light duty natural gas engine characterization thesis presented in partial fulfillment of the
TRANSCRIPT
Light Duty Natural Gas Engine Characterization
THESIS
Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in
the Graduate School of The Ohio State University
By
David Roger Hillstrom
Graduate Program in Mechanical Engineering
The Ohio State University
2014
Master's Examination Committee:
Professor Giorgio Rizzoni, Advisor
Professor Shawn Midlam-Mohler
Dr. Fabio Chiara
Copyright by
David Roger Hillstrom
2014
ii
Abstract
The purpose of this project was to characterize the baseline performance of a
2012 Honda Civic Natural Gas vehicle including: designing experiments to generate
complete performance maps, executing the experiments, and analyzing the experimental
data. In the end, the results yielded a deep understanding of the 1.8 L four cylinder CNG
engine’s combustion and air flow performance, as well as a good understanding of steady
state engine out emissions. This information is used to isolate inefficiencies in design and
propose possible avenues for improvement. The data that was acquired was then used to
inform an existing 1-D computational model of the same engine in order to determine if,
and where, the model was inaccurate, and determine what steps were necessary to
improve it.
The resulting test data provides a data based background to the well-understood
issues regarding a CNG port-fuel injected vehicle. The volumetric efficiency at low
engine speeds was typically around 70%, resulting in an IMEP loss of about 15%
compared to the engines peak possible performance. A CNG direct injection system is
one possible solution to this problem. Additionally, the engine efficiency and spark
timing map demonstrate that, even with the high compression ratio, the vehicle is not
currently limited by engine knock. This available pressure headroom could be used with
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boosting to improve the overall performance of the vehicle to bring it more in line with
consumer expectations.
The development of this natural gas vehicle technologies research platform will
allow the Center for Automotive Research at The Ohio State University to more easily
pursue CNG related research topics. Some particular thrust areas of interest regarding this
platform are the reduction of hydrocarbons while operating with lean burn, CNG direct
injection, turbocharging optimization, and possibly even CNG / gasoline concomitant
operation. The benefits to be had from these technology improvements can be gleaned by
examining the baseline performance covered herein.
iv
Acknowledgments
I would like to thank my advisor Dr. Giorgio Rizzoni for providing the
opportunities I have received since I arrived at The Ohio State University. He placed me
in the natural gas consortium project which allowed me to very quickly get my hands
dirty with heavy experimental work. Without this, I would have struggled to get such an
involved and independent project to use for my Master’s Thesis.
I would like to thank the Honda Partnership Program for their donation of a 2012
Honda Civic Natural Gas for our research. Without their support, there would have been
no foundation for this work to begin.
I would also like to thank my co-advisor Dr. Shawn Midlam-Mohler, Eric Shacht,
and Dr. Fabio Chiara for their continued guidance in my work and all the technical help
they have given me throughout my graduate career.
Finally I would also like to thank Dr. Jim Durand for providing extra
opportunities for me to get involved around the Center for Automotive Research to
ensure that my education extended beyond just the academic and into actual industrial
and business relations.
v
Vita
January 1989 Born – Tulsa, Oklahoma
December 2011 B.S. Mechanical and Aerospace Engineering, Oklahoma State
University
August, 2012 to Present Graduate Research Associate,
The Ohio State University,
Center for Automotive Research
Fields of Study
Major Field: Mechanical Engineering
vi
Table of Contents
Abstract ............................................................................................................................... ii
Acknowledgments.............................................................................................................. iv
Vita ...................................................................................................................................... v
List of Tables ................................................................................................................... viii
List of Figures .................................................................................................................... ix
Chapter 1: Introduction ...................................................................................................... 1
Brief Overview of the State of Natural Gas in US Energy ............................................. 1
CNG vs Gasoline ............................................................................................................. 3
CNG Vehicle Market Overview ...................................................................................... 6
Chapter 2: Literature Review ............................................................................................ 10
Direct Injection Executive Summary ............................................................................ 11
Geometric Design Considerations Executive Summary ............................................... 16
Hydrogen Executive Summary ..................................................................................... 18
Dual fuel and Bi-fuel Executive Summary ................................................................... 20
Combustion Executive Summary .................................................................................. 24
Noise, Vibration, and Harshness Executive Summary ................................................. 27
Emissions Executive Summary ..................................................................................... 28
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Chapter 3: Experimental Setup ......................................................................................... 31
Throttle Model: ............................................................................................................. 33
Combustion Model ........................................................................................................ 35
Emissions ...................................................................................................................... 39
Sample Timing .............................................................................................................. 39
Chapter 4: Engine Characterization Results ..................................................................... 43
Experimental Plan ......................................................................................................... 43
Thermodynamic Method for Locating Top Dead Center.............................................. 45
Calculating for the Throttle Model ....................................................................... 47
Fuel Burn Rate Analysis for the Combustion Model .................................................... 49
Emissions and Efficiency Analysis ............................................................................... 55
Volumetric Efficiency ................................................................................................... 60
Chapter 5: Integration With GT Power ............................................................................. 62
Chatper 6: Conclusions and Future Work ......................................................................... 67
Appendix A: Instrumentation ........................................................................................... 75
viii
List of Tables
Table 1. Motoring Tests and Resulting TDC .................................................................... 46
ix
List of Figures
Figure 1. EIA Natural Gas Data .......................................................................................... 2
Figure 2. EIA Fuel Price History and Projections .............................................................. 3
Figure 3. Laminar Flame Speed S1 at High Pressure and High Temperature [6] .............. 5
Figure 4. Mercedes B200 NGT With a Well-Integrated Fuel System [9] .......................... 7
Figure 5. Curves of Injector Needle Lift and Gas Mass Flow .......................................... 12
Figure 6. Scheme of the proposed SS simplification ........................................................ 13
Figure 7. WOT Torque-Speed Curves for Three Engine Classes ..................................... 15
Figure 8. Pre-Chamber Design Example .......................................................................... 17
Figure 9. Brake Thermal Efficiency against EGR ............................................................ 19
Figure 10. Normalized Bi-fuel BSFC ............................................................................... 20
Figure 11. BMEP at full load, nominal performance for each fuel .................................. 22
Figure 12. Laminar Flame Speed at 10x atmospheric pressure ........................................ 24
Figure 13. Schematic setup of a catalyst coated heat exchanger with bypass valve ........ 29
Figure 14. Aged bi-fuel taxi emissions measurements ..................................................... 30
Figure 15. 1-D Engine Model Block Diagram.................................................................. 32
Figure 16. Laminar Flow Element Setup .......................................................................... 34
Figure 17. Manifold Air Pressure Setup ........................................................................... 34
Figure 18. Sample Fuel Burn Rate with and ....................................... 35
x
Figure 19. Cylinder Head Cross Section........................................................................... 37
Figure 20. Cylinder Head Cross Section........................................................................... 37
Figure 21. IMEP Error as a Function of TDC Error ......................................................... 41
Figure 22. Crank Speed Fluctuation ................................................................................. 41
Figure 23. Testing DAQ Schematic .................................................................................. 42
Figure 24. Steady State Point Density .............................................................................. 45
Figure 25. CdA as a Function of Throttle Position ........................................................... 47
Figure 26. CdA as a Function of Engine Speed [RPM] .................................................... 48
Figure 27. Exhaust Pressure vs. Cylinder Pressure during Exh. Valve Open .................. 50
Figure 28. P-V Diagram with Gamma Values Indicated for Exp. and Comp. ................. 51
Figure 29. Heat Release Rates .......................................................................................... 53
Figure 30. CA50 as a Function of RPM and MAP ........................................................... 54
Figure 31. CA10-CA90 as a Function of RPM and MAP ................................................ 54
Figure 32. Total Hydrocarbon Emissions ......................................................................... 56
Figure 33.Steady State CO [% Vol.] ................................................................................. 57
Figure 34. Steady State NOx [ppm] .................................................................................. 57
Figure 35. Excess Air Ratio as a Function of RPM and Torque ....................................... 58
Figure 36. Total System Efficiency .................................................................................. 59
Figure 37. Spark Advance ................................................................................................. 59
Figure 38. Manifold Vol. Efficiency ................................................................................. 61
Figure 39. IMEP................................................................................................................ 61
Figure 40. MAF Error Using Throttle Input ..................................................................... 63
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Figure 41. MAF Error Using MAP Input ......................................................................... 63
Figure 42. Unmodified MAF Modeling Error [%] ........................................................... 64
Figure 43. Stock Intake Valve Lift Profile........................................................................ 65
Figure 44. MAF Error After Implementation of Tighter Valve Timing ........................... 66
1
Chapter 1: Introduction
Brief Overview of the State of Natural Gas in US Energy
Natural gas as a transportation fuel is not a new idea; however large finds of
natural gas, and the technology to recover this fuel at reasonable costs, have spurred
increased national interest in CNG. Although NG has been used as a fuel in IC engines
for a number of years, much development and optimization are still possible, both for the
case of dedicated and bi-fuel engines. World-wide emphasis on CO2 emissions
reduction/fuel economy improvements suggests that it may be worthwhile to make a
small investment to understand what can be achieved with a CNG (dedicated or bi-fuel)
engine in passenger car applications.
Even with oil production growing domestically, the US consumption vs
production ratio still hovers around 2:1 [1, 2]. This reliance on imported product is
fueling the search for domestic reserves of energy that may help the US reach ‘energy
independence’. The United State Energy Information Administration (EIA) natural gas
reserve data demonstrates a growing supply of proven natural gas reserves within United
States territory topping 300 trillion cubic feet [3]. This is about 12 times as much natural
gas as the country consumes annually providing a relatively large supply cushion. These
statistics can be viewed in Figure 1.
2
Figure 1. EIA Natural Gas Data
Historically, NG fuel has maintained a cost around half of that of competing fuels
such as gasoline and diesel. As Figure 2 demonstrates, The United States Energy
Administration Short Term Energy Outlook (STEO) does not predict this trend changing
within the near future as the price is protected by the abundant supply mentioned
previously. It is important to note that this natural gas price is based on EIA residential
pricing information and not on common fuel pump prices. The reason for using
residential pricing is that the data is readily available from reputable sources, such as the
US government, and this price is representative of the price one can find at the pump. If
this price differential persists, the amount of money that one could save by operating a
natural gas vehicle instead of a gasoline vehicle is significant.
1980 1985 1990 1995 2000 2005 20100
50
100
150
200
250
300
350
Year
TC
F o
f n
atu
ral g
as
Proven Total US Reserves
Total Annual US Consumption
3
Figure 2. EIA Fuel Price History and Projections
CNG vs Gasoline
The engine design approach behind a CNG vehicle and a gasoline vehicle should
be different due to some key differences in the thermodynamic properties of the fuel.
This section serves to give an overview of what some of the differences are and how they
can have a drastic effect on performance, emissions, or reliability.
Methane, the primary component of natural gas, is composed of one carbon atom
and four hydrogen atoms. This H/C ratio of 4:1 is advantageous to an engine’s
emissions as compared to gasoline which has an H/C ratio of about 1.85 [4]. The reason
being that during combustion, heat energy and oxygen mix with the methane to break the
molecular bonds and re-combine them. This ideally turns carbon into and hydrogen
into . Thus, if there is less carbon and more hydrogen in the reactants, there should
Jan-10 Jan-12 Jan-14 Jan-160
2
4
6
8
Fuel P
rices in $
/GG
E
STEO Projections
Residential NG Price
Gasoline
Diesel
Crude Oil
Present Time
4
be less and more in the products. This is indeed the case for CNG as compared
to gasoline as natural gas observes a emissions reduction of about 20% [5]. This is
the reason that natural gas is typically regarded as a ‘greener’ fuel than its petroleum
based brethren.
Another benefit of natural gas is its RON octane number which is typically much
higher than gasoline. This allows the fuel/air mixture to reach a much higher temperature,
and therefore a much higher pressure, before auto-ignition starts to occur. This extra
pressure headroom can be utilized during an engine’s design stage to achieve a more
efficient engine by increasing the compression ratio, a more powerful engine by
turbocharging inlet air, or some combination of these [5]. Additionally, the peak
compressed flame speed of natural gas is nearer to stoichiometry than gasoline and there
is no charge cooling effect from CNG which reduces the desire to run rich in high
performance operating modes. For example, gasoline race engines typically operate
around a lambda of 0.9 [6]. Figure 3 demonstrates these points.
5
Figure 3. Laminar Flame Speed S1 at High Pressure and High Temperature [6]
The caveat with the increased pressure headroom, lack of latent heat of
vaporization, and low lubricating capability is higher mechanical and thermal stresses on
the engine. If an engine is to be designed to run reliably on CNG and highly optimized
for CNG it must be reinforced or modified as compared to a typical gasoline engine
(crank-shaft, connecting rod, valve seats, etc.) [7]. This and other drawbacks associated
with natural gas result in some interesting design challenges.
As CNG is a gaseous fuel, it takes up a much larger volume than liquid fuels like
gasoline. As a result of this, typical injectors take a relatively long time to inject all the
fuel for a particular engine cycle. For example, some CNG race engines must incorporate
two injectors in each manifold port to meet the fuel delivery demand and can’t consider
direct injection presently as no available CNG fuel injector is able to deliver enough fuel
in the shortened window of time [7]. This is a design concern for any engine attempting
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to reach a higher RPM. Moreover, if the engine uses manifold injection, the gaseous fuel
expansion post-injector displaces a lot of air, leading to a detrimental effect on volumetric
efficiency [7].
Within the United States, natural gas vehicles are still rare in the light-duty
market. Other countries however, such as Germany and Italy, have a much higher
adoption rate as consumers are beginning to understand the significant cost savings that
can be realized.
CNG Vehicle Market Overview
Within the United States, CNG vehicles have yet to catch major consumer
attention with a light duty NG vehicle market share of about 0.1% [8]. In the light duty
sedan segment there is only one LD car available which is the natural gas version of the
Honda Civic. Europe however, has had more success, with a larger variety of models at
19. Part of my initial work with this topic was to extensively analyze the market in
Europe. This helped us to gather insight into what vehicle technology is available which
can direct our efforts when exploring how these vehicles can and should be improved.
Additionally, understanding the differences between what is available in the United
States, and what is available in Europe, might help to direct our attention to the possible
reasons for the low domestic NGV adoption rate.
Some of the major takeaways from the study are the key differences between the
technology present in the Honda Civic that we have experimented on, and the vehicles
7
available in Europe. The main idea behind a well-designed NGV is that the consumer
should not realize it is an NGV, it should be indistinguishable from a gasoline or diesel
counterpart unless it is better, else a consumer might not prefer the CNG version of a car.
Our study focused on a broad range of topics such as fuel system integration, vehicle
performance, refueling infrastructure, and government incentives. I will only discuss our
findings related to vehicle performance here.
Figure 4. Mercedes B200 NGT With a Well-Integrated Fuel System [9]
From a performance perspective, every natural gas vehicle (NGV) offered in
Europe is a bi-fuel vehicle [10] meaning that the vehicle can run on either CNG or
gasoline fuel. This helps to alleviate range anxiety associated with being uncomfortable
with the CNG refueling station density in one’s region. The downside of running a bi-fuel
vehicle is that the engine must be capable of handling gasoline which, in all present
cases, means it is not optimized for CNG. Additionally, every single vehicle is CNG port-
8
fuel injected, resulting in a substantial volumetric efficiency loss as compared to gasoline
operation. For some of these vehicles, this results in lower performance than gasoline.
However, others that have a turbocharger can control the boost pressure such that output
horsepower between both CNG and gasoline operation is virtually the same.
The bi-fuel engines present in these vehicles appear to be purpose built for
gasoline and then slightly modified to accept CNG. This leads to sub-optimal
performance as CNG engines should be designed around the much higher octane number.
An advanced CNG engine could utilize various control methods to control compression
ratio, and boost, in order to maximize performance and/or efficiency at every given
moment. The desire for such an optimized vehicle is what drives our efforts with the
present research.
This study is meant to lay the ground work for future efforts of OSU-CAR in the
arena of natural gas fuelled vehicles. The testing done herein will include information on
volumetric efficiency, steady state emissions, in-cylinder pressure curves, and heat
release analyses of a production 2012 Honda Civic NG. This study will help to provide
insight into what specific design changes should be considered in order to harness more
efficiency or performance where it is available, while simultaneously demonstrating the
emissions and efficiency benefits that CNG is already providing.
This research is done alongside another study utilizing the GT-Power software to
virtually explore the performance potentials of such an engine with modifications such as
boosting and direct injection. The experimental data harvested herein can be compared
with the results from the virtual test bench in order to validate the model. Afterwards,
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design changes can be implemented on the model for an initial perspective on expected
returns. The combination of these two theses will provide a compass for future
experimental and computational efforts.
Chapter two of this thesis will cover the literature review that was performed in
preparation of this thesis work, covering many aspects of CNG engine technologies from
research publications released in the last five years. Chapter three will cover the
motivations behind the experimental setup based on the intended use of the gathered
information. Chapter four will cover the analysis performed on the experimental data and
resulting key discussions. Chapter five will cover the integration of the information into a
previously developed GT power model for validation purposes. Finally, chapter six will
cover the main resulting conclusions of the work and recommendations for future studies
to continue the work.
10
Chapter 2: Literature Review
The focus of the work with the 2012 Honda Civic is to develop a platform in
which to pursue more focused topics of global interest. In preparation for the work with
the 2012 Honda Civic, an extensive literature review was performed giving insight into
what the major thrust areas are related to CNG. The results of the review will dictate the
future directions of this research. It is therefore paramount to ensure that all modern
developments are fully understood. As such, this literature review was focused on the
analysis of academic publications within the last seven years (2006+) related to
compressed natural gas automotive engine technologies. The study leveraged the
University’s access to publication networks such as SAE Digital Library, ScienceDirect,
OhioLink, SAGE Journals, and SpringerLink in order to review over 100 publications
with around 80 being analyzed in depth. The papers that were deemed relevant to our
interests could be lumped into the following categories: direct injection, geometric
design, hydrogen mixtures, dual-fuel and bi-fuel technologies, combustion, NVH, and
emissions [11].
11
Direct Injection Executive Summary
Direct injection is a topic that is garnering much interest in the pursuit of CNG
engine optimization. The volumetric efficiency losses from manifold injection are widely
known, and direct injection is the most obvious cure for this dilemma. However, since
CNG is a gaseous fuel it introduces several new dynamics to the injection system that
must be considered in order to have a well-functioning engine.
Recently, much effort has been devoted to the creation of accurate injection
models for CNG. The dynamics of the injector can arguably be very complex. Due to the
gaseous nature of the fuel, pressure wave phenomena are present within the fueling
system. In order to circumvent any negative effects from this, the fuel rail must be
designed intentionally such that the opening and closing of each injector does not
negatively affect subsequent injections through pressure wave troughs propagation.
Additionally, the act of opening and closing the injector needle itself is subject to
fluctuations. In a gasoline injector, the liquid fuel acts as a sort of damper on the injector
needle as it is opening and closing such that the needle does not have significant dynamic
fluctuations. However a CNG needle will bounce when it is commanded open or closed
causing pulsations of fuel to leak through the opening (Figure 5). At high engine speeds,
these pulses of fuel can account for up to 1/3 of the total fuel injected rendering the
understanding of this behavior significantly important [5].
12
Figure 5. Curves of Injector Needle Lift and Gas Mass Flow
In regards to 3-D modeling of the injection process, there are several levels of
complexity that have been tested. The unified goal of each is to somehow prevent an
asymptotically complex mesh near the injector tip typically required in order to
accurately model the behavior of this critical region. Firstly, a method of simplification is
to disregard phenomena upstream of the injector, accomplished by instead modeling a
sudden increase of pressure just inside the injector. This pressure rise then propagates
into the cylinder modeling the fuel injection. It has been observed, using the STAR-CD
environment, that no matter how this pressure increase is modeled, eventually the mass
flow rate coming into the cylinder will reach a steady state flow, typically within 1/3 the
time of a typical injection [12]. This idea of steady state flow was than extrapolated to
create a much simpler 3-D CFD code (Figure 6).
13
Figure 6. Scheme of the proposed SS simplification
This simpler injection method operates under the assumption that fuel mass flow
creates a quasi-steady jet into the cylinder. This jet carves out a conical area that is
predicted by a phenomenological model, whose boundaries impose the initial conditions
of the 3-D CFD code. This technique yields a much lower computational time due to the
fact that the injector tip, generally represented by the finest mesh, is now lumped into a
steady flow model [13]. The drawbacks associated with this methodology are that the
injector bounce is not accurately modeled, and any nuances present around the injector
tip are ignored.
It is important to note that these previously mentioned techniques yield quicker
results at the expense of accuracy as they do not properly model the injector behavior,
which is the most important aspect of a CNG direct injection system. The fluctuations
present at the injector, even if controls are held constant, can be significant. Conversely
14
to these simplification models, FKFS approached the problem in a slightly different way.
In order to maintain accuracy, a fine mesh is implemented near the injector tip, but a
slight modification to how the code views the incoming fuel can improve the mesh
performance without having to shrink to an unrealistically small size. The code views the
incoming fuel as very small droplets, not gas, which initially pass through the mesh; than
after some small distance from the injector, these fictive gas droplets evaporate without
any latent heat. Additionally this model considers the mass flow rate injection
fluctuations brought on by the injector tip bounce. All of these models have their
advantages, but the modeling approach from FKFS yields very promising results in terms
of its capability of predicting fuel jet development [5].
The ability to accurately model the fuel jet development is a significant step in
accurately modeling the combustion chamber in an engine; moreover the results garnered
from these simulations all yielded insight into important design considerations for flow
development in a direct injection engine running on CNG fuel. Namely, the characteristic
attributes of such a fuel injection. CNG direct injection, even at high injection velocities,
has little impact on the flow field within the cylinder, such that, compared to its gasoline
brethren, there is little impact on the level of turbulence in the fuel/air mixture. The
charge motion must be controlled through some other design parameter such as the intake
port layout or piston head. In order to represent this quantitatively, an injector was tested
in different orientations and the ISFC was measured to determine if any improvements
could be made. As the injector was angled from 50 degrees to 90 degrees, the ISFC
changed by no more than +- 1% [14].
15
An additional important parameter to consider when discussing a CNG direct
injector is the injection timing with respect to top dead center. In general, for a
homogenous mixture, the emissions and combustion stability improve the more advanced
the injection window, all the way up to intake valve close. The mixture needs as much
time as possible to smooth out any rich and lean combustion zones [15].
Figure 7. WOT Torque-Speed Curves for Three Engine Classes
In conclusion, the performance improvements a CNG direct injection system has
on engine can be substantial. In one case, a direct injection system was able to improve
power and torque by around 20% across the majority of the RPM range as compared to a
port injected system (Figure 7). Additionally, the BSFC of the engine was lowered by
16% yielding a significantly more efficient engine [16]. Another method for improving
the efficiency of an engine is to consider a stratified charge. However, the successful
16
implementation of stratified charge combustion is reliant on the design of the cylinder
geometry as discussed in the next section.
Geometric Design Considerations Executive Summary
The design of the cylinder geometry can be leveraged to enhance charge motion
control and is required for fuel stratification in order to enhance engine efficiency. In
regards to the application of a stratified charge, the design of the piston does not have to
be overly complex to be effective at creating a stratified charge near the spark plug.
Additionally, exotic fuel injection systems have been tested in an attempt to maximize the
efficiency of the engine, along with exotic crankshaft designs.
A simple and effective method for implementing stratified charge operation with
CNG fuel is a simple bowl in the center of the piston head with the fuel injector in a
perpendicular orientation directly above. As the piston is rising to top dead center, the
injector floods the bowl with fuel. The bowl can then hold the fuel in the middle of the
cylinder reasonably well to be ignited by the spark plug at TDC [17, 18]. Several
simulations have validated that this can successfully maintain ignitable mixtures near the
spark plug. The narrower the bowl, the leaner the overall mixture in the cylinder can be.
Experimental results show that this mixture formation method is ignitable but are not
conclusive as to the effect on the emissions and engine performance. Other injection
methods have also been explored involving more unique methods of mixture ignition.
17
Figure 8. Pre-Chamber Design Example
One of these methods is the utilization of a pre-chamber, a small crevice volume,
where ignition takes place as seen in Figure 8. Various approaches have been tested on
how this should be best utilized however all of them function on the basis that the pre-
chamber is ignited and then the flame propagates out to combust the mixture in the main
chamber. The novelties lie in how the pre-chamber is ignited whether by spark plug or
compression ignition, and the controls on how the two chambers interact [19, 20].
Thermal efficiencies have been observed as high as 44.1% using compression ignition,
while spark ignition also leads to stable engine operation at mixtures leaned out to a
lambda as high as 1.4. The compression ignition methodology is so sensitive that it was
only successfully controlled at steady state operation which is not conducive to its
implementation in a motor vehicle.
Other exotic designs have been tested as well such as the utilization of a z-shaped
crankshaft which allows for different compression and expansion strokes [21]. The
implementation of a higher expansion stroke allows for the engine to operate more
18
efficiently by extending the workable area on a P-V diagram reflecting in a 2.6% increase
in thermal efficiency and a 7% increase in measured fuel economy as compared to a
standard engine with a similar compression ratio; the only downside being the additional
complexity in the crankshaft.
Hydrogen Executive Summary
Hydrogen has been proposed to solve a setback typically associated with CNG
operation which is the low laminar flame speed as compared to gasoline. In practice, this
flame speed difference results in longer 10-90 CAD burn durations for the CNG fuel.
This problem can be circumvented by diluting the fuel with hydrogen as hydrogen is
known to burn very quickly. Many researchers have explored how different proportions
of hydrogen can affect the combustion within the engine with the general consensus
being that more hydrogen means a more efficient engine. However, the issue lies in
making hydrogen readily available to the consumer. A method for hydrogen integration is
custom tailored synthetic natural gas which is gaining popularity as an energy storage
medium. Experimental work dictates that hydrogen dilution of 40% by volume can lead
to about a 2% increase in thermal efficiency due to the higher laminar flame speed [22].
Hydrogen presence in the fuel also allows for a lower coefficient of variance due to the
ease with which the fuel/air mixture ignites, however power output tends to decrease with
increases in hydrogen dilution percent.
19
Figure 9. Brake Thermal Efficiency against EGR
When attempting to optimize the engine performance, there is a concern with
regards to hydrogen enriched CNG fuel mixtures. EGR is oftentimes used in order to
increase the efficiency of a CNG engine, however excessive amounts of hydrogen result
in excessive amounts of water in the exhaust gas [23]. Hence, care must be taken when
recycling too much water into the combustion chamber as this can lead to high cyclic
variability even with fairly modest amounts of EGR. The limit depends on the amount of
hydrogen in the fuel, but for a 25% by volume mixture, The EGR upper bound is about
8% less than if hydrogen was not present (Figure 9).
20
Dual fuel and Bi-fuel Executive Summary
CNG fuel has not yet integrated itself throughout the nation’s infrastructure. This
lack of refueling stations can lead to a well-known ‘range anxiety’ issue among
consumers. Therefore, CNG technology is presently only pushing hard into the heavy
duty scene due to the low cost of the fuel and the tendency for this customer to perform a
more complete financial analysis. On the light duty side of things, it is still necessary to
relieve this anxiety issue through granting CNG vehicles the capability of running on
alternative sources of power such as gasoline. The drawback with such an engine is that
an optimized CNG engine generally would not work with gasoline, therefore design
compromises must be made.
Figure 10. Normalized Bi-fuel BSFC
21
Nevertheless, CNG/gasoline bi-fuel engines may be necessary in courting the
technology into the market. As such, the performance benefits and possibilities are a
popular topic of study. Directly comparing the two fuels during operation yields the fact
that the CNG fuel can readily be more efficient in terms of BSFC (Figure 10) [24, 25].
The fuel inherently burns leaner than gasoline due to its higher stoichiometric air to fuel
ratio. Additionally, at high engine speeds, gasoline must sometimes inject extra fuel to
cool the exhaust gas for fear of harming the catalyst. In these high speed operating
regions, the CNG fuel can offer much better efficiency as it remains at stoichiometry
throughout the RPM band; unfortunately most bi-fuel engines have port injected CNG
resulting in a significantly detrimental impact to power . Moreover, having access to two
fuels on a vehicle raises the question of what happens if both fuels are used
simultaneously? Could one attempt to harvest the benefits of both, the knock resistance of
CNG, and the volumetric efficiency benefits of gasoline?
Research has been performed to see how concomitant injection may be optimized
and what benefits could be extracted from such a system. If the system is capable of
actively varying the proportion of gasoline and CNG entering the combustion chamber
there are optimization algorithms that could be employed depending on the desired
outcome (Figure 11) [26, 27, 28]. If maximum power is desired, the control scheme is
dictated as follows: less CNG at low RPM, mixed gasoline and CNG at mid RPM,
Mostly CNG at high RPM. Typically at low engine speeds, CNG fuel has less power than
gasoline due to the severe impact from volumetric efficiency. Therefore at low RPM it
could be considered best to use as much gasoline as possible. In the midrange RPM, it is
22
best to have a concomitant injection of the two with a gasoline mass fraction around 40%
which yields equivalent power to gasoline only operation. In the high RPM region it is
best to taper off the gasoline fraction in order to take advantage of CNG’s strong knock
resistance and to avoid having to enrich the gasoline injection. If maximum efficiency is
desired, than a different algorithm could be employed. This aspect of optimization yields
another level of control to bi-fuel vehicles.
Figure 11. BMEP at full load, nominal performance for each fuel
In the case of a CNG/gasoline bi-fuel engine, the CNG operational mode
generates higher thermal stresses on the internal components. Hot spot temperatures
within the cylinder can reach up to 20 degrees C higher for the CNG fuel. This is
generally due to the lack of latent heat benefits the gasoline fuel enjoys when it
evaporates. Hence, the cooling jacket must be developed with care [29].
23
There is another popular bi-fuel system, more commonly referenced as dual-fuel
which is usually the combination of CNG and diesel. In most cases, these engines are
converted diesel engines which operate with compression ignition. Compression ignition
however is not preferable to the CNG fuel. Therefore, CNG is considered the primary
fuel and enough diesel is injected such that its auto-ignition can serve as the combustion
catalyst to propagate a flame through the CNG/air mixture. A typical ratio for such a
mixture is 80-90% CNG with the rest being diesel pilot fuel [30, 31, 32]. For a vehicle
operating with this fuel, it is beneficial to the BSFC of the vehicle to increase the intake
air temperature. However, increased temperature within the cylinder can increase the risk
of knock onset, which has been an issue among active vehicles in Thailand [33]. The
increase of intake air temperature can also benefit CO emissions due to the fact the hotter
mixture promotes a more complete combustion. Conversely, this increased temperature
has a negative effect on NO emissions. The intake air temperature can be controlled via
exhaust gas recirculation
24
Combustion Executive Summary
The most important facet of efficient CNG operation is to develop a thorough
understanding of the combustion process. This section will attempt to bring together all
of the research ideas and innovations that have been discovered in the last five years in
order to shed light on the important considerations regarding the CNG combustion
process.
Figure 12. Laminar Flame Speed at 10x atmospheric pressure
The first topic of importance is that of the laminar flame speed which gives
insight into combustion quality. At ambient temperature and pressure, CNG has a higher
flame speed than that of gasoline. However at 10x ambient pressure, which is brought
25
about by a typical engine compression stroke, the peak laminar flame speed of gasoline is
65% faster than that of CNG (Figure 12). Something else of interest is that the laminar
flame speed of CNG is highest nearer to stoichiometry, therefore there is little incentive
to en-richen the mixture, whereas the peak for gasoline resides at an excess air ratio of
about 0.9. The rich gasoline mixture allows for a higher laminar flame speed and
manages to generate cooler exhaust gases through the consumption of heat through the
evaporation of the excess fuel [6].
The CNG fuel, being of a gaseous nature, does not absorb heat through
vaporization like gasoline fuel. This is a very important consideration in the design of a
race engine using a turbocharger as one of the design constraints is the temperature of the
exhaust gas entering the turbocharger. The temperature of this exhaust gas should not
exceed the tolerances of the materials used in the turbine. Typically, if CNG and gasoline
are running on two separate identical engines at stoichiometry, the exhaust gas from the
gasoline engine will be slightly higher than that of the CNG engine. However in a race
engine, the gasoline fuel mixture is run at a lambda of 0.9, the region promoting the
highest laminar flame speed. At this air/fuel ratio, the extra fuel in the cylinder absorbs
some of the heat causing the exhaust gas temperature to decrease to levels lower than the
stoichiometric CNG engine. The higher temperature in the exhaust gas for the CNG fuel
can be detrimental to the turbine reliability, but beneficial in the sense that this increases
the enthalpy of the fluid. This means a smaller turbine can be used while still maintaining
the same level of boost [6].
26
Boosting is a concept that has gained momentum as of late in the advent of fuel
economy relevance in engine design. The combination of engine downsizing and
boosting means that the engine can bridge the compromise between fuel economy and
performance. In this respect, CNG is extremely knock tolerant making it favorable to
such an application. So much so, that a particular engine running on CNG is capable of
more power through excessive boosting than that same engine running on gasoline, even
if the CNG is indirectly injected into the manifold intake port. This is once again
discussed in terms of its application to motorsports. Regulations restrict the ability of a
motorsports CNG engine to perform due to regulated air restrictors and maximum
allowable peak pressure, but if these rules are lifted, the engines could generate just as
much power, or more than gasoline, while still maintaining less CO2 emissions [6].
In contrast to CNG’s involvement in motorsports is the pursuit of maximal
efficiency. The fuel consumption of the engine can be reduced through leaning the
combustible mixture. Within CNG engines, the lean limit typically falls around an excess
air ratio of 1.2 to 1.3 for a homogenous mixture; however, experiments have shown that
proper stratification techniques can bring the lean limit as high as 1.8 [34]. Another
technique for enhancing efficiency is through exhaust gas recirculation. The probability
for ignition of CNG fuel in the combustion chamber goes up with increased air
temperature and the implementation of EGR allows for control of this temperature.
Moreover, the presence of the exhaust gas gives unburned hydrocarbons a second chance
at combustion reducing these emissions from the vehicle. However the intake air
temperature must be carefully controlled such that temperatures do not rise to knock
27
inducing levels. Of course, the CNG’s tendency to knock is dependent on its octane
number, which varies depending on what CNG fuel is used.
CNG fuel can vary significantly from one fuel pump to another. Therefore, an
engine programmed to operate on CNG fuel must be prepared to accept these variations.
Engine performance can be properly maintained as long as the vehicle implements some
form of adaptive AFR control. The ECU needs to recognize when the methane number of
the fuel has dropped and switch its control parameters accordingly. Typically, for CNG
fuel, a lower methane number means a higher presence of the heavier hydrocarbons
ethane and butane. These heavier hydrocarbons are beneficial to combustion as they
increase the density of the fuel, which reduces volumetric efficiency losses, and increases
the laminar flame speed. Conversely, lower methane number fuel has a higher tendency
to knock [35].
Noise, Vibration, and Harshness Executive Summary
CNG fueled engines have been found to operate more quietly than an identical
gasoline engine. The rate of pressure increase during combustion correlates with the
noise emissions of the process, and due to the lower flame speed of CNG fuel, this results
in quieter combustion. This means that a higher compression ratio can be utilized while
still maintaining the same level of noise output from combustion, or the engine can just
be more consumer friendly [36]. However, one point of concern for this fuel is the
injectors themselves. CNG injectors can cause loud pulsation noises due to pressure
28
waves when the injector needle bounces open and closed. This is highly depended on the
injector design, but is a concern nonetheless [37].
Emissions Executive Summary
The emissions of a CNG engine are heavily dependent on the design of said
engine but nevertheless certain trends exist. The major players in emissions regulations
are hydrocarbons, CO, NOx, and CO2. Thus, most of the papers which discuss emissions
focus on these key players. Generally, a CNG engine can be expected to produce less
CO2 than a gasoline or diesel counterpart due to the inherent nature of the fuel: CNG has
a much higher hydrogen/carbon ratio than gasoline or diesel. The NOx emissions are
dependent on the peak pressure/temperature within the combustion cycle and show no
clear trend for comparison with gasoline. Moreover, the CO and HC emissions can vary
from one engine to the next and are heavily dependent on engine speed. CNG fuel has a
tendency to hide in crevice volumes leading to incomplete combustion; additionally CH4
is a light hydrocarbon which more easily passes through 3-way catalysts leading to
increased THC emissions.
29
Figure 13. Schematic setup of a catalyst coated heat exchanger with bypass valve
These THC emissions remain a topic of focus when discussing CNG vehicles. A
catalyst can let slip large quantities of hydrocarbons before it is properly lit off, and
typically the CH4 coatings are near the back of the catalyst so it is the last section to
reach its operational temperature [38]. A solution to this problem is a bypass valve that
ensures the CH4 coatings are heated promptly (Figure 13). The low exhaust temperature
typical of CNG vehicles can additionally contribute to the delayed catalyst light off time.
Research has also been performed into a currently unregulated emission, ammonia.
Ammonia is expected to soon join the roster of unwanted emissions by government
agencies around the world, and should it succeed in doing so, CNG engines have a
tendency to produce about half as much ammonia as gasoline or diesel engines. This is
due to the fact that much of the hydrogen required for NH3 slips through the catalyst as
methane, deprived of the opportunity to recombine with nitrogen. However, as catalysts
evolve, so too may this problem.
30
Figure 14. Aged bi-fuel taxi emissions measurements
According to a study performed on an aging taxi fleet, the emission benefits
enjoyed by CNG vehicles as compared to gasoline should remain throughout the lifetime
of the vehicle. The purpose of this study was to compare the emissions of heavily used
bi-fuel engines operating in each mode (Figure 14). As expected the emissions were far
worse due to the aging of the catalyst, but the trends present in new cars are still present
in the used ones [39].
In conclusion, there are many opportunities for improvement and optimization of
natural gas engine performance. The performance potential of the high octane fuel, the
improvement of consumer acceptability due to lower combustion noise, and the control
of excess hydrocarbon emissions are just a few of these topics. This study hopes to shed
light on the relevance of these thrust topics to the 2012 Honda Civic Natural Gas
presently under investigation.
31
Chapter 3: Experimental Setup
The goal of this project is to develop a 2012 Honda Civic Natural Gas as an
experimental platform for exploring natural gas engine technologies. The first two tasks
conducive to this goal are: 1. Validate an existing computational model of a CNG engine;
2. Generate emissions out maps of the engine. Once the model is validated, it can be used
as a starting point for research into engine modifications such as direct injection, turbo-
charging, EGR, etc. The modeling software that will be used is the 1-D computational
software GT-Power.
It is important to note that the typical convention for characterizing an engine or
performing engine experimentation is to remove the engine from the vehicle and place it
on an engine test bench [41]. For the entirety of our experimentation, testing will actually
be performed in-vehicle on a light-duty chassis dynamometer. This allows us to get data
from a more realistic operating environment, with the caveat that there are additional
unknowns in our torque measurements, such as transmission dynamics and torque
converter losses. For the experiments performed herein, steps will be taken to reduce the
effects of the transmission on the data as much as possible and are detailed in the
experimental plan section of chapter 4.
The design of the experiments takes focus on what data is needed to inform the
computational model. Fortunately, a model has been previously developed at The Ohio
32
State University by a student projects team known as EcoCAR. The model is a 1-D
engine computational model in the GT Power environment for a 2008 Honda Civic GX.
GT Power attempts to model the gas dynamics of the plumbing in an engine efficiently
by only taking into consideration the forward and backward propagation of pressure
waves [40]. Pipe bends, splits, etc. are taken into account through pressure loss
coefficients. This method ignores the effect of full 3-D phenomena, but greatly improves
the model run time as one can simulate a whole engine in a matter of minutes with
reasonably accurate results. EcoCar’s particular model was calibrated to run on E85, but
the engine geometry between the 2008 Civic and the 2012 Civic are nearly identical. For
this reason, this model serves as an excellent starting point for our research. A flow chart
of how the model works can be seen in Figure 15. The black arrows represent piping
geometry, the red blocks represent models that have already been calibrated by the
EcoCAR team, and the green blocks represent the models that will be calibrated using
this experimental data.
Figure 15. 1-D Engine Model Block Diagram
Ambient Throttle
Model
Fuel Injector
& Intake Port
Combustion
Model
Exhaust
Port Ambient
33
Throttle Model:
The throttle model in GT Power uses compressibility equations for flow through
an orifice (equation 3.1 & 3.2) where 𝑚 ̇ is the mass flow rate of air through the throttle,
𝑝 is ambient pressure, 𝑅 is the specific gas constant for air, 𝑇 is the ambient
temperature, 𝑝 is the intake manifold pressure, 𝛾 is the specific heat ratio for air, and
is an effective discharge coefficient and area that changes as a function of throttle
opening. In GT-Power, one needs to input the array for as a function of throttle
opening angle. This can be easily calculated as long as all the other parameters are
known, therefore pressure and temperature will be taken at the specified locations during
experimentation.
𝑚 ̇ =
√ (
)
{
[ (
)
]}
if
≥ . 28 Equation (3.1)
𝑚 ̇ =
√ 𝛾
{
}
if
< . 28 Equation (3.2)
The mass flow rate of air will be measured by forcing all of the air the engine
intakes through a laminar flow element. The laminar flow element (LFE) forces the air
stream through extremely small flow channels effectively dropping the Reynolds number
into the laminar regime by lowering the characteristic length of the flow. This is
beneficial as it creates a linear relationship between the differential pressure of the flow
and the volumetric flow rate. The density for air can be calculated from the ideal gas law
34
and used to turn the volumetric flow into mass flow. One caveat from using a device like
this with an IC engine is the non-uniformity with which the engine breathes. The chaotic
pulsation of pressure waves as each cylinder breathes has to be dampened. This is
accomplished in our case by inserting a large oil drum for the pressure waves to disperse
in between the engine air box and the LFE and sealing the entire system.
The other measurements of temperature and pressure are acquired using K-type
thermocouples, a barometer, and piezo-resistive pressure sensors. Pressure is measured
immediately post-throttle for the 𝑝 and with a barometer for the 𝑝 terms. Ambient
temperature is measured at the LFE while 𝛾 and 𝑅 are considered constant at 1.4 and
287 [
] respectively. Pictures of the instrumentation can be viewed in Figure 16 and
Figure 17.
Figure 16. Laminar Flow Element Setup
Figure 17. Manifold Air Pressure Setup
35
Combustion Model
GT-Power has several options for modeling the combustion. The software can
even interface will full 3-D software packages like Star-CD. For the purposes of this
research, the wiebe curve fit will be used to model combustion which uses experimental
data to simulate the heat release rate from the fuel. A sample normalized burn rate taken
from the Honda Civic can be seen in Figure 18. The Wiebe function, seen in Equation
3.3, is not predictive in any sense, it just happens to be a function that matches up well
with the desired shape [41].
Figure 18. Sample Fuel Burn Rate with and
36
Equation (3.3)
The coefficients in the Wiebe function are not what GT-Power takes as inputs.
For GT-Power, the inputs are the crank angle position after TDC corresponding to 50%
fuel burned. Additionally, GT Power requires the 10% to 90% burn duration in crank
angle degrees. Examples of these are on Figure 18. In order to calculate the fractional
burn rate of the fuel, information is needed on the heat release rate within the combustion
chamber during the combustion cycle. An equation for this can be derived from the first
law of thermodynamics and is seen below in Equation 3.4.
Equation (3.4)
In order to model the heat release rate inside the cylinder we need access to the in-
cylinder pressure and its derivative and the in-cylinder volume and its derivative. The
volume vector and its derivative can be calculated from crank kinematics presuming one
knows the geometry of the cylinder [41]. The in-cylinder pressure, however, must be
measured directly during experimentation. One way to accomplish this is using an
extremely fast response piezo-electric pressure transducer mounted directly into the
engine. The initial plan for the project was to mount one sensor in each and every
37
cylinder. However, due to time constraints and the complexity of the cylinder head, it
was decided to only measure pressure in one cylinder on the outside of the engine.
Section views of the cylinder head can be seen in Figure 19 and Figure 20. In order to get
pressure in all cylinders, the sensors would have to be mounted on the top of the
combustion chamber; however there is very limited room to drill a hole here due to the
size of the intake/exhaust valves and the position of the spark plug. Conversely, the driver
side of the engine had an exposed section of solid aluminum that was very easy to access
and deemed sturdy enough to allow for a small hole. This location is indicated by the red
square in Figure 20.
Figure 19. Cylinder Head Cross Section
Figure 20. Cylinder Head Cross Section
Piezo-electric pressure transducers are great at monitoring very fast changes in
pressure, however the pressure measured is relative to some arbitrary zero point. This
arbitrary zero point can drift over the course of a test, requiring another known pressure
source in which to anchor too. For the purposes of these experiments the known pressure
source that will be used is the exhaust pressure just after the exhaust ports. Inserted here
will be a piezo-resistive pressure transducer which measures absolute pressure and does
38
not drift dramatically with time. As the exhaust ports are open, the two pressure sensors
are temporarily very close to each other and attached to the same stream of air and should
therefore report the same pressure. This information will be used in post processing to
move the in-cylinder pressure measurement higher or lower with each cycle as necessary
to ensure the two pressures are identical during this period of time.
The information calculated from the in-cylinder pressure is typically reported in
crank angle degrees and not in time. It is therefore necessary to measure the crank
position alongside the other measurements. In order to accomplish this, a 180 tooth
encoder disc with photo-interrupters for 1/revolution and every two degrees are mounted
to the passenger side of the crankshaft. It is worth noting that no production off the shelf
encoder would fit in the engine bay, therefore this disc was custom manufactured at The
Ohio State University using a water-jet CNC machine.
When performing an engine characterization, it is typical to remove the engine
from the vehicle and instrument it inside an engine test cell attached to an engine
dynamometer. This removes the unknowns associated with the transmission and
drivetrain. Unfortunately, in the case of our research facility, the engine test cells are not
outfitted for natural gas operation, specifically in terms of the high pressure fueling
infrastructure and fire safety codes. It was therefore necessary to design the
instrumentation in order to accommodate installation on a chassis dynamometer. Ergo, all
instrumentation had to fit within the engine bay. In this case, the testing could be
performed taking advantage of the vehicle’s onboard fueling system. The detailed
39
experimental plan for performing the steady state tests in this environment is detailed at
the start of chapter 4.
Emissions
In addition to the validation of the GT power model, this project also seeks to map
the emissions output from the engine. In order to accomplish this, a Fourier transform
infrared spectroscopy (FTIR) emissions analyzer was used to measure the engine-out
emissions before the three way catalyst. The FTIR analyzer compares the absorption
spectrum of the engine emissions sample gas against a known spectrum provided by a
constant stream of pure nitrogen. Each molecule absorbs different wavelengths in the
spectrum and the intensity of the absorption changes with increased quantity of the
molecule [42]. The quantitative capabilities of the FTIR have been calibrated by the
company that developed the instrument. Using this device we can track the volume
percent of several gases within sample gas stream such as methane, carbon dioxide, etc.
Sample Timing
The data acquisition system used is made up of a high speed National Instruments
data acquisition card and a low speed National Instruments data acquisition card inside a
Dell Workstation running Labview. Additionally, the CAN data is recorded using a
separate laptop within the vehicle and the emissions data is measured using another
40
separate laptop near the man DAQ computer. The reason for the separate computer in the
vehicle is due to the need to monitor CAN data in real time during testing, and the
availability of funds to set the system up. The need for a separate computer for the
emissions system is due to the fact that the FTIR was graciously loaned to us by the
company Stoneridge as a total package including its own software, computer, and
hardware. The two laptops record data at a standard low frequency rate, but as these tests
are steady state, the samples will be averaged for each test. On the other hand, the Dell
Workstation responsible for in-cylinder pressure measurements, uses a more complex
method for organizing the samples.
It is typical to trigger samples based on the digital signal from the encoder; this
ensures that data is recorded in the crank angle domain and not in the time domain.
However, as the encoder disc only had 180 teeth, and due to manufacturing reasons, we
could only use the leading edge of each tooth’s signal (the digital step up and not the
digital step down). This would translate into a sample resolution of two degrees. A very
small error in the location of top dead center, due to this relatively coarse resolution,
could lead to incorrect values in our determination of the wiebe curve fit parameters for
the GT Power model. If these values are off by even one degree, it will lessen the
predictive capabilities of the model by more than 5% in terms of IMEP and hence, BMEP
or torque / power prediction. This point is emphasized in Figure 21.
41
Figure 21. IMEP Error as a Function of
TDC Error
Figure 22. Crank Speed Fluctuation
In order to circumvent this issue, the data will instead be measured in time at very
high frequencies (80 kHz). This allows us to interpolate between the encoder teeth with
real data. This requires an assumption that the encoder rotational speed is constant in
between each encoder tooth for a duration of two crank angle degrees, but is a reasonable
assumption based on the small variability in speed seen in Figure 22. Additional details
on the instrumentation can be viewed in Appendix A with the Labview details in
Appendix B. A diagram of the testing equipment can be seen in Figure 23.
The experimental setup with provide pressures and temperatures in key location
in order to give insight into the performance of the engine in all its operational regions.
Additionally the logging of the data in time, rather than triggered by encoder pulse, will
allow much finer crank angle resolutions leading to more accurate determinations of top
dead center and indicated mean effective pressure. This will help minimize the error
associated with the collection of in-cylinder pressure helping to give more accurate data.
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2-10
-8
-6
-4
-2
0
2
4
6
8
10
IME
P E
rro
r [%
]
TDC Error [CAD]
IMEP Error [%]
100 200 3001050
1100
1150
1200
data number
Engin
e S
peed [R
PM
]
Figure 23. Testing DAQ Schematic
42
43
Chapter 4: Engine Characterization Results
This chapter will serve to give a detailed breakdown of the fundamental
objectives of the thesis: the design of the experiments, the execution of the experiments,
and the analysis of the experimental data. The goal of the experimentation is to generate
several maps of steady state performance for the vehicle. The performance maps will
typically be presented with engine speed vs torque in the x-y plane, with a number of
different parameters being exchanged for the z. This data will be useful for a number of
tasks that require understanding the baseline performance of the engine, namely, the
calibration of the GT Power model, and the understanding of engine-out emissions for the
purpose of guiding further exhaust research.
Experimental Plan
For the purpose of steady state map generation, the experimentation should match
the accuracy of a speed locked engine test cell as much as possible. In order to
accomplish this, the chassis dyno will be locked to a specific vehicle speed, such as 10
mph. This will hold the wheels at a fixed speed, which if the transmission remains
engaged, will indirectly hold the engine at a fixed speed. With the engine speed fixed,
different levels of torque can be reached allowing one to populate a torque vs. speed map
44
with steady state data. While performing these tests, as the Civic has an automatic
transmission, it must be ensured that the transmission does not shift. Even though The
Honda Civic has an automatic gearbox, the transmission controls are conducive to this
type of testing as it allows the car to remain in second gear, never changing to 1st or 3
rd
for any reason. Of course, this does not remove the transmission and torque converter
dynamics from our results. With these thoughts in mind, the following procedure has
been developed:
Procedure:
1. Place Civic in 2nd
gear
2. Bring dyno up to speed
3. Throttle to desired torque
4. Monitor MAP until deviations remain < 0.2 psi for 5 seconds (steady)
5. Collect Data
6. Repeat 3-5 for a total of six torque steps from low throttle to full throttle
7. Repeat 2-6 for speeds 5, 10, 15, … 55 mph.
In each operating point, it is recommended to take at least 100 engine cycles of
data for averaging purposes as the engine is susceptible to cyclic variability and must be
averaged for meaningful results [41]. This experimentation will record 250 engine cycles
at each data point to ensure that the answers are as robust as possible. The entire
operating space can be seen in Figure 24.
45
Figure 24. Steady State Point Density
The torque shown is that which is measured by the chassis dyno. Therefore this is
post-transmission torque. It can be seen that the torque converter is playing a large role in
the test at speeds less than 25 mph as it appears to be multiplying the torque reaching the
wheels. The slight creep up in RPM with higher torque can be attributed to more wheel
spin at higher speeds and a combination of wheel spin and torque converter slip at speeds
less than 25 mph.
Thermodynamic Method for Locating Top Dead Center
The method used for locating top dead center is explicitly from reference 43, “An
Universally Applicable Thermodynamic Method for T.D.C. Determination” by Marek J.
Stas. This method allows the determination of top dead center from in-cylinder pressure
0 1000 2000 3000 4000 5000 60000
20
40
60
80
100
120
Engine Speed [RPM]
Torq
ue A
fter
Div
idin
g O
ut
Fin
al D
rive R
atio [
N-m
]
5 mph
15 mph
25 mph
35 mph
45 mph
55 mph
46
and volume alone, as it is based on the heat transfer equation seen previously as 3.4. For
robustness, seven different motoring tests were performed for the application of this
technique. Motoring was performed on the chassis dynamometer by disabling the fuel
injector to our measured cylinder. The engine could than run using the other three
cylinders remaining warmed up. Table 1details the experimental points explored and the
final results of the TDC method. It is important to note that the ECU has safety measures
in place during miss-fire conditions such as this, so the ability to explore higher throttle
positions was hindered, nevertheless, engine speed effects on the algorithm were
explored. The TDC location is normalized to Test 1. The final results show six of the
seven tests within 0.1 degrees of one another, with one test 0.4 degrees off. These results
will minimize the error in our characterization of the engine’s performance due to
incorrectly phasing the volume vector with the data.
Table 1. Motoring Tests and Resulting TDC
Test # Engine Speed
[RPM]
Peak Pres. Loc.
[deg. ATDC]
TDC Loc. [deg.
ATDC]
1 670 -0.7 0.0
2 670 -0.7 0.0
3 670 -0.7 0.0
4 1330 -0.5 0.1
5 2140 -0.3 0.4
6 3640 -0.2 0.0
7 3640 -0.2 0.1
47
Calculating for the Throttle Model
In order to inform the throttle model, it is necessary to calculate the discharge
coefficient across the butterfly valve. As the effective area changes with increased
throttle angle, it is convenient to lump the discharge coefficient and area term together.
The results of the CdA term as a function of throttle position can be seen in Figure 25.
Figure 25. CdA as a Function of Throttle Position
The values seem to follow a trend until after around thirty degrees. From this
point the throttle jumps up to 84% where the pedal is actually at full throttle. The torque
however does not increase immensely from the last position around 30% to wide open
throttle. If we examine the CdA values from another angle, it might shed light on this
phenomenon.
0 10 20 30 40 50 60 70 80 900
1
2
x 10-4
Throttle Position [degrees]
CdA
CdA
48
Figure 26. CdA as a Function of Engine Speed [RPM]
From the perspective of engine speed, it can be seen that the peak CdA value
increases with engine speed. It is likely that above thirty degrees, the throttle is no longer
responsible for chocking the flow. At the larger throttle angles, another flow restriction is
the weakest link in permitting a higher mass flow rate for air. In this sense, the throttle is
oversized for this engine. This makes sense as the throttle should not limit the engine’s
full torque capability. Nevertheless, for the purpose of the GT Power model, a double
sigmoid curve fit can be fitted to the CdA values beneath 30 degrees and the highest CdA
value at full throttle as the throttle will not be responsible for choking the flow in the
upper region.
1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 60000
1
2
x 10-4
Engine Speed [RPM]
CdA
CdA
49
Fuel Burn Rate Analysis for the Combustion Model
This section will detail the process of using the in-cylinder pressure to calculate
the fuel burn rate. The in-cylinder pressure must first be used to calculate the heat release
rate and heat transfer within the cylinder as given by equation 3.4. The starting point for
this analysis is to first clean up the noise of the pressure signal and shift it using the
exhaust pressure. The theory that when the exhaust valve is open, the two pressures
should be completely identical, is not entirely true. This is due to the fact that much of the
exhaust manifold is integrated into the cylinder block making it difficult to place the
exhaust transducer as close as it needs to be to the exhaust valve. Therefore the exhaust
transducer is actually far enough away that it does not see some of the cylinder pressure
phenomena, demonstrated in Figure 27. In order to remedy this fact, a range of crank
angle degrees that are safely within exhaust valve open and close were averaged before
shifting. This range is indicated by the red bars in the figure. The entirety of exhaust
valve open to close was not used as during the higher speed and power cycles, the
cylinder pressure remained much higher than the exhaust pressure for several degrees
after exhaust valve open, which severely skewed the average.
50
Figure 27. Exhaust Pressure vs. Cylinder Pressure during Exh. Valve Open
The next step is to smooth the pressure signal. A first order Butterworth filter was
used within Matlab to this effect. The Butterworth filter cutoff frequency was altered
until it could be seen that the filtered pressure signal did not lose any of the magnitude of
the un-filtered signal and then applied across all cycles. The filtfilt command within
Matlab was used to ensure that the filtering did not shift the data. An ensemble average
was then performed to blend the 250 engine cycles into one average curve for the test.
This average curve is the one that will be used for future calculations.
One of the variables of equation 3.4, is the specific heat ratio (gamma) for the
mixture inside the combustion chamber. For air, this value is 1.4, however we cannot
assume that the combustion mixture will have the same value. One method for
calculating the specific heat ratio for the mixture during combustion is to look at the
loglog plot of the pressure vs volume [41]. The slope of the compression and expansion
curves is representative of the specific heat ratio for the mixture at those times. This value
350 400 450 500 5500.8
0.9
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
CAD
Pre
ssure
[b
ar]
Cyl P
Exh. P
Averaging Range
51
is somewhat complex to measure exactly as it is also dependent on the heat transfer
through the walls, therefore the compression and expansion strokes will result in different
gammas. For the calculations performed herein, the compression and expansion stroke
gammas will be averaged in order to find the final value that will be implemented into the
equation. Figure 28 demonstrates the difference in gammas, and how they appear on a
typical P-V diagram.
Figure 28. P-V Diagram with Gamma Values Indicated for Exp. and Comp.
With the gamma value calculated, the net heat release rate can be easily
calculated. The net heat release rate includes the chemical energy released by the fuel and
the loss of energy dissipating through the cylinder walls. In order to calculate the burn
rate of the fuel, these two parameters need to be separated. The method used on these
results to calculate the heat loss through the walls is the Woschni correlation for the
TDC 1/4 1/2 3/4BDC10
-1
100
101
102
Volume
Pre
ssu
re [b
ar]
averaged pressure
gamma exp = 1.286
gamma comp = 1.1424
52
convection heat transfer coefficient (equation 4.1 & 4.2) [41]. In these equations
represents the gas side heat transfer coefficient, B represents the cylinder bore, p is the
instantaneous cylinder pressure, T is the instantaneous cylinder temperature, w is the
average cylinder gas velocity, is the mean piston speed, is the engine displaced
volume, 𝑇 𝑝 are the reference temperature, pressure, and volume respectively,
𝑝 is the instantaneous motored pressure, and 𝑚 are model parameters.
= 𝑝 𝑇 . . Equation (4.1)
= [ ̅
𝑝 𝑝 ] Equation (4.2)
In these equations T, Sp, 𝑝 , and the reference variables have not yet been
discussed. T will be calculated using the ideal gas law, Sp can be calculated using engine
geometry and engine speed, and the motored pressure trace will be calculated using the
ideal gas behavior of = . For the motored pressure trace, the n value will
be 1.3 instead of 1.4 for air as this value must include the heat loss through the cylinder
walls [41]. After the gas side heat transfer coefficient has been calculated, the simple
equation for convective heat transfer can be used. However an assumption has to be made
as to the temperature of the wall, which will be kept constant at 370 K which is just
slightly higher than the temperature of the engine coolant. The heat release rate for a
particular test can be seen in Figure 29.
53
Figure 29. Heat Release Rates
The narrow peaks are attributed to noise induced in the pressure signal from the
spark plug. These peaks were removed before further calculations were made on the fuel
burn rate. The mass fraction burned can now be calculated from integrating the chemical
heat release rate using equation 4.3 [41].
=∫
∫
Equation (4.3)
In order to clean the graph of the heat release rate, the result will be forced to zero
when the spark happens, as no chemical heat should have been released up to this point.
Additionally, once the function reaches its maximum value, it will stop calculating in
order to remove the effects of the heat loss later in the expansion stroke. Figure 18
0 90 180 270 360-20
-10
0
10
20
30
40
50
60
CAD
dQ
/dth
eta
[J/d
eg
]
Q net
Q loss
Q chem
54
demonstrates the mass fraction burned and associated burn angles necessary for GT
Power. The resulting maps that will be implemented in GT Power can be seen in Figure
30 and Figure 31. GT Power will then reconstruct the fuel burn rate based on this
information and the operating point that is being simulated.
Figure 30. CA50 as a Function of RPM
and MAP
Figure 31. CA10-CA90 as a Function of
RPM and MAP
From these figures, the effect of engine speed on burn duration can be readily
viewed. Additionally, the variation in the 50% burn location is fairly small over most of
the operating region. It appears that at high load and high speed the prolonged burn
duration begins to have an impact.
55
Emissions and Efficiency Analysis
In this section, the engine out emissions will be presented and discussed. The
FTIR used is calibrated to measure a variety of hydrocarbons, the carbon monoxide, the
carbon dioxide, and the nitrous oxides present in the exhaust gas stream. The exhaust gas
was sampled prior to the three-way catalytic converter. These emissions are of particular
concern because they are regulated by the environmental protection agency and must not
exceed a certain value in order to allow the vehicle to go in to production. The values
regulated by the EPA are a vehicle’s g/mile emissions over particular drive cycles. As the
experiments performed were focused on steady state performance, discussions will be
limited to the volume fractions of each exhaust gas. These volume fractions translate into
higher or lower g/mile emissions depending on the air flow through the engine. The first
emission to be discussed will be the total hydrocarbons as it is arguably the most
significant CNG engine out emission.
The hydrocarbons emitted from a CNG engine are primarily composed of
methane (CH4). Methane is much harder to ignite in a catalyst then the heavier
hydrocarbons associated with gasoline and diesel as it requires a much higher
temperature [45]. As demonstrated by Figure 32, the hydrocarbon emissions from this
particular vehicle are somewhat consistent between 1000-1800 ppm across the entire
engine operating space. There is however a spike in hydrocarbon emissions at low speed
and low torque which will be explained later on after the discussion of air to fuel ratio.
56
Figure 32. Total Hydrocarbon Emissions
The other greenhouse gas emissions of interest are the nitrous oxides (NOx), and
carbon monoxide (CO). These gases absorb more radiation than CO2 and are therefore of
particular interest to regulatory committees such as the EPA. The Honda Civic steady
state emission for these gases can be seen in Figure 33 and Figure 34. The area of
intensified hydrocarbon emissions also lends to somewhat higher carbon monoxide
emissions which come about as a result of incomplete combustion.
57
Figure 33.Steady State CO [% Vol.]
Figure 34. Steady State NOx [ppm]
The emissions data can be used to determine the total system efficiency of the
vehicle by determining the energy available from the fuel flow rate and comparing it
against the measured power at the wheels. In order to do this, the emissions data must
first be used to determine the air to fuel ratio using the carbon balance method. As we
know the mass air flow rate into the engine accurately due to the LFE, we can use this air
to fuel ratio to accurately determine the fuel flow rate [41].
The FTIR records the volume percent of several chemicals. The chemicals of
particular interest to us are the total hydrocarbons, carbon monoxide, water, and carbon
dioxide. The equation for converting these values into an air to fuel ratio is commonly
known as the carbon balance method seen in equation 4.4.
=
[
] Equation (4.4)
58
In the carbon balance equation, y is the hydrogen to carbon ratio of the fuel and
each molecule is its volume fraction. y is heavily fuel dependent and, as discussed
previously, can vary from one sample of natural gas to another. For the purposes of this
analysis, the value used by the EPA to demonstrate the calculation mechanisms for fuel
economy with CNG fueled vehicles will be used, 3.97 [44]. The M values in the equation
are the molecular weights of air and fuel respectively. Air is a known quantity and will be
28.96 while fuel can be calculated by the H/C ratio, 16.01. Finally, the map of A/F can be
seen in Figure 35 presented as the excess air ratio with a stoichiometric A/F ratio of 17.2
as detailed by Heywood for methane [41].
Figure 35. Excess Air Ratio as a Function of RPM and Torque
Figure 35 shows that, for the most part, the engine runs ever so slightly lean
without any large deviations until the vehicle is at full throttle. When the vehicle is at full
throttle, the excess air ratio drops as low as 0.96 in order to enhance combustion. This is
59
still considerably less than most gasoline fueled vehicles may dare to go when pushed to
full throttle [6]. As mentioned previously, the A/F ratio can be used to determine the fuel
flow rate, which can be used to calculate total system efficiency (Figure 36).
Figure 36. Total System Efficiency
Figure 37. Spark Advance
The total system efficiency falls within expectations for a high compression ratio
internal combustion engine such as this, with a maximum value around 31% [41]. It is
also interesting to see that the efficiency remains very near 30% for most of the high
torque region. Comparing this information with the spark timing Figure 37 is evidence
that the vehicle is not retarding the spark out of fear for engine knock. Ergo there is likely
pressure headroom available for extra performance either through turbocharging or
potentially further increasing the compression ratio. Also of note, below 15 mph, it would
seem that the torque converter has a massively detrimental impact on total system
efficiency so these values are not necessarily representative of real world performance
and have been removed from the plot to avoid confusion. The efficiency in the low rpm
region depends heavily on torque converter lock-up controls at these engine speeds in
60
higher gears. In the future it would be interesting to re-perform these tests with a torque
converter lock-up override in order to get more consistent results in this region.
Nevertheless, the majority of the engine operating space is well populated.
Volumetric Efficiency
Natural gas is, as the name suggests, a gaseous fuel. Therefore its physical density
is far less than that of a liquid fuel such as gasoline and diesel. This is detrimental to
engine performance if the fuel injectors lie outside the combustion chamber as it does
with port injection. When the intake valves are open, the natural gas pushes a lot of the
air out of the way as they surge together into the cylinder leading to less air in the
combustion chamber, which in turn yields to less power from the combustion process [6].
Volumetric efficiency helps to quantify this effect representing the ratio of the actual air
in the cylinder against the amount of air that could get in the cylinder based on its total
volume. The map of the volumetric efficiency for the Honda Civic can be viewed in
Figure 38 along with the indicated mean effective pressure in Figure 39.
61
Figure 38. Manifold Vol. Efficiency
Figure 39. IMEP
The full load torque is the most important with regards to volumetric efficiency as
part load, by definition, is already restricted performance. At full load, the volumetric
efficiency ranges from between 72-81%. This results in an IMEP that ranges from about
9.5-11. The volumetric efficiency is directly responsible for the ~15% drop in IMEP at
the lower RPM values. These results verify the potential for either direct injection or
turbocharging to overcome this performance drawback.
62
Chapter 5: Integration With GT Power
This experimental work was done in parallel with a GT power model
investigation of the same engine. The model was previously developed by EcoCAR and
validated using their experimental data obtained on an engine dynamometer with the
2008 Honda Civic GX engine. In the validation of the model, one of the primary
modeling components of interest is the mass air flow through the engine. If the model can
accurately predict airflow, it will more accurately predict fuel flow providing the
foundation for a good engine model [46].
Initially, the engine was simulated by inputting engine speed and throttle position
and observing the resulting mass air flow values. As can be seen in Figure 40, this yields
fairly inaccurate results especially at lower mass air flows. This is due to the somewhat
complicated nature around modeling the throttle opening angle’s effect on air flow
through the intake system. In fact, the GT Power user document recommends not
attempting to model the throttle exactly, but rather forcing the throttle to achieve the
desired end result: its ability to match the desired manifold air pressure. Therefore, for the
immediate purposes of validating the model, the throttle calibration data that was
collected will not be implemented into the GT Power model until the air flow from
imposing forced manifold air pressure values is correct. It is therefore desirable to
remove the throttle variables from the equation through the use of a PID controller which
63
seeks out the required throttle angle to match the experimentally acquired manifold air
pressure. If the manifold air pressure is forced to the desired value in the GT Power
model, this sets up the air path to more accurately simulate flow through the rest of the
system including the intake/exhaust valves [46].
Figure 40. MAF Error Using Throttle
Input
Figure 41. MAF Error Using MAP Input
As can be witnessed by Figure 40 and Figure 41, the experimental data collected
from the EcoCAR team has excellent agreement with simulation results. However in this
case, if the model is taken as is and run to simulate the new experimental operating
points, the model consistently under predicts total air flow. This trend can be seen in
Figure 42. This could either mean that the model is not correctly simulating the engine, or
there is some error associated with the imposed manifold air pressure. In other words, the
measured manifold air pressure may not represent the actual values. Due to time
considerations, the validation of this hypothesis will be included as future work for the
project.
0 10 20 30 40 50 600
10
20
30
40
50
60
MAF-Experimental Data[g/s]
MA
F-S
imula
tion D
ata
[g/s
]
0 10 20 30 40 50 600
10
20
30
40
50
60
MAF-Experimental Data[g/s]M
AF
-Sim
ula
tion D
ata
[g/s
]
64
Figure 42. Unmodified MAF Modeling Error [%]
If the model is not accurately predicting air flow through the engine, even when
the manifold air pressure is forced to match the experimental data, the combustion model
has little chance of accurately predicting torque. It was therefore pertinent to investigate
the cause for the consistent air flow offset and move the combustion model validation to
future work for the project. As the manifold air pressure is matching the experimental
data by design of the model, the air flow restriction would seem to be between the
manifold and the cylinder. This leads one to believe the valves from the 2012 Honda
Civic Natural Gas must not be correctly represented in the model. The stock valve timing
implemented in the model can be viewed in Figure 43.
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-40
-30
-20
-10
0
10
20
Manifold Air Pressure [bar]
MA
F E
rror
[%]
20 mph
25 mph
30 mph
35 mph
40 mph
45 mph
50 mph
55 mph
5 mph
65
Figure 43. Stock Intake Valve Lift Profile
The suspicion is that the somewhat delayed closure of the implemented valve
timing is allowing a lot of air to flow back through the intake ports during compression.
In order to test this, the valve timing was modified to advance the closing crank angle
position. This was accomplished by scaling the valve lift duration by a factor of 0.95,
effectively making the valve lift profile 5% shorter, and also shifting it such that the
intake valves continue to open at the same point. The simulations were then re-ran with
the new valve timing to see if this had a positive effect on the mass air flow error.
66
Figure 44. MAF Error After Implementation of Tighter Valve Timing
From Figure 44, it can be seen that this is extremely beneficial to matching the
predicted air flow against the experimental airflow. In conclusion, the cylinder head
needs to be re-flowed in a flow lab and the valve lift profiles need to be re-calculated
using information from the new engine. The assumption that the intake and exhaust
valves were the same here is apparently invalid.
The flowing of the new head and experimental characterization of the new valve
lift profile will not be covered herein, but will be included in the desired future work. As
the air flow is not very well predicted by the model, the implementation of the
combustion model will be put on hold until the issues regarding valve timing have been
resolved.
In conclusion, the air flow of the 2012 engine seems to have been improved over
the 2008 engine of which this model was derived. Nevertheless, the model still provides a
good starting point for the work, as the intake manifold, and air path are very similar,
however the cylinder head geometry changes must be taken into account.
0.3 0.4 0.5 0.6 0.7 0.8 0.9-40
-30
-20
-10
0
10
20
Manifold Air Pressure [bar]
MA
F E
rror
[%]
20 mph
25 mph
30 mph
35 mph
40 mph
45 mph
50 mph
55 mph
5 mph
67
Chatper 6: Conclusions and Future Work
The primary goal of this research was to design and execute the experiments
necessary to characterize the performance of a 2012 Honda Civic Natural Gas, analyze
the data from the experiments, and prepare the results in such a way that they can be used
to inform a computational model. Additionally, this work was designed to build up the
2012 Honda Civic Natural Gas into a new experimental platform at The Ohio State
University for further natural gas research.
The experimental procedure has been developed for characterizing an engine on a
chassis dynamometer through the use of the dyno’s ability to lock the roll speed. Locking
the roll speed is an effective way of locking the engine speed through the transmission
allowing the operator to step through different values of torque in order to populate a
steady state performance map. The instrumentation and data acquisition systems
necessary to facilitate such experimentation were also developed. The data acquisition
system is mobile and can be moved to any experimentation room necessary, and all
instrumentation was designed and installed in-vehicle such that the vehicle can still
operate like normal, allowing it to be moved where necessary.
The testing results reinforce what is commonly known to be design challenges on
natural gas vehicles. The volumetric efficiency is a performance limiting factor as the
natural gas displaces air during the injection process. At full throttle the volumetric
68
efficiency resides around 80% at high RPM but drops as low as 70% at lower engine
speeds. A potential avenue to overcome this issue is to move the fuel injectors into the
combustion chamber operating as a direct injection vehicle. Additionally, the spark
timing and efficiency maps demonstrate that the vehicle is operating very near maximum
brake torque at all times. The total system efficiency hovers around 30% for the majority
of the high torque map and the spark timing does not significantly retard beyond 20
degrees spark advance meaning the vehicle is not presently concerned with engine knock.
This may potentially point to the possibility of enhanced performance through
turbocharging without having a significant impact on efficiency as there may be a
significant amount of peak pressure headroom for most of the engine operating space.
The data gathered has been used to validate a GT Power model. The GT Power
model was developed to simulate a 2008 Honda Civic GX engine by a student projects
team several years ago and was used as the starting point for the model. After initial
investigations of the differences between the two engines, it was concluded that they
were similar enough that no serious geometry modifications had to be made. The data
from the 2012 Honda Civic Natural Gas proved that the cylinder head had in fact
changed and needs to be re-calibrated using flow bench data for the new components.
This work is outside the scope of the present project, but would nevertheless need to be
performed in order to utilize the GT Power model for further design studies on the engine
if the results are expected to have meaningful relationships with the experimental
platform.
69
In order to further investigate the engine performance it would also be necessary
to acquire control of the vehicle ECU. This would enable direct control over things like
spark timing, fuel injection timing, and torque converter lock-up. With these parameters
controlled, it would allow the systematic removal of variables allowing a more precise
identification of the different control parameter’s impact on vehicle performance.
Nevertheless, the baseline performance and control of the vehicle is now well
understood. This new experimental platform can now be utilized for further design
studies involving advanced natural gas vehicle technologies.
70
References
1. U.S. Energy Information Administration. (2014). U.S. crude oil supply &
disposition summary. Retrieved from
http://www.eia.gov/dnav/pet/pet_sum_crdsnd_k_a.htm
2. U.S. Energy Information Administration. (2014). U.S. product supplied.
Retrieved from http://www.eia.gov/dnav/pet/pet_cons_psup_dc_nus_mbbl_a.htm
3. U.S. Energy Information Administration. (2014). Natural gas reserves summary.
Retrieved from http://www.eia.gov/dnav/ng/ng_enr_sum_dcu_NUS_a.htm
4. U.S. Environmental Protection Agency. (n.d.). 40 CFR 86.1342-94 -
Calculations; exhaust emissions.. Retrieved From
http://www.law.cornell.edu/cfr/text/40/86.1342-94
5. Chiodi, M., Berner, H., and Bargende, M., "Investigation on different Injection
Strategies in a Direct-Injected Turbocharged CNG-Engine," SAE Technical Paper
2006-01-3000, 2006, doi:10.4271/2006-01-3000.
6. Chiodi, M., Ferrari, A., Mack, O., Bargende, M., and Wichelhaus, D., “A Way
Towards Remarkable Reduction of Co2 Emissions in Motorsports: The CNG-
Engine,” SAE Technical Paper 2011-37-0006, 2011, doi: 10.4271/2011-37-0006.
7. Chiodi, M., Ferrari, A., Mack, O., Bargende, M., Wichelhaus, D., “Improvement
of a High-Performance CNG-Engine based on an innovative Virtual Development
Process,” SAE Technical Paper 2011-24-0140, 2011, doi: 10.4271/2011-24-0140.
8. Natural and bio Gas Vehicle Association Europe, (2013). Worldwide ngv shares
in total vehicle market (June 2013). Retrieved from The GVR website:
http://www.ngvaeurope.eu/worldwide-ngv-statistics
9. Mercedes-Benz introducing B200 Natural Gas Drive. (2012, September 6).
Greencarcongress. Retrieved from:
http://www.greencarcongress.com/2012/09/b200-20120906.html#more.
10. Cars. (n.d.). NGVA Europe. Retrieved , from http://www.ngvaeurope.eu/cars
11. Hillstrom, D., Chiara F., and Durand, J., “CNG Literature Review.” Center for
Automotive Research, The Ohio State University.
71
12. Baratta, M., Catania, A., Spessa, E., et al., “Multi-Dimensional Modeling of
Direct Natural-Gas Injection and Mixture Formation in a Stratified-Charge SI
Engine with Centrally Mounted Injector,” SAE Technical Paper 2008-01-0975,
2008, doi: 10.4271/2008-01-0975.
13. Andreassi, L., Facci, A., Ubertini, S., “Multidimensional Modelling of Gaseous
Injection in Modern Direct Injection Internal Combustion Engines: Analisys of
Different Fuel Injection Strategies,” SAE Technical Paper 2009-24-0030, 2009,
doi: 10.4271/2009-24-0030.
14. Douailler, B., Ravet, F., Delpech, V., et al., “Direct Injection of CNG on High
Compression Ratio Spark Ignition Engine: Numerical and Experimental
Investigation,” SAE Technical Paper 2011-01-0923, 2011, doi: 10.4271/2011-01-
0923.
15. Hu, C., Hou, S., “Investigations on Combustion Process of Low-pressure CNG
Compound Direct Injection Spark-ignited Engines,” SAE Technical Paper 2010-
32-0052, 2010, doi: 10.4271/2010-32-0052.
16. Kalam, M., Masjuki, H., Mahlia, T., et al., “Experimental Test of a New
Compressed Natural Gas Engine with Direct Injection,” SAE Technical Paper
2009-01-1967, 2009, doi: 10.4271/2009-01-1967.
17. Yadollahi, B., Boroomand, M., “The Effect of Piston Head Geometry on Natural
Gas Direct Injection and Mixture Formation in a SI Engine with Centrally
Mounted Single-Hole Injector,” SAE Technical Paper 2011-01-2448, 2011, doi:
10.4271/2011-01-2448.
18. Liu, Y., Yeom, J. K., & Chung, S. S. “An experimental study on the effects of
impingement-walls on the spray and combustion characteristics of SIDI CNG.”
Journal of Mechanical Science and Technology 26, no. 11 (2012): 2239-2246.
19. Sasaki, H., Sekiyama, S., Hashimoto, M., Nakashima, K., “Low-Emission
Combustion of a Pre-Chamber-Type Compression Ignition Natural Gas Engine,”
International Journal of Engine Research 8, no. 6 (2007): 465-476.
20. Liu, Y., Dong, Y., Yeom, J. K., & Chung, S. S. “An experimental investigation of
the engine operating limit and combustion characteristics of the RI-CNG engine.”
Journal of Mechanical Science and Technology 26, no. 11 (2012): 3673-3679.
21. Kono, S., Koga, H., Watanabe, S., “Research on Extended Expansion General-
Purpose Engine-Efficiency Enhancement by Natural Gas Operation,” SAE
Technical Paper 2010-32-0007 (2010), doi: 10.4271/2010-32-0007.
72
22. Zheng, J., Hu, E., Huang, Z., Ning, D., Wang, J., "Combustion and Emission
Characteristics of a Spray Guided Direct-injection Spark-ignition Engine Fueled
with Natural Gas-hydrogen Blends."International Journal of Hydrogen Energy
36, no. 17 (2011): 11155-1163.
23. Saanum, I., Bysveen, M., Tunestal, P., Johansson, B., “Lean Burn Versus
Stoichiometric Operation with EGR and 3-Way Catalyst of an Engine Fueled with
Natural Gas and Hydrogen Enriched Natural Gas.” SAE Technical Paper 2007-
01-0015 (2007), doi: 10.4271/2007-01-0015.
24. Kalam, M., Masjuki, H. “An Experimental Investigation of High Performance
Natural Gas Engine With Direct Injection,” Energy 36, (2011): 3563-3571.
25. Abianeh, O., Mirsalim, M., Ommi, F., “Combustion Development of a Bi-fuel
Engine,” International Journal of Automotive Technology 10, no. 1 (2009): 17-25.
26. Delpech, V., Obiols, J., Soleri, D., et al., “Towards an Innovative Combination of
Natural Gas and Liquid Fuel Injection in Spark Ignition Engines,” SAE Technical
Paper 2010-01-1513 (2010), doi: 10.4271/2010-01-1513.
27. Pipitone, E., Beccari, S., “Performances Improvement of a S.I. CNG Bi-fuel
Engine by Means of Double-fuel Injection,” SAE Technical Paper 2009-24-0058
(2009), doi: 10.4271/2009-24-0058.
28. Obiols, J., Soleri, D., Dioc, N., Moreau, M., “Potential of Concomitant Injection
of CNG and Gasoline on a 1.6L Gasoline Direct Injection Turbocharged Engine,”
SAE Technical Paper 2011-01-1995 (2011), doi: 10.4271/2011-01-1995.
29. Ghorbanian, J., Ahmadi, M., “Experimental Thermal Analysis of Cylinder Block
and Head of a Bi-fuel Turbocharged Engine,” Meccanica 47, (2012): 1987-2004.
30. Papagiannakis, R.G., Kotsiopoulos, P.N., Zannis, T.C., Yfantis, E.A., et al.,
“Theoretical Study of the Effects of Engine Parameters on Performance and
Emissions of a Pilot Ignited Natural Gas Diesel Engine,” Energy 35, (2010):
1129-1138.
31. Lim, O., Iida, N., Cho, G., Narankhuu, J., “The Research About Engine
Optimization and Emission Characteristic of Dual Fuel Engine Fueled with
Natural Gas and Diesel,” SAE Technical Paper 2012-32-0008 (2012), doi:
10.4271/2012-32-0008.
32. Qi, Y., Srinivasan, K., Krishnan, S., Yang, H., Midkiff, K., “Effect of Hot
Exhaust Gas Recirculation on the Performance and Emissions of an Advanced
Injection Low Pilot-ignited Natural Gas Engine,” Inetrnational Journal of Engine
Research 8, no. 3 (2007): 289.
73
33. Wannatong, K., Akarapanyavit, N., Siengsanorh, S., Chanchaona, S.,
“Combustion and Knock Characteristics of Natural Gas Diesel Dual Fuel
Engine,” SAE Technical Paper 2007-01-2047 (2007), doi: 10.4271/2007-01-2047.
34. Ohm, I., Park, C., “Effect of Fuel Stratification on Initial Flame Development:
Part 3 – High Swirl Condition,” International Journal of Automotive Technology
12, no. 5 (2011): 653-660.
35. Feist, M., Landau, M., Harte, E., “The Effect of Fuel Composition on
Performance and Emissions of a Variety of Natural Gas Engines,” SAE Technical
Paper 2010-01-1476 (2010), doi: 10.4271/2010-01-2047.
36. Bozza, F., Fontanesi, S., Gimelli, A., Severi, E., Siano, D., “Numerical and
Experimental Investigation of Fuel Effects on Knock Occurrence and Combustion
Noise in a 2-Stroke Engine,” SAE Technical Paper 2012-01-0827 (2012), doi:
10.4271/2012-01-0827.
37. Sachdeva, A., Mansuri, S., “Reduction of Pulsation Noise in CNG Vehicles,” SAE
Technical Paper 2013-26-0098 (2013), doi: 10.4271/2013-26-0098.
38. Rink, M., Eigenberger, G., Nieken, U., Tuttlies, U., “Optimization of a Heat-
integrated Exhaust Catalyst for CNG Engines,” Catalysis Today 188, (2012): 113-
120.
39. Zhang, C., Xie, Y., Wang, F., Ma, Z., Qi, D., Qiu, Z., “Emission Comparison of
Light-duty In-use Flexible-fuel Vehicles Fuelled with Gasoline and Compressed
Natural Gas Based on the ECE 15 Driving Cycle,” Journal of Automobile
Engineering 225, (2011): 90.
40. Gamma Technologies. "GT Power Engine Simulation Software." .
https://www.gtisoft.com/applications/a_Engine_Performance.php (accessed July
1, 2014).
41. Heywood, John B.. Internal combustion engine fundamentals. New York:
McGraw-Hill, 1988.
42. Amand, Lars-Erik. "The Theory Behind FTIR analysis." Lecture, from
Department of Energy Conversion Chalmers University of Technology, Göteborg,
Sweden.
43. Stas, M., “An Universally Applicable Thermodynamic Method for T.D.C.
Determination,” SAE Technical Paper 2000-01-0561 (2000), doi: 10.4271/2000-
01-0561.
74
44. "40 CFR 600.113-93 - Fuel Economy Calculations." LII / Legal Information
Institute.
45. Rink, M., Eigenberger, G., Nieken, U., Tuttlies, U., “Optimization of a Heat-
Integrated Exhaust Catalyst for CNG Engines,” Catalysis Today 188, (2012): 113-
120.
46. Mukherjee, T. (2014), “One-Dimensional Air System Modeling of Advanced
Technology Compressed Natural Gas Engine,” MS Thesis, The Ohio State
University.
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Appendix A: Instrumentation
Temperature Sensors:
All: K-type thermocouples from Temprel Inc.
Pressure Sensors:
Intake Pressure: Omega PX209 Series 030A5V
o 0-30 PSI absolute pressure sensor
Exhaust Pressure: Kistler 4045A5v200s Piezo-resistive pressure transducer
o Water cooled 0-5 bar absolute pressure sensor
In-cylinder pressure: AVL GH13Z-24 Piezo-electric pressure transducer
Wideband O2 Sensor:
Bosch Wideband O2 Sensor : 0 256 007 151
Intake Mass Air Flow
Laminar Flow Element: Meriam Instruments Model: 50MH10-5
o Flow: 270.33 CFM at 8 in H2O @ 70 F and 29.92” Hg. Abs.
Emissions:
MKS Instruments FTIR: Model #: 2030D-28229
Encoder Disc:
Custom manufactured disc with 180 teeth, and one extra deep groove for 1/rev
Encoder Photo-interruptors:
TT Electronics Photologic Slotted Optical Switch “Wide Gap” Series
TT Electronics Photologic Slotted Optical Switch OPB916 Series
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Appendix B: Labview Code Overview
Efficiency was paramount when designing the labview data acquisition code. One
of the drawbacks of the system was the limitation of the high speed data acquisition
card’s recording rate of 250 kHz. This 250 kHz was the translation rate of the one analog
to digital converter on the card. The allocation of only one ADC meant that all signals
coming in would be multiplexed, so the recording rate of all signals added together must
be less than 250 kHz. Three signals were being recorded so the recording rate of each
signal was set at 80 kHz which totals to 240 kHz when multiplexed, which is just beneath
the maximum capacity of the card. Multiplexing was not a concern for data accuracy
because the three signals were: in-cylinder pressure, exhaust pressure, and spark voltage.
The phase shift that might occur due to the sampling of one signal after another was not a
major concern as there was only one pressure signal. The accuracy of the exhaust
pressure and spark voltage was acceptable to be phase shifted by up to ~1/3 of a degree
as they were not imperative for placing top dead center, or for other calculations that
required the crank angle position to be very finely resolved.
The Labview program functions by breaking up the experimental setup into data
acquisition tasks. For this program, the tasks were as follows: high speed data analog
input task, 1/rev encoder pulse counter task, two degree encoder pulse counter task, low
speed analog input task (thermocouples), and low speed USB analog input task. In order
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to synchronize the high speed task and the counter input tasks, these three channels were
start triggered by the passing of a 1/rev pulse. The low speed tasks were synchronized
utilizing Labview’s sequence blocks such that they did not start until right after the high
speed tasks were triggered, pending the CPU getting around to it.
The Labview code was kept rather simple to lower CPU usage. After all of the
signals were triggered, the high speed tasks would cycle through a while loop for five
engine cycles buffering the data in binary form on the computer RAM. After five engine
cycles, the buffered data would be dumped to the hard drive through a TDMS save file.
The TDMS streaming ability with Labview is a very efficient method of storing data at
high speed as it minimizes the manipulations that must be made before the data is written.
The low speed data is buffered throughout the experiment and dumped to the hard drive
after the experiment had completed. No calculations were performed on the data in the
Labview setup in order to improve CPU efficiency.
Some caveats associated with the system were the allocation of direct memory
access (DMA) channels from the National Instruments PCI cards in order to not over-run
the CPU. The high speed signals had DMA, while the low speed signals (intake pressure,
LFE differential pressure, and wideband O2 sensor) were funneled through a USB DAQ
using USB polling at 10 Hz and the low speed thermocouple signals were funneled
through a PCI card with CPU polling at 10 Hz. With the system set up in this manner, the
CPU usage never went over 40-50%, allowing the experiments to be performed as
intended.