mobility of the future: hybrid and electric...
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MOBILITY OF THE FUTURE: HYBRID AND ELECTRIC VEHICLESMOBILITY OF THE FUTURE: HYBRID AND ELECTRIC VEHICLESMOBILITY OF THE FUTURE: HYBRID AND ELECTRIC VEHICLESMOBILITY OF THE FUTURE: HYBRID AND ELECTRIC VEHICLES
Ryan Ahmed, Ph.D., P.Eng., SCPM
Adjunct Professor and Post-doctoral Research Fellow
Center For Mechatronics and Hybrid Technologies (CMHT)
McMaster Automotive Resource Center (MARC), ON Canada
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 1
Education
• Masters of Business Administration (MBA) Candidate (2015-2017)DeGroote School of Business, ON Canada
• Ph.D. in Mechanical/Mechatronics Engineering (2014)McMaster University, ON Canada – Battery Management and Control
• M.A.Sc. in Mechanical Engineering (2011)McMaster University, ON Canada – Engine Management and Fault Detection
• B.Sc. in Mechatronics Engineering (2007)Ain Shams University – Mechatronics Engineering
Work Experience
• Adjunct Professor, McMaster University, ON Canada (2016 – present)
• Senior Systems Engineering Lead, Samsung SDI America, USA (2015 – 2016)
• Senior Technical Specialist, Fiat Chrysler Automobiles (FCA), ON Canada (2014 – 2015)
• R&D engineer, Ford Powertrain R&D Center, ON Canada (2011 – 2014)
• Teacher Assistant, Ain Shams University (2007 – 2009)
Research Seminar: Mobility of the Future 2
SPEAKER BACKGROUNDSPEAKER BACKGROUNDSPEAKER BACKGROUNDSPEAKER BACKGROUND
Ain Shams - 24/12/2016
• Why Electric Vehicles?
• Paradigm shift in transportation
• Hybrid Vehicles Configurations
• Mechatronics Engineering in the Automotive Industry
• Case Study: Battery Management Systems (BMS)
oBattery Modeling
oBattery Aging
o State of Charge Estimation
o State of Health Estimation
• Alternative Technologies
AGENDAAGENDAAGENDAAGENDA
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 3
WHY?
Sustain-ability
Well-to-Wheel
Efficiency
Environ-mental Impact
Energy Recovering
Tank to WheelWell to Tank
~ 20%
~ 80%
~ 83%
~ 30%
~ 17%
~ 24%
WHY ELECTRIC VEHICLES?WHY ELECTRIC VEHICLES?WHY ELECTRIC VEHICLES?WHY ELECTRIC VEHICLES?
4http://nextbigfuture.com/2009/04/direct-
conversion-of-nuclear-power-to.html
~ 50-60%
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future
HYBRID ELECTRIC VEHICLESHYBRID ELECTRIC VEHICLESHYBRID ELECTRIC VEHICLESHYBRID ELECTRIC VEHICLES
PARADIGM SHIFT IN TRANSPORTATIONPARADIGM SHIFT IN TRANSPORTATIONPARADIGM SHIFT IN TRANSPORTATIONPARADIGM SHIFT IN TRANSPORTATION
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 5
Not sustainable –
Transportation 1.0
Sustainable –
Transportation 2.0
More Efficient, cleaner, greener
ICE
•Not efficient – Efficiency of approximately 20%
More Electric Vehicles
•Electrification less than 20% – Non-propulsion Electric components
•Electrically assisted power steering, electrically driven air-conditioning, electromechanical valve control
Hybrid Electric Vehicles
•Micro hybrids, mild hybrids, power (full) hybrids, and energy hybrids
•hybridization factor is ratio between its peak electrical power and peak total electrical and mechanical power
Plug-In Electric Vehicles
•Dual fuel vehicles – Most Promising
All Electric Vehicles
•Electrification level 100% – Ultimate form
• HEVs market will form 8% of the global passenger vehicle segment by 2020
• PHEVs will become one of the main forms of transportation in Canada and across the globe by 2030
• In five years, advanced electric-drive vehicles will exceed 15% of the global new vehicle market
• This means production of at least 7.5 million units per year
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 6
PARADIGM SHIFT IN TRANSPORTATIONPARADIGM SHIFT IN TRANSPORTATIONPARADIGM SHIFT IN TRANSPORTATIONPARADIGM SHIFT IN TRANSPORTATION
Electric Transportation By Ali Emadi
HEVs POWERTRAIN CONFIGURATIONS: HEVs POWERTRAIN CONFIGURATIONS: HEVs POWERTRAIN CONFIGURATIONS: HEVs POWERTRAIN CONFIGURATIONS: SERIES HYBRID EVSERIES HYBRID EVSERIES HYBRID EVSERIES HYBRID EV
Electric Transportation By Ali Emadi
• Engine is mechanically decoupled from the wheels
• Power is delivered by the electric motor while the engine drives an electric generator
• Generator charges the batteries which drives the electric motor
HYBRID HYBRID HYBRID HYBRID –––– SERIES CONFIGURATIONSERIES CONFIGURATIONSERIES CONFIGURATIONSERIES CONFIGURATIONPLUGPLUGPLUGPLUG----ININININ HYBRID HYBRID HYBRID HYBRID –––– SERIES CONFIGURATIONSERIES CONFIGURATIONSERIES CONFIGURATIONSERIES CONFIGURATION
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 7
HEVs POWERTRAIN CONFIGURATIONS: HEVs POWERTRAIN CONFIGURATIONS: HEVs POWERTRAIN CONFIGURATIONS: HEVs POWERTRAIN CONFIGURATIONS: PARALLEL HYBRID EVPARALLEL HYBRID EVPARALLEL HYBRID EVPARALLEL HYBRID EV
• The engine and electric motor are coupled to drive the vehicle
PLUGPLUGPLUGPLUG----ININININ HYBRID HYBRID HYBRID HYBRID –––– PARALLEL CONFIGURATIONPARALLEL CONFIGURATIONPARALLEL CONFIGURATIONPARALLEL CONFIGURATIONHYBRID HYBRID HYBRID HYBRID –––– PARALLEL CONFIGURATIONPARALLEL CONFIGURATIONPARALLEL CONFIGURATIONPARALLEL CONFIGURATION
Electric Transportation By Ali EmadiAin Shams - 24/12/2016 Research Seminar: Mobility of the Future 8
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HEVs POWERTRAIN CONFIGURATIONS: HEVs POWERTRAIN CONFIGURATIONS: HEVs POWERTRAIN CONFIGURATIONS: HEVs POWERTRAIN CONFIGURATIONS: BATTERY ELECTRIC VEHICLES (BEV)BATTERY ELECTRIC VEHICLES (BEV)BATTERY ELECTRIC VEHICLES (BEV)BATTERY ELECTRIC VEHICLES (BEV)
• No engine exists, pure electrified vehicles
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MECHATRONICS ENGINEERING: MECHATRONICS ENGINEERING: MECHATRONICS ENGINEERING: MECHATRONICS ENGINEERING: AUTOMOTIVE INDUSTRYAUTOMOTIVE INDUSTRYAUTOMOTIVE INDUSTRYAUTOMOTIVE INDUSTRY
Systems/
Mechatronics
Electrical Engineer
Mechanical Engineer
Software Engineer
Hardware Engineer
Validation Engineer
Chemical Engineer
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MECHATRONICS ENGINEERING: MECHATRONICS ENGINEERING: MECHATRONICS ENGINEERING: MECHATRONICS ENGINEERING: JOB MARKET IN U.S.JOB MARKET IN U.S.JOB MARKET IN U.S.JOB MARKET IN U.S.
Salary Range: 65K-110K USD # Openings: 168,984
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 12
MECHATRONICS ENGINEERING: MECHATRONICS ENGINEERING: MECHATRONICS ENGINEERING: MECHATRONICS ENGINEERING: INTERNATIONAL COUNCIL ON SYSTEMS INTERNATIONAL COUNCIL ON SYSTEMS INTERNATIONAL COUNCIL ON SYSTEMS INTERNATIONAL COUNCIL ON SYSTEMS ENGINEERING (INCOSE)ENGINEERING (INCOSE)ENGINEERING (INCOSE)ENGINEERING (INCOSE)
http://www.incose.org/
• Represents a structured process for guiding project development from its conception through design, implementation, operations.
• This approach increases the probability of producing a successful outcome and minimizes the project budget and schedule.
Research Seminar: Mobility of the Future
V PROCESS MODEL:V PROCESS MODEL:V PROCESS MODEL:V PROCESS MODEL:
13Ain Shams - 24/12/2016
CASE CASE CASE CASE STUDY: BATTERY STUDY: BATTERY STUDY: BATTERY STUDY: BATTERY MANAGEMENT MANAGEMENT MANAGEMENT MANAGEMENT SYSTEMSYSTEMSYSTEMSYSTEM
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Battery cell:
• Smallest packaged form of a battery
• Voltage ranges from 1 - 6V
Module
• Modules are formed by connecting cells in series and parallel configurations
• Cells are contained in metal case to protect them
Pack
• Assembled by connecting a group of battery modules together in series or parallel
http://blog.cafefoundation.org/less-expensive-batteries-
may-lead-to-more-homebuilt-electric-airplanes/
http://www.eco-aesc-lb.com/en/product/liion_ev/
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CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTURE
Cell
• Cells should provide long life span, strong heat dissipation and high energy density
• Lithium Manganese Oxide cells
http://www.eco-aesc-
lb.com/en/product/liion_ev/Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 16
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTURE
NISSAN LEAF BATTERY PACKNISSAN LEAF BATTERY PACKNISSAN LEAF BATTERY PACKNISSAN LEAF BATTERY PACK
Module
• The EV modules adopted in the Nissan Leaf and other vehicles feature a 2-series, 2-parallel formation
• The case functions are used to protect the cells from vibration
• It improves the pack design flexibility because of its simple and compact shape
http://www.eco-aesc-lb.com/en/product/liion_ev/Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 17
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTURE
Pack
• The pack is formed by installing a battery management system, sensors, and housing
• For Nissan Leaf, battery pack is formed by connecting 48 modules in series with 360V, capacity of 24kWh
• The pack can be designed with a shape suitable to be installed under the vehicle floor
http://www.eco-aesc-lb.com/en/product/liion_ev/Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 18
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTURE
• Voltage, current and temperature are monitored by sensors from each module
• Data is sent from the battery controller (BMS) to the vehicle control unit via CAN
• During maintenance, the circuit is interrupted by operating the SDSW (Service Disconnect Switch)
http://www.eco-aesc-lb.com/en/product/liion_ev/
http://www.greencarreports.com/image/100409382_lithiu
m-ion-battery-pack-for-2014-chevrolet-spark-ev-electric-carAin Shams - 24/12/2016 Research Seminar: Mobility of the Future 19
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTUREBATTERY SYSTEM STRUCTURE
• A Battery Management System (BMS) is employed to actively monitor and protect the cells in real-time.
• The BMS accurately monitor cell voltage, current, temperatures, impedance and other variables of the cells.
• The BMS performs several functions:
o Cell Monitoring
o Supervising Battery Pack Behavior
o Cell Balancing
o Fuel Gauging (SOC Estimation)
Research Seminar: Mobility of the Future 20
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY BATTERY BATTERY BATTERY
PACK MONITORINGPACK MONITORINGPACK MONITORINGPACK MONITORING
http://www.pues.co.jp/en/products-en/390.html
Ain Shams - 24/12/2016
Research Seminar: Mobility of the Future 21https://www.dspace.com/shared/data/pdf/2011/Elektr
onik_Automotive_July_2011_01_110811_E_ebook.pdf
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CELL CELL CELL CELL SUPERVISORY SUPERVISORY SUPERVISORY SUPERVISORY
CIRCUIT (CSC)CIRCUIT (CSC)CIRCUIT (CSC)CIRCUIT (CSC)
• The BMS consists of the ECU (micro-controller) or the brain along with the CSC (Cell Supervisory circuit) (Cell Module)
• The CSC is attached to each module to measure individual cell voltages.
• The CSC has balancing resistors used to dissipate current into them to maintain all cells at the same state of charge.
• The CSC measurements are sent to the ECU (brain) for decision making.
http://ams.com/eng/Products/Battery-
Management/Cell-Supervision-Circuits
Ain Shams - 24/12/2016
• V,I,T monitoring
• SOC estimation
• Power limits
• Cell balancing
• State of health
• Remaining useful life
• Communication
• Data recording/reporting
• Prevent over/undercharge
• Short circuits
• Temp. limits
Safety/
Protection Interfacing
Performance Monitoring/
Management
Diagnostics
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CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BMS KEY FUNCTIONSBMS KEY FUNCTIONSBMS KEY FUNCTIONSBMS KEY FUNCTIONS
• Equivalent to fuel gauge in conventional vehicles
• Represents battery current capacity as a percentage of maximum capacity
• SOC is calculated using coulomb counting/current integration
Battery SOC = 100% Battery SOC = 50% Battery SOC = 0%
“However, while there exist sensors to accurately measure a gasoline level in a tank, there is no sensor available to
measure SOC. Instead, SOC must be estimated from physical measurements by some algorithm.”
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 23
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BMS KEY BMS KEY BMS KEY BMS KEY FUNCTIONS: FUNCTIONS: FUNCTIONS: FUNCTIONS: BATTERY STATE OF CHARGE BATTERY STATE OF CHARGE BATTERY STATE OF CHARGE BATTERY STATE OF CHARGE (%) ESTIMATION (%) ESTIMATION (%) ESTIMATION (%) ESTIMATION
Battery Life Time
Fresh Battery
Life Fraction (LF)= 0
Midlife Battery
LF = 0.5
Aged Battery
LF = 1
• Battery SOC and SOH?
• EVs are Relatively new, some time is required to assess the estimators in real-world operating conditions
http://www.mynissanleaf.com/
Model 1 Model 2 Model 3
Capacity = 100% Capacity = 90% Capacity = 80%
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 24
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BMS KEY BMS KEY BMS KEY BMS KEY FUNCTIONS: FUNCTIONS: FUNCTIONS: FUNCTIONS: STATE OF HEALTH ESTIMATION STATE OF HEALTH ESTIMATION STATE OF HEALTH ESTIMATION STATE OF HEALTH ESTIMATION
• After many charging/discharging cycles, cells may become out of balance
• Cells vary due to manufacturing differences, columbic efficiencies, and capacities
• Cells may limit the discharge ability of the pack if their SOC is much lower than remaining cells
http://www.eetimes.com/docum
ent.asp?doc_id=1272951Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 25
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BMS KEY BMS KEY BMS KEY BMS KEY FUNCTIONS: FUNCTIONS: FUNCTIONS: FUNCTIONS: CELL CELL CELL CELL BALANCINGBALANCINGBALANCINGBALANCING
Error ~ 0
High Fidelity Model
Estim-ation
Strategy
SOC
SOH
Battery
Model
SOC/SOH
Input Current Output Voltage
Estimator
SLIDE 26
50 100 150 200 250-12
-10
-8
-6
-4
-2
0
2
4
Curr
ent [A
]
Time [min]
Input Current
50 100 150 200 25030
40
50
60
70
80
90
SO
C [%
]
Time [s]
State of Charge
50 100 150 200 2503.5
3.6
3.7
3.8
3.9
4
4.1
4.2
4.3
Voltage [V
]
Time [s]
Output Voltage
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY BATTERY BATTERY BATTERY STATE OF STATE OF STATE OF STATE OF CHARGE CHARGE CHARGE CHARGE (%) ESTIMATION (%) ESTIMATION (%) ESTIMATION (%) ESTIMATION
Ryan Ahmed, Mohammed El Sayed, Saeid Habibi, and Jimi Tjong (2014): Reduced-Order
Electrochemical Model Parameters Identification and SOC Estimation for Healthy and
Aged Li-Ion Batteries. Part I: Parameterization Model Development for Healthy Batteries,
IEEE Journal of Emerging Technologies.
• Empirical models
• Account for hysteresis, polarization , ohmic loss
• No/minimal physical significance/SOH
Lumped-parameters models
• Simple, less parameters to tune
• Easy implementation/Computationally efficient
• No/minimal physical significance/SOH
Equivalent circuit models
• Model lithium diffusion
• Physical insight of battery SOH
• Large number of parameters
• Hard to obtain these parameters
• Computationally expensive (reduced form)
Electrochemical models
• Battery Models Overview
State Equation:
0 00 00 0 1
1 00 1 ∆ 0
, Output Equation: !"#$% & '& ( )
Battery Systems and Controls MECHENG
599, University of Michigan
Leve
l of D
eta
ils Incre
ase
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY MODELINGBATTERY MODELINGBATTERY MODELINGBATTERY MODELING
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 27
Battery Cells
• Mount temperature sensors on each cell
Cyclers/
Chambers
• Chambers: thermally stress cells (-20 to 70*• Independent parallel channels (Ex: 400 A, 20V)
Data Acquisition
• Software
• Test Procedure
Ryan Ahmed, Jimi Tjong, and Saeid Habibi, “Battery Aging Model
Development based on Behavioural and Equivalent Circuit-Based
Models”, Journal of Power Sources (2014)
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 28
CASE STUDY/BATTERY MANAGEMENT SYSTEMCASE STUDY/BATTERY MANAGEMENT SYSTEMCASE STUDY/BATTERY MANAGEMENT SYSTEMCASE STUDY/BATTERY MANAGEMENT SYSTEM: : : : EXPERIMENTAL SETUPEXPERIMENTAL SETUPEXPERIMENTAL SETUPEXPERIMENTAL SETUP
5 10 15 20
-150
-120
-90
-60
-30
0
30
60
90
120
150
Time (Mins)
Cu
rre
nt (A
mp
s)
Pack Current - UDDS
5 10 15 20
-15
-12
-9
-6
-3
0
3
6
9
12
15
Time (Mins)
Cu
rren
t (A
mps)
Cell Current - UDDS
0 5 10 15 20 25
0
10
20
30
40
50
60
70
80
90
100
Time (Mins)
Velo
city (
Kph)
Velocity Profile - UDDS
• Generate Velocity Profile to Cycler?
•Urban/Highway
•Velocity Profile
Driving Cycle
•Matlab/
•Simulink
•Pack level current
Electric Vehicle Model
•Cell-level scaling
•No cell balancing
Cycler
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 29
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: VELOCITY PROFILE TO VELOCITY PROFILE TO VELOCITY PROFILE TO VELOCITY PROFILE TO CYCLERCYCLERCYCLERCYCLER
Static Capacity
Test
SOC-OCV
C/25
Pulse Charge/
Discharge
Hybrid Pulse Power (HPPC)
Test
Resistance Test
Driving Cycles: UDDS, HWFET,
US06
Real-World Driving
Scenario (Aging Test)
Repeat Every 5% Capacity
Degradation
• Aging Model Development: Aging Study
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY AGING STUDYBATTERY AGING STUDYBATTERY AGING STUDYBATTERY AGING STUDY
Ryan Ahmed, Jimi Tjong, and Saeid Habibi, “Battery Aging Model
Development based on Behavioural and Equivalent Circuit-Based
Models”, Journal of Power Sources (2014)
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future SLIDE 30
0 20 40 60 80 100 120
0
20
40
60
80
100
120
Time (Mins)
Velo
city (
Kp
h)
UDDS+US06 - Home to Work
UDDS+US06 - Work to Home
UDDS - Home to City (Evening Errand)
UDDS - City to Home (Evening Errand)
Home To WorkUDDS+US06
Work To HomeUDDS+US06
Home To City(Evening Errand)
UDDS
City To Home(Evening Errand)
UDDS
• Aging Model Development: Real-World Driving Aging Test
Velocity Profile for One Week Day With Errands
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future SLIDE 31
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY AGING STUDYBATTERY AGING STUDYBATTERY AGING STUDYBATTERY AGING STUDY
0 50 100 150 200 250 300 350 400
-60
-50
-40
-30
-20
-10
0
10
20
Time [min]
Cu
rre
nt
[A]
Battery Current for One Aging Week
Cell Applied Current
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future SLIDE 32
0 50 100 150 200 250 300 350 400
3
3.2
3.4
3.6
3.8
4
4.2
4.4
Time [min]
Vo
lta
ge
[V
]
Battery Voltage for One Aging Week
Cell Voltage
0 50 100 150 200 250 300 350 400
40
50
60
70
80
90
Time [min]
SO
C [
%]
Battery SOC for One Aging Week
Cell SOC
• Aging Model Development: Experimental Data Sample
Experiments Running on 24/7 basis for ~ 13 months
0 5 10 15 20 25 30 35 40
3
3.2
3.4
3.6
3.8
4
4.2
4.4
Time [min]
Vo
lta
ge
[V
]
Battery Voltage for one Day with Errand (Tuesday)
Cell Voltage
0 5 10 15 20 25 30 35 40
40
50
60
70
80
90
Time [min]
SO
C [
%]
Battery SOC for one Day with Errand (Tuesday)
Cell SOC
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY AGING STUDYBATTERY AGING STUDYBATTERY AGING STUDYBATTERY AGING STUDY
• Solid and electrolyte potentials
• Linear lithium diffusion in electrolyte:
• Spherical solid diffusion:
• Terminal Voltage Calculation
++, - .. ++, ∅ ++, -0 .. ++, ln 3 45++, 6 .. ++, ∅ 45
+7 3 + ++, 8 .. +3 +, 1 9 45
+3+ ++: 8 +3+:
;5 4<=>,? ∝$ ( >,?∝B ( C ∅ ∅ D3
∅ , E ∅ , 0 (.F
Model Assumptions:
• No aging or capacity fade has been accounted for
• Model parameters assumed to be held constant
• Significant errors at high C-rates (rate capacity/recovery effect)
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 33
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY MODELINGBATTERY MODELINGBATTERY MODELINGBATTERY MODELING
ELECTROCHEMICAL BATTERY MODELELECTROCHEMICAL BATTERY MODELELECTROCHEMICAL BATTERY MODELELECTROCHEMICAL BATTERY MODEL
• SOC Module: Reduced-Order Electrochemical Model
• Terminal Voltage Module
H # I∅J .H ∅J .# DL 3 ,H D# 3 ,# (.F
'& 100 ∗ NO,PQRSNO,TQU,PVWPX%WPZXX%VWPX% )
3,HQRS 3[ \]]^_3\`3>:\`?:3]>a\]^_> ∑ :c4e∆:3fgVh43e ( ∆: j
0 5 10 15 20
0.0328
0.0329
0.033
0.0331
0.0332
0.0333
0.0334
0.0335
Time [min]
Co
nce
ntr
atio
n [
mo
l/cm
3]
Cathode Concentrations Across Shells
Shell 1
Shell 2
Shell 3
Shell 4
Shell 5
Shell 6
Shell 7
Shell 8
Shell 9
Shell 10
3 3.5 4 4.5
0.0332
0.0333
0.0334
9.8 10 10.2 10.4
0.0331
H ∅,H ∅ .H DH3 ,H# ∅,# ∅ .# D#3 ,#
3 ,# 3,"$k,# l#9% 3 ,H lH9%3,"$k,HlH99% lH9%3,"$k,H l#99% l#9%m
0 5 10 15 20
3.1
3.15
3.2
3.25
3.3
3.35
Time [min]
Vo
lta
ge
[V
]
Voltage for UDDS Driving Cycle
LiFePO4 Measured Voltage
0 5 10 15 20
42
44
46
48
50
SO
C [
%]
Time [min]
0 5 10 15 20
-2
0
2
4
6
Cu
rre
nt
[A]
Time [min]
Current and SOC for UDDS Cycle
LiFePO4 SOC
LiFePO4 Input Current
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY MODELINGBATTERY MODELINGBATTERY MODELINGBATTERY MODELING
ELECTROCHEMICAL BATTERY MODELELECTROCHEMICAL BATTERY MODELELECTROCHEMICAL BATTERY MODELELECTROCHEMICAL BATTERY MODEL
Ryan Ahmed, Mohammed El Sayed, Saeid Habibi, and Jimi Tjong (2014): Reduced-Order
Electrochemical Model Parameters Identification and SOC Estimation for Healthy and
Aged Li-Ion Batteries. Part I: Parameterization Model Development for Healthy Batteries,
IEEE Journal of Emerging Technologies.
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future SLIDE 34
0 5 10 15 20
3.1
3.15
3.2
3.25
3.3
3.35
Time [min]
Vo
lta
ge
[V
]
Voltage for UDDS Driving Cycle
LiFePO4 Measured Voltage
0 5 10 15 20
42
44
46
48
50
SO
C [
%]
Time [min]
0 5 10 15 20
-2
0
2
4
6
Cu
rre
nt
[A]
Time [min]
Current and SOC for UDDS Cycle
LiFePO4 SOC
LiFePO4 Input Current
0 5 10 15 20
3.17
3.2
3.23
3.26
3.29
3.32
Time [min]
Vo
lta
ge
[V
]
Experimental Terminal Voltage Vs. Model Output
Measured Terminal Voltage
Model Terminal Voltage (Optimized)
15.4 15.6 15.8
3.29
0 5 10 15 20
43
43.5
44
44.5
45
45.5
46
46.5
47
47.5
48
48.5
49
49.5
50
Time [min]
SO
C [
%]
Actual Vs. Model SOC
Actual SOC
Model SOC
3 4 5 6
48
48.5
16 17 18
45
45.5
• Model Parameters Fitting
• Actual SOC (Arbin Cycler) vs. Model SOC for UDDS Cycle
• Model Terminal voltage vs. Measured voltage
no RMSE (UDDS) = 0.22 mV SOC RMSE (UDDS) = 0.0547 %
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY MODELINGBATTERY MODELINGBATTERY MODELINGBATTERY MODELING
ELECTROCHEMICAL BATTERY MODELELECTROCHEMICAL BATTERY MODELELECTROCHEMICAL BATTERY MODELELECTROCHEMICAL BATTERY MODEL
Ryan Ahmed, Mohammed El Sayed, Saeid Habibi, and Jimi Tjong (2014): Reduced-Order
Electrochemical Model Parameters Identification and SOC Estimation for Healthy and
Aged Li-Ion Batteries. Part II: Aged Battery Model and State of Charge Estimation, IEEE
Journal of Emerging Technologies, Special Issue on Transportation and Electrification.
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future SLIDE 35
BV Current
Calculation
Spherical
particle sub-
model
Solid
Electrolyte-[
Solid
Electrolyte-
SOC
Current (I) BV (I) p[
SOC
Experimental
Battery
Experimental
Battery
Current (I)
-
[
Model States Update
q >rstZ|s v >rs|s ∘ x >rstZ|sy >rstZ|s V
>rstZ|s
;5 4<=>,? ∝$ ( >,?∝B ( C
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 36
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY SOC ESTIMATIONBATTERY SOC ESTIMATIONBATTERY SOC ESTIMATIONBATTERY SOC ESTIMATION
Ryan Ahmed, Ienkaran Arasaratnam, Mohamed El-Sayed, Jimi Tjong, and Saeid Habibi, “Online and Offline Parameters Identification
and SOC Estimation for Healthy and Aged Electric Vehicle Batteries Based on Equivalent Circuit Models”, Journal of Power Sources, 2015.
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 37
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY SOC ESTIMATIONBATTERY SOC ESTIMATIONBATTERY SOC ESTIMATIONBATTERY SOC ESTIMATION
0 200 400 600 800 1000 1200 140080
82
84
86
88
90
Time
SO
C
SOC
SOCEstimated
0 200 400 600 800 1000 1200 1400-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
Term
inalV
olt
ag
e
TerminalVoltage
TerminalVoltageEstimated
• Battery models change over the vehicle lifetime
• Estimators are used to predict the battery state of health
Battery
Model
SOC/SOH
Error ~ 0
Input Current Output Voltage
Estimator
0 50 100 150 200 250
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Time [min]
SO
C [
%]
Healthy Vs. Aged SOC - Driving Schedule A1
Battery SOC - Fresh [Capacity = 100%]
Battery SOC - Aged [Capacity = 80%]
15 20 25
80
85
90
190 195 200
35
40
45
50
0 50 100 150 200 250
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4
4.1
4.2
4.3
Time [min]
Vo
lts [
V]
Healthy Vs. Aged Terminal Voltage - Driving Schedule A1
Terminal Voltage - Fresh [Capacity = 100%]
Terminal Voltage - Aged [Capacity = 80%]
110 115 120
3.7
3.8
265 270 275 280
3.5
3.6
3.7
50 100 150 200 250-12
-10
-8
-6
-4
-2
0
2
4
Curr
ent [A
]
Time [min]
Input Current
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future 38
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY BATTERY BATTERY BATTERY STATE OF STATE OF STATE OF STATE OF HEATLH ESTIMATION HEATLH ESTIMATION HEATLH ESTIMATION HEATLH ESTIMATION
High Fidelity Model
Estim-ation
Strategy
SOC
SOH
Ryan Ahmed, Mohammed El Sayed, Saeid Habibi, and Jimi Tjong (2014): Reduced-Order
Electrochemical Model Parameters Identification and SOC Estimation for Healthy and
Aged Li-Ion Batteries. Part I: Parameterization Model Development for Healthy Batteries,
IEEE Journal of Emerging Technologies.
• Aging Model Development: Do we need model update?
• Actual SOC from Aged Battery vs. Model SOC
• RMSE variations for healthy and aged batteries
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future
0 5 10 15 20
3.17
3.2
3.23
3.26
3.29
3.32
Time [min]
Vo
lta
ge
[V
]Experimental Terminal Voltage for Aged Battery Vs. Model Output
Measured Terminal Voltage for Aged Battery (80% Capacity)
Model Terminal Voltage (Optimized)
12.612.8 13 13.213.4
3.23
3.26
3.29
0 1 2 3 4 5 6 7 8 9
x 10-3
Fresh Battery (100%)
Aged Battery (80%)
Terminal Voltage RMSE
Volts [V]
0 0.5 1 1.5
Fresh Battery (100%)
Aged Battery (80%)
SOC RMSE
SOC [%]0 5 10 15 20
41
41.5
42
42.5
43
43.5
44
44.5
45
45.5
46
46.5
47
47.5
48
48.5
49
49.5
50
Time [min]
SO
C [
%]
Actual (Aged) Vs. Model SOC
Actual SOC (Aged)
Model SOC
5 6 7 8
47
47.5
48
44%
41.5%
SLIDE 39
z. |%~. ~%
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY AGING STUDYBATTERY AGING STUDYBATTERY AGING STUDYBATTERY AGING STUDY
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future
0 1 2 3 4 5 6 7 8
x 10-9
FreshAged (EOL)
Solid phase diffusion coefficient (Positive) (Ds,p)
cm2/sec
0 0.002 0.004 0.006 0.008 0.01 0.012
FreshAged (EOL)
Solid-Electrolyte-Interface Resistance
Ohms Ω
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
FreshAged (EOL)
Electrode Aging Factor τ
• Aging Model Development
• Model increase in the electrode resistance to accept E
• Track changes in ( , 8, & '&
• Introducing: electrode aging factor (), electrode effective volume
SLIDE 40
CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: CASE STUDY/BATTERY MANAGEMENT SYSTEM: BATTERY AGING STUDYBATTERY AGING STUDYBATTERY AGING STUDYBATTERY AGING STUDY
• The Ragone plot compares the performance of various electrochemical devices
• Ultra capacitors provide a high power density but their storage capacity is very limited thus makes them suitable for capturing regenerative braking energy in EV applications
• Fuel Cells have very high energy density but low power density limiting their application in EV applications
• Lithium batteries are in between therefore provide a compromise between the two
• A combination of more than one device can be beneficial
Research Seminar: Mobility of the Future
OTHER TECHNOLOGIES: OTHER TECHNOLOGIES: OTHER TECHNOLOGIES: OTHER TECHNOLOGIES: RAGONE PLOTRAGONE PLOTRAGONE PLOTRAGONE PLOT
41http://www.mpoweruk.com/performance.htmAin Shams - 24/12/2016
• A supercapacitors (ultracapacitors) are high-capacity electrochemical capacitor with capacitance values much higher than other capacitors.
• Supercapacitors bridges the gap between electrolytic capacitors and rechargeable batteries.
• They are capable of storing 10 - 100 times more energy per unit volume or mass than electrolytic capacitors.
• They have very high power capability, i.e.: can accept and deliver charge much faster than batteries.
• They have a high cycle life, can withstand great number of charge and discharge cycles than rechargeable batteries.
• However, they have low energy density, they require 10 times more space than conventional batteries for a given charge.
• Supercapacitors are used in regenerative braking, i.e.: applications with many rapid charge/discharge cycles.
Research Seminar: Mobility of the Future 42
OTHER TECHNOLOGIES: OTHER TECHNOLOGIES: OTHER TECHNOLOGIES: OTHER TECHNOLOGIES: ULTRA CAPACITORSULTRA CAPACITORSULTRA CAPACITORSULTRA CAPACITORS
https://en.wikipedia.org/wiki/Supercapacitor
“Supercaps can charge in seconds, without
capacity degradation like rechargeable
batteries. They can endure virtually unlimited charge cycles”
http://www.mouser.com/application
s/supercapacitors-hero-automotive/
Ain Shams - 24/12/2016
• A fuel cell converts chemical energy from a fuel into electricity on the fly.
• Fuel cells are different from batteries in requiring a continuous source of fuel and oxygen (or air) to sustain the chemical reaction.
• Check this out: https://www.youtube.com/watch?v=08ZH7vwzzEg
Research Seminar: Mobility of the Future 43
OTHER TECHNOLOGIES: OTHER TECHNOLOGIES: OTHER TECHNOLOGIES: OTHER TECHNOLOGIES: FUEL CELLSFUEL CELLSFUEL CELLSFUEL CELLS
https://en.wikipedia.org/wiki/Fuel_cellAin Shams - 24/12/2016
1. Ryan Ahmed, Mohammed El Sayed, Saeid Habibi, and Jimi Tjong (2014): Reduced-Order Electrochemical Model Parameters Identification and SOC Estimation for Healthy and Aged Li-Ion Batteries. Part I: Parameterization Model Development for Healthy Batteries, IEEE Journal of Emerging Technologies, Special Issue on Transportation and Electrification. (Published).
2. Ryan Ahmed, Mohammed El Sayed, Saeid Habibi, and Jimi Tjong (2014): Reduced-Order Electrochemical Model Parameters Identification and SOC Estimation for Healthy and Aged Li-Ion Batteries. Part II: Aged Battery Model and State of Charge Estimation, IEEE Journal of Emerging Technologies, Special Issue on Transportation and Electrification. (Published).
3. Ienkaran Arasaratnam, Jimi Tjong, Ryan Ahmed and Saeid Habibi, (2013): Adaptive Temperature Monitoring for Battery Thermal Management, SAE World Congress, Detroit, Michigan. (Published).
4. I. Arasaratnam, Ahmed, Ryan, M. El-Sayed, S. Habibi, and J. Tjong, (2013): Li-Ion Battery SOC Estimation Using a Bayesian Tracker. SAE World Congress and Exhibition, Detroit, Michigan. (Published).
5. M. Farag, Ryan Ahmed, S.A. Gadsden, S. Habibi, and J. Tjong, (2012) A Comparative Study of Li-Ion Battery Models and Nonlinear Estimation Techniques. iTEC Conference, Dearborn, Michigan. (won the best paper award at the ITEC Conference). (Published).
6. Ryan Ahmed, Mohammed El Sayed, Saeid Habibi, and Jimi Tjong (2014): Literature Review of Battery Models, Aging Models, State of Charge, and State of Health Estimation Strategies, Journal of Power Sources. (Submitted and under review).
7. Ryan Ahmed, Ienkaran Arasaratnam, Mohamed El-Sayed, Jimi Tjong, and Saeid Habibi, “Online and Offline Parameters Identification and SOC Estimation for Healthy and Aged Electric Vehicle Batteries Based on Equivalent Circuit Models”, Journal of Power Sources, 2015.
8. Ryan Ahmed, Andrew Gadsden, Mohamed El-Sayed, Jimi Tjong, Saied Habibi, “Aged Battery State of Charge Estimation based on Interacting Multiple Models”, Journal of Power Sources, 2015.
9. Ryan Ahmed, S.A. Gadsden, M. El-Sayed, S. Habibi, and J. Tjong, (2013) Artificial Neural Network Training Utilizing the Smooth Variable Structure Filter Estimation Strategy. Journal of the International Neural Network Society. Ref#: NEUNET-D-12-00336. (Submitted and under review).
10. Arasaratnam Ienkaran, Jimi Tjong, and Ryan Ahmed (2014), Battery Management System in the Bayesian Paradigm: Part I, Transportation and Electrification Conference and Expo, 2014. (Published)
CASE STUDY/BATTERY MANAGEMENT CASE STUDY/BATTERY MANAGEMENT CASE STUDY/BATTERY MANAGEMENT CASE STUDY/BATTERY MANAGEMENT SYSTEM: SYSTEM: SYSTEM: SYSTEM: PUBLICATIONSPUBLICATIONSPUBLICATIONSPUBLICATIONS
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future SLIDE 44
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future SLIDE 45
CASE STUDY/BATTERY MANAGEMENT CASE STUDY/BATTERY MANAGEMENT CASE STUDY/BATTERY MANAGEMENT CASE STUDY/BATTERY MANAGEMENT SYSTEM: SYSTEM: SYSTEM: SYSTEM: RESEARCH AREASRESEARCH AREASRESEARCH AREASRESEARCH AREAS
Smart embedded controllers
• Artificial Neural Networks
• Optimal HEV controls
Energy storage systems
• Management and control
• Thermal management/packaging
• Modeling, SOC, SOH estimation, and aging
Hybrid battery/Supe
rcap.
• Modeling
• State prediction
• Packaging and integration
Fault detection
• State estimation
• Fault prediction and RUL prediction
THANKS, QUESTIONS?THANKS, QUESTIONS?THANKS, QUESTIONS?THANKS, QUESTIONS?
Ain Shams - 24/12/2016 Research Seminar: Mobility of the Future SLIDE 46
Ryan Ahmed