MOBILITY OF THE FUTURE: HYBRID AND ELECTRIC...

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

[email protected]

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


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


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


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


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


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


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


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


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


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/


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


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


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


• 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


• 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)


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


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.”


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%


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


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


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)


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


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



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


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


• 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)



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








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 %





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.


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


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.


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


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





• Aging Model Development: Do we need model update?

• Actual SOC from Aged Battery vs. Model SOC

• RMSE variations for healthy and aged batteries


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. |%~. ~%



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


• 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.


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


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



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?


Ryan Ahmed

[email protected]

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