« EMR for Li-ion Battery electro- thermal model taking ... · PDF file5 « EMR for Li-ion ... "Systemic design methodologies for electrical energy systems, Chapter 3: Graphic formalism

EMR’17

University Lille 1

June 2017

Summer School EMR’17

“Energetic Macroscopic Representation”

« EMR for Li-ion Battery electro-

thermal model taking into account the

charge transfer delay »

Dr. Ronan GERMAN [1,3], Dr. Seima Shili [2,3], Dr. Ali Sari [2,3], Prof. Alain

BOUSCAYROL [1,3], Prof. Pascal Venet [2,3]

1 L2EP, Université Lille1, France2 Laboratoire Ampère, Université Lyon 1, France

3 MEGEVH network, France

EMR’17, University Lille 1, June 20172

« EMR for Li-ion Battery electro-thermal model»

- Outline -

1. Context of the presentation

• Objective and method

• Batteries in EV context

• Important notions for Li-ion batteries

2. Battery modeling

• Electrical model of battery

• Thermal model of battery

• Coupling thermal and electrical domains by EMR

3. Model Validation

• Validation model with experimental results

4. Conclusion

EMR’17

University Lille 1

June 2017



« Objective and context»



- Objective an method-

Objective of the work • Take into account temperature in Li-ion battery

models in order to improve simulation precision in EV

EMR [Bou 12]• Tool to organize coupling between equivalent circuit and thermal models

Method

Application • Energy management of batteries in EV

Model organizing by EMR

CCell=160 Ah

ICell max= 3 C

Real EV large cell

Electro-thermal modelling

Comparison of experimental

and model electro-thermal

behaviour

WLTC : Worldwide Light duty

vehicles Test Cycle

Ref EV : Tazzari

Zero



- Batteries in EV context-

• Responsible of

• Cost

• Recharge time

• Autonomy 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)

36 ms

1 h 36 s100 h

Fuell cell

SCs

Capacitors

Li-ion battery technology

• Energy density compatible

with 150 km autonomy for

standard EV

• Power density compatible

with EV acceleration

Batteries

Li-ion

Ni-MhPb

In most EV the battery is the main ESS,

Example of 14,5 kWh Li-ion pack

placed in theTazzari Zero



Instant battery electric parameters variation

• Increase of Electrolyte viscosity with lower temperature [Lin 13]

Ageing acceleration factor [Red 16]

Catastrophic failures

Triple temperature effect on batteries

Max power dependent of T°

Very important to estimate temperature in

simulation for sizing purpose

State of Charge (SoC)

SoC= 100% : fully charged SoC= 0% : fully discharged

- Important notions for Li-ion Batteries-

EMR’17

University Lille 1

June 2017



« Modelling Li-ion batteries»



- Electrical model-

Structural representation

OC

V (

So

C,T

)

iCell

RS(SoC, T)

uCell

Cdl (SoC,T)

uRC

u’

iCdl

iRt

Rt (SoC,T)

Equations EMR

Electrochemical

storage

Voltage source

Energy losses (connectors, electrodes, electrolyte …)

Conversion

Voltage coupling

Available voltage and

current for traction

Current source

Charge transfer delay

OCV

OCV

iCell

RS

u’

iCell

uCell

Tract.

iCell

Cdl

iCdl

uRC

iRt

uRC

Rt

iCelluRC

Voltage coupling

Current coupling

Current couplingAccumulationConversion



- Introduction to battery 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

[ Forgez 09] [Lin 13]

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



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

Equations

qS : entropy flow (W/K)

T : Temperature (K)

For thermal domain

Ccore

Tcore

qS3

EMR

- EMR for thermal model-

RS Rt

Tcore

qS1’

qS1

Tcore

[Hor 16]



OCV

Cdl

OCV

iCell

RS

u’

iCell

uRCiCell

Tract.uCell

iCell

uRC

iCdl

iRt

uRC

Rt

Voltage coupling

Current coupling

RS

Air

Ccore Rcond + Rconv

Rt

Tcore

qS1’

TCore

qStot

qS1

Tcore

Tcore

qS3

qS5

TAmb

Electrical model EMR

Thermal model EMR

Here resistances are linked to electrical and thermal model

- Coupling thermal and electrical domains by EMR-



OCV

Cdl

OCV

iCell

RS

u’

iCell

iCelluRC

Tract.uCell

iCell

iCdl

uRC

iRt

uRC

Rt

Voltage coupling

Current coupling

Air

Ccore Rcond + Rconv

Tcore

qS1’

TCore

qStot

Tcore

qS3

qS5

TAmb

qS1Tcore

EMR of the electro-thermal model

Here resistances becomes multi-physical ( electro-thermal) conversion

elements

Thermal domain

Electrical domain

- Coupling thermal and electrical domains by EMR-

EMR’17

University Lille 1

June 2017



« Comparison with experimental results»



0 500 1000 1500-100

0

100

200

300

Time (s)

i Cell (

A)

Urban

Extra urban

OCV

Cdl

OCV

iCell

RS

u’

iCell

uRCiCell

Tract.uCell

iCell

uRC

iCdl

iRt

uRC

Rt

Voltage coupling

Current coupling

Air

Ccore Rcond + Rconv

qS1’

Tcore

TCore

qStot

Tcore

qS3

qS5

TAmb

qS1Tcore

Simulation results

(Tcore, uCell)

0 2000 4000 6000 8000 1000025

26

27

28

29

30

31

Time (s)

Tem

per

ature

(ºC

)

T core

Sim

T core

Exp

WLTC x 6

Avg error on Temperature < 1,5 %

0 2000 4000 6000 8000 100003

3.2

3.4

3.6

3.8

Time (s)

uC

ell

(V)

u CellSim

u CellExp

WLTC

SOCinit = 100%

SOCend ~30%

Avg error on voltage < 1 %

iCell cycle corresponding to WLTC

Experimental results

(Tcore, uCell)

Battery test bench

EMR’17

University Lille 1

June 2017



« Conclusion»



EMR is systematic tool for multi-domain

models organization

Good precision of the model in term of electro-thermal behavior

Interesting for EV computer aided engineering:

• Battery sizing at different temperatures

• Estimation of self-heating

• BMS conception

• Energy management strategies

- Comparison between experimental results and model-

EMR’17

University Lille 1

June 2017



« BIOGRAPHIES AND REFERENCES »



- 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,

Associate professor,

PhD in Electrical engineering at university Lyon 1 (2013)

Research topics: Battery model organization in EV system



- Authors -

Dr. Seima SHILI

University Lyon 1, Laboratoire Ampère UMR CNRS 5005, France

PhD in Electrical Engineering at Univ.Lyon 1 (2016)

Research topics: Energy storage systems (ESS) reliability,

Battery Management Systems (BMS) Control, ESS characterization

Dr. Ali SARI


Associate professor at Univ. Lyon 1

PhD in Electrical Engineering at Univ. of Franche-comté (2009)

Research topics: ESS reliability, ESS Models, ESS

characterization, BMS

Prof. Pascal VENET


PhD in Electrical Engineering at Univ. Lyon 1 (1993)

Research topics: ESS reliability, ESS Models, ESS

characterization, BMS



- Bibliography -

[Bou 12] A. Bouscayrol, J.-P. Hautier, and 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.

[Edd 12] A. Eddahech, O. Briat, E. Woirgard, J.M. Vinassa, " Remaining useful life prediction of lithium

batteries in calendar ageing for automotive applications," Microelectronics Reliability, Volume 52, Issues

9–10, September–October 2012, Pages 2438-2442.

[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.

[Hor 16] L. Horrein, A. Bouscayrol, W. Lhomme, and C. Depature, “Impact of heating system on the range

of an electric vehicle,” IEEE Trans. Veh. Technol., 2016

[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.

[Red 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.

EMR’17

University Lille 1

June 2017



« Appendix»



i

e-e-

Electrodes

Separator

Electrolyte

Solid lithium Ionic lithium

e- Electrons flow

Instant battery electric parameters

variation• Increase of Electrolyte viscosity with lower

temperature [Lin 13]

Ageing acceleration factor [Red 16]

Catastrophic fails

Triple temperature effect on batteries

Max power dependent of T°

Discharge principle

• 1 : Charge transfer (- electrode)

• 2 : Ion transfer

• 3 : Charge transfer (+ electrode)

Very important to estimate temperature in

simulation for sizing purpose

State of Charge (SoC)

SoC= 100% : fully charged

SoC= 0% : fully discharged

- Important notions for Li-ion Batteries-

Documents

« EMR for Li-ion Battery electro- thermal model taking ... · PDF file5 « EMR for Li-ion ... "Systemic design methodologies for electrical energy systems, Chapter 3: Graphic formalism