
in the road transportation sector, PhD Thesis, Lisboa, Portugal, 2011
57. Nissan Portugal, http://www.nissan.pt/PT/pt/vehicle/electric-vehicles/leaf.html (Last
access May 2013)
58. Nuno Pereira, Eficiência energética no sector dos transportes rodoviários: Metodologia
para quantificação do excesso de energia consumido devido ao factor comportamental na
condução de veículos automóveis ligeiros, MSc Thesis, Lisboa, 2011