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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
Summer School EMR’17
“Energetic Macroscopic Representation”
« Objective and context»
EMR’17, University Lille 1, June 20174
« EMR for Li-ion Battery electro-thermal model»
- 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
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« EMR for Li-ion Battery electro-thermal model»
- 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
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« EMR for Li-ion Battery electro-thermal model»
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
Summer School EMR’17
“Energetic Macroscopic Representation”
« Modelling Li-ion batteries»
EMR’17, University Lille 1, June 20178
« EMR for Li-ion Battery electro-thermal model»
- 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
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« EMR for Li-ion Battery electro-thermal model»
- 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
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« EMR for Li-ion Battery electro-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]
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« EMR for Li-ion Battery electro-thermal model»
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-
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« EMR for Li-ion Battery electro-thermal model»
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
Summer School EMR’17
“Energetic Macroscopic Representation”
« Comparison with experimental results»
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« EMR for Li-ion Battery electro-thermal model»
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
Summer School EMR’17
“Energetic Macroscopic Representation”
« Conclusion»
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« EMR for Li-ion Battery electro-thermal model»
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
Summer School EMR’17
“Energetic Macroscopic Representation”
« BIOGRAPHIES AND REFERENCES »
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« EMR for Li-ion Battery electro-thermal model»
- 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
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« EMR for Li-ion Battery electro-thermal model»
- 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
University Lyon 1, Laboratoire Ampère UMR CNRS 5005, France
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
University Lyon 1, Laboratoire Ampère UMR CNRS 5005, France
PhD in Electrical Engineering at Univ. Lyon 1 (1993)
Research topics: ESS reliability, ESS Models, ESS
characterization, BMS
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« EMR for Li-ion Battery electro-thermal model»
- 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
Summer School EMR’17
“Energetic Macroscopic Representation”
« Appendix»
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« EMR for Li-ion Battery electro-thermal model»
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-