«emr of a battery multi-physical model for electric vehicles€¦ · emr’18, hanoi univ....

EMR Hanoi

June 2018Summer School EMR’18

“Energetic Macroscopic Representation”

«EMR of a battery multi-physical model

for electric vehicles»

Dr. Ronan GERMAN, Prof. Alain BOUSCAYROL

L2EP, Université Lille1, France

EMR’18, Hanoi Univ. S&T, June 20182

« EMR of a battery multi-physical model for electric vehicles»

- Context and objective -

Safety

Ageing

Operation

Triple temperature impact on batteries

Very important to include cell temperature in models (simulation studies…)

Objective:

Represent in EMR an electro-thermal model of a Li-ion Battery

for EV simulation studies

Literature

• Small cells [Lin 13] [Forgez 09]

• 2,5 Ah

• 3,3 V

• Large cells used in EV

Our work

• 160 Ah

• 3,3 V

Li-ion LFP



-Electrification of vehicles-

Toyota Prius V « plug

in »

– 23 km electrical

driving range

Plug-in hybrid (PHEV)

External recharge

Electric vehicle (EV)

100-400 km driving

range

Electrification level

Electrification level

ICE vehicle

Volvo S 60 D5

Energy storage systems

(ESS) size

Mazda 6 i-Eloop

– Braking energy

recovery

– Stop and start

µ-hybrid +recovery

Peugeot 3008

hybrid4

– 4 km electrical

driving range

Full hybrid

Hybrid (HEV) No external recharge



-Battery technologies for e-mobility-

[Pillot 2015]

Market tendencies and forecast

15 %: NiMH 85% : Li-ion

Today

Measure Forecast

NiMH is present in HEVs only

NiMH is replaced by Li-ion in HEV

Li-ion tends to be the exclusive technology in

electromobility in a 5 years horizon

This study is focused on Li-ion

battery modeling



-Summary-

Introduction on batteries in electrical vehicles (EVs)

Electro-thermal model for one cell

Construction of the battery model from the cell model

Validation of the battery model

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« Concepts and definitions»



- Definitions -

• Cell : Battery elementary component

• State of charge SoC (%)

• Battery capacitance (A.h)

1 A.h means that the battery is fully discharged after 1 h at 1 A

SoC = 0% Battery totally discharged

SoC = 100% Battery fully charged

• Battery energy (kW.h) 1 kW.h =3.6 MJ

Golf GTE : 8 kW.h Tazzari Zero: 14.5 kW.h Renault Zoe: 41 kW.h

Plug in Hybrid electric vehicle (PHEV) Electric vehicles (EVs)

50 km 120 km 400 kmNEDC driving

range1 kWh ≈ 8 km NEDC electric driving range



- Lithium ion technology in EVs-

• Responsible of

• Cost

• Recharge time

• Driving range of the vehicle

Comparison of different ESSs

100

102

104

106

10-2

100

102

104

Mass Power (W/kg)

Mas

s En

ergy

(W

h/k

g)

SCs

Capacitors

Li-ion battery technology

• Energy density compatible

with 300 km autonomy for

standard EV

• Power density compatible

with EV acceleration

• Decreasing price

Pb

Example of 14,5 kWh Li-ion pack

placed in theTazzari Zero

Ni-Mh

Batteries

Li-ionFuell cell

+

H2 tank

In EV the Li-ion battery is the main ESS,



- Influence factors on Li-ion battery-

T 60°C, SoC 100%

T 45°C, SoC 100%

T 45°C, SoC 65%

1

0.2

0.4

0.6

0.8

0 500 1000

Time (h)

Ln(C0/C) T 60°C, SoC 100%

T 45°C, SoC

100%

T 45°C, SoC

65%

2.5

0.5

1.0

1.5

2.0

0 500 1000

Time (h)

Ln(ESR/ESR0)

• Ageing Rate

• temperature increases

ageing rate

• SoC insreases ageing

rate

[Baghdadi 16]

• Parameters instant value

− Battery Capacity influenced by the temperature

− Battery equivalent series resistance ( ESR)

influenced by the SoC and the temperature

Capacity

2 Ah

1 AhESR

200 mΩ

400 mΩ

+Temperature (°C)

-20 0 20 40

SoC= 80%

SoC= 50%

SoC= 20%

[Zhang 17]

Include temperature and SoC in battery model for EV simulation



- Li-ion battery in studied EV-

275 mm

183 mm

65 mm

Mass : 5.68 kg

CCell Nom : 160 Ah

UCell : 4V->2.5V

• Battery elementary component : 1 Cell • Cells are placed side by side in a module

+

+ -

+ -

-

Rear

Front Module 2

Module 1 Module 3

• Modules are placed in the EV for mass

repartition

uBat=24.uCell

iBat

Module1 =7 Cells

Module 2 =10 Cells

Module3 =7 Cells

• Modules are connected together to

achieve high battery voltage

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«Electro-thermal model for one cell»



-Energetic Macroscopic Representation [Bouscayrol 12]-

Real

systemUnified representation

systemic

organization

Subsystems dynamical

Models

+

Controllers

Simulation

studies

Energetic Macroscopic Representation (EMR)

• Causality principle: Output delayed compared to input

• 4 basic pictograms (In x Out=Power)

EMR

Bat. MS

UBat

iBat

DCM Winding

UDCM

IDCM

TDCM

ΩDCM

IDCM

FEMDCM

E/M conv Mechanical partChopperBattery

• Example of the torque

control of a DC Machine

(TDCM)m

• Control structure systematically

deduced by mirror effectTDCM RefTDCM RefUbat Mes IDCM Ref

IDCM MesFEMDCM MesmRef

Source SourceMono Phys.

Conv.

Accumulation Multi Phys.

Conv.



- Electrical model -

Structural representation

OC

V (

So

C,T

)

iCell

RS(SoC, T)

uCell

Cdl (SoC,T)

uRC

u’

iCdl

iRt

Rt (SoC,T)

Energetic Macroscopic Representation (EMR [Bou 12])

Conversion

Traction system

Current source

Electrochemical

storage

Voltage source

OCV

OCV

iCell

Energy losses (connectors, electrodes, electrolyte …)

RS

u’

iCell

uCell

Tract.

iCell

Cdl

iCdl

uRC

iRt

uRC

Rt

iCelluRC

Voltage coupling

Current coupling

Voltage coupling

Charge transfer and diffusion

Current couplingAccumulationConversion



-Introduction to cell thermal modeling-

Thermal capacitance (J/K)

Thermal energy storage

Thermal resistance (K/W)

Resistance to the power transfert

Hypothesis• Heat source at the core center

• Conduction only in solid

• Convection only for solid to gas

heat transfer

• Thermal resistances are

located at the interfaces

• Thermal capacitance of the

package neglected

Important notions

1cell

+

-

Core

Package

surface

Air

Tamb

Air

Tamb

Tamb

Rcond

Rconv

Pheat

=

RS.i

Cell²+R

t.i

Rt² T

core

Ccore

Pcore

POut

Tsurf

Tamb

Equivalent circuit

thermal model



-EMR for thermal model-

Tamb

Rcond +Rconv

Pheat

= RS.i

Cell²+R

t.i

Rt²

= qStot. Tcore

T

core

Ccore

Pcore=

qS2. Tcore

POut=

qS3. Tcore

POut=

qS5. Tamb

Tamb

Structural representation

TCore

qStot Rcond + Rconv

Air

qS5

TAmb

qS: entropy flow (W/K)

T: Temperature (K)

For thermal domain

Ccore

Tcore

qS3

EMR

RS Rt

Tcore

qS1’

qS1

Tcore

[Hor 16]



-Coupling thermal and electrical domains by EMR-

OCV

Cdl

OCV

iCell

RS

u’

iCell

iCelluRC

iCell

uCell

iCell

iCdl

uRC

iRt

uRC

Voltage coupling

Current coupling

Air

Ccore Rcond + Rconv

Tcore

qS1’

TCore

qStot

Tcore

qS3

qS5

TAmb

Rt

qS1Tcore

• EMR of the electro-thermal model

• Resistances are multi-physical ( electro-thermal) conversion elements

Thermal domain

Electrical domain

Resistances are at the border between thermal and electrical domains

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«From the cell to the battery»



-Li-ion battery in studied EV-

OCV

Cdl

OCV

iCell

RS

u’

iCell

iCelluRC

uCell

iCell

iCdl

uRC

iRt

uRC

Rt

Voltage coupling

Current coupling

Air

Ccore Rcond + Rconv

Tcore

qS1’

TCore

qStot

Tcore

qS3

qS5

TAmb

qS1Tcore

Thermal domain

Electrical domain

iBat

1 cell EMR

Assumptions

• Cells are identical

• Cells are not thermally influenced by

surrounding cells

Module1 EMR

uMod1

iMod1

Adaptation 1

𝑖𝐶𝑒𝑙𝑙 = 𝑖𝑀𝑜𝑑1

𝑢𝐶𝑒𝑙𝑙 . 7 = 𝑢𝑀𝑜𝑑1

Battery EMR

uBat

iBat

Adaptation 2

𝑖𝑀𝑜𝑑1 = 𝑖𝑃𝑎𝑐𝑘

𝑢𝑀𝑜𝑑1.24

7= 𝑢𝐶𝑒𝑙𝑙Use adaptation elements



-Experimental protocol for pack model validation-

+

-

iMod1

TAmb TAmb

V

uMod1

TCoreCell

TAmbMod1

Module1

Created at gpsvisualizer.com with google maps

N

Campus (urban)

Road (sub-urban)

• Instrumenting a module in the studied EV • Choosing a varied road

uMod1 (V)

time (s)0 1000 2000 3000 4000 5000 6000

10

15

20

25

30

35Experimental

Simulation

Time (s)

time (s)0 1000 2000 3000 4000 5000 6000

0

10

20

30

40TCoreCell (°C)

Time (s)

ΔTMax

• Compare model and experimental results

Relative absolute error on voltage : 7 % Relative absolute error on temperature : 4.8 %

• Driving

Battery model is validated with a real driving cycle in a real EV (ε<10%)



-Conclusion-

EMR organization and coupling of classical thermal and electrical cell

models

Assumptions have been made to build the battery electro-thermal model

from the cell model

Onboard validation a with an instrumented EV module during driving



- Authors -

Prof. Alain BOUSCAYROL

University Lille 1, L2EP, MEGEVH, France

Coordinator of MEGEVH, French network on HEVs

PhD in Electrical Engineering at University of Toulouse (1995)

Research topics: EMR, HIL simulation, tractions systems, EVs and HEVs

Dr. Ronan German

University Lille 1, L2EP, France

PhD in Electrical Engineering at Univ. Lyon 1 (2013)

Research topics: Battery Modelling, Energy management of multi-sources vehicles

EMR Hanoi



« BIOGRAPHIES AND REFERENCES »



- References -

[Baghdadi 16] I. Baghdadi, O. Briat, J.-Y. Delétage, P. Gyan, et J.-M. Vinassa, « Lithium battery aging model based on Dakin’s

degradation approach », Journal of Power Sources, vol. 325, p. 273-285, sept. 2016.

[Forgez 09] Christophe Forgez, Dinh Vinh Do, Guy Friedrich, Mathieu Morcrette, Charles Delacourt, " Thermal modeling of a

cylindrical LiFePO4/graphite lithium-ion battery," Journal of Power Sources, Volume 195, Issue 9, 1 May 2010, Pages 2961-

2968, ISSN 0378-7753, http://dx.doi.org/10.1016/j.jpowsour.2009.10.105.

[Bouscayrol 12] A. Bouscayrol, J.-P. Hautier, et B. Lemaire-Semail, Systemic design methodologies for electrical energy

systems-Chapter 3: Graphic formalism for the control of multi-physical energetic systems: COG and EMR, Wiley. New York,

NY, USA, 2012.

[German 17] R. German, S. Shili, A. Sari, P. Venet, et A. Bouscayrol, « Characterization Method for Electrothermal Model of

Li-Ion Large Cells », in 2017 IEEE Vehicle Power and Propulsion Conference (VPPC), 2017, p. 1-6

[German 18] R. German, P. Delarue, et A. Bouscayrol, « Battery pack self-heating during the charging process », in 2018

IEEE International Conference on Industrial Technology (ICIT), 2018, p. 2049-2054.

[Horrein 16] L. Horrein, A. Bouscayrol, W. Lhomme, et C. Depature, « Impact of heating system on the range of an electric

vehicle », IEEE Transactions on Vehicular Technology, 2016.



-References-

[Yi 13] J. Yi, U. S. Kim, C. B. Shin, T. Han, et S. Park, « Modeling the temperature dependence of the discharge behavior of a

lithium-ion battery in low environmental temperature », Journal of Power Sources, vol. 244, p. 143-148, déc. 2013.

[Zhang 17] Y. C. Zhang, O. Briat, J. Y. Deletage, C. Martin, G. Gager, et J. M. Vinassa, « Performance

quantification of latest generation Li-ion batteries in wide temperature range », in IECON 2017 - 43rd Annual

Conference of the IEEE Industrial Electronics Society, 2017, p. 7666-7671.

[Lin 13] X. Lin, H. E. Perez, S. Mohan, J. B. Siegel, A. G. Stefanopoulou, Y. Ding, M. P. Castanier, “A lumped-parameter

electro-thermal model for cylindrical batteries”, Journal of Power Sources, Volume 257, 1 July 2014, Pages 1-11, ISSN 0378-

7753.

[Redondo 16] E. Redondo-Iglesias, P. Venet, and S. Pelissier, “Measuring Reversible and Irreversible Capacity Losses on

Lithium-Ion Batteries,” presented at the Vehicle Power and Propulsion Conference (VPPC) , pp. 1–5, 2016 IEEE, 2016.

[Pillot 2015] C. Pillot, « Battery Market Development for Consumer Electronics, Automotive, and Industrial: Materials

Requirements and Trends», Avicenne Energy, 2015.

«emr of a battery multi-physical model for electric vehicles€¦ · emr’18, hanoi univ....

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