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
EMR’18, Hanoi Univ. S&T, June 20183
« EMR of a battery multi-physical model for electric vehicles»
-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
EMR’18, Hanoi Univ. S&T, June 20184
« EMR of a battery multi-physical model for electric vehicles»
-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
EMR’18, Hanoi Univ. S&T, June 20185
« EMR of a battery multi-physical model for electric vehicles»
-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
EMR Hanoi
June 2018Summer School EMR’18
“Energetic Macroscopic Representation”
« Concepts and definitions»
EMR’18, Hanoi Univ. S&T, June 20187
« EMR of a battery multi-physical model for electric vehicles»
- 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
EMR’18, Hanoi Univ. S&T, June 20188
« EMR of a battery multi-physical model for electric vehicles»
- 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,
EMR’18, Hanoi Univ. S&T, June 20189
« EMR of a battery multi-physical model for electric vehicles»
- 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
EMR’18, Hanoi Univ. S&T, June 201810
« EMR of a battery multi-physical model for electric vehicles»
- 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
EMR Hanoi
June 2018Summer School EMR’18
“Energetic Macroscopic Representation”
«Electro-thermal model for one cell»
EMR’18, Hanoi Univ. S&T, June 201812
« EMR of a battery multi-physical model for electric vehicles»
-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.
EMR’18, Hanoi Univ. S&T, June 201813
« EMR of a battery multi-physical model for electric vehicles»
- 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
EMR’18, Hanoi Univ. S&T, June 201814
« EMR of a battery multi-physical model for electric vehicles»
-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’18, Hanoi Univ. S&T, June 201815
« EMR of a battery multi-physical model for electric vehicles»
-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]
EMR’18, Hanoi Univ. S&T, June 201816
« EMR of a battery multi-physical model for electric vehicles»
-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
EMR Hanoi
June 2018Summer School EMR’18
“Energetic Macroscopic Representation”
«From the cell to the battery»
EMR’18, Hanoi Univ. S&T, June 201818
« EMR of a battery multi-physical model for electric vehicles»
-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
EMR’18, Hanoi Univ. S&T, June 201819
« EMR of a battery multi-physical model for electric vehicles»
-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%)
EMR’18, Hanoi Univ. S&T, June 201820
« EMR of a battery multi-physical model for electric vehicles»
-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
EMR’18, Hanoi Univ. S&T, June 201821
« EMR of a battery multi-physical model for electric vehicles»
- 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
June 2018Summer School EMR’18
“Energetic Macroscopic Representation”
« BIOGRAPHIES AND REFERENCES »
EMR’18, Hanoi Univ. S&T, June 201823
« EMR of a battery multi-physical model for electric vehicles»
- 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.
EMR’18, Hanoi Univ. S&T, June 201824
« EMR of a battery multi-physical model for electric vehicles»
-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.