LMS French Users Conference 2013 - Li-ion qging Modeling and battery pack sizing

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Énergies renouvelables | Production éco-responsable | Transports innovants | Procédés éco-efficients | Ressources durables©2011-IFPEnergiesnouvelles
PRADA et al. - LMS Conference - May 2013 - France
Aging modeling for advanced
Li-ion battery pack sizing and
management for HEV/EV
through AMESim simulation
platform
Eric PRADA and Martin PETIT
Electrochemistry and Materials Department
IFPEN
©2011-IFPEnergiesnouvelles
PRADA et al. - LMS Conference - May 2013 - France2
Industrial Context
As LiAs LiAs LiAs Li----ion batteries are more and more deployed forion batteries are more and more deployed forion batteries are more and more deployed forion batteries are more and more deployed for
transportation applications, assurances of lifetime andtransportation applications, assurances of lifetime andtransportation applications, assurances of lifetime andtransportation applications, assurances of lifetime and
reliability are essential.reliability are essential.reliability are essential.reliability are essential.
LiLiLiLi----ion systems lifetime prediction is still an issue forion systems lifetime prediction is still an issue forion systems lifetime prediction is still an issue forion systems lifetime prediction is still an issue for
battery engineers and automotive makers becausebattery engineers and automotive makers becausebattery engineers and automotive makers becausebattery engineers and automotive makers because
of multiple and complex aging mechanisms andof multiple and complex aging mechanisms andof multiple and complex aging mechanisms andof multiple and complex aging mechanisms and
multiple chemistries (NCA, LFP, NMC, LTO...)!multiple chemistries (NCA, LFP, NMC, LTO...)!multiple chemistries (NCA, LFP, NMC, LTO...)!multiple chemistries (NCA, LFP, NMC, LTO...)!
Illustrations PSA & Renault Cars
What is the typical battery usage? How do I size the battery pack?What is the typical battery usage? How do I size the battery pack?What is the typical battery usage? How do I size the battery pack?What is the typical battery usage? How do I size the battery pack?
How long will it last? Warranty 5 years, 6 years, 10years?How long will it last? Warranty 5 years, 6 years, 10years?How long will it last? Warranty 5 years, 6 years, 10years?How long will it last? Warranty 5 years, 6 years, 10years?
What is the impact of usage on the battery lifetime?What is the impact of usage on the battery lifetime?What is the impact of usage on the battery lifetime?What is the impact of usage on the battery lifetime?
Is the chosen LiIs the chosen LiIs the chosen LiIs the chosen Li----ion technology a good choice?ion technology a good choice?ion technology a good choice?ion technology a good choice?
......?......?......?......?

2
As a function of battery usage inAs a function of battery usage inAs a function of battery usage inAs a function of battery usage in
an electrified vehicle, one canan electrified vehicle, one canan electrified vehicle, one canan electrified vehicle, one can
distinguish two aging modes :distinguish two aging modes :distinguish two aging modes :distinguish two aging modes :
CYCLING & CALENDAR distribution for
different vehicles (Source, Magna)
CYCLING ModeCYCLING ModeCYCLING ModeCYCLING Mode
Battery is cycled in charge/dischargeBattery is cycled in charge/dischargeBattery is cycled in charge/dischargeBattery is cycled in charge/discharge
(I=I(I=I(I=I(I=Ich/dchch/dchch/dchch/dch)))) / Driving Mode/ Driving Mode/ Driving Mode/ Driving Mode
CALENDAR ModeCALENDAR ModeCALENDAR ModeCALENDAR Mode
Battery is stored (I = 0 A)Battery is stored (I = 0 A)Battery is stored (I = 0 A)Battery is stored (I = 0 A) //// Parking ModeParking ModeParking ModeParking Mode
How to define Li-ion batteries aging? (1/2)
CYCLING & CALENDAR modes
are combined during the lifetime for different vehicles
How to define Li-ion batteries aging? (2/2)
Batteries are characterized by two macroscopic parameters :Batteries are characterized by two macroscopic parameters :Batteries are characterized by two macroscopic parameters :Batteries are characterized by two macroscopic parameters :
Internal Resistance R (Internal Resistance R (Internal Resistance R (Internal Resistance R (ΩΩΩΩ)))) PowerPowerPowerPower (W)(W)(W)(W)
Nominal Capacity CNominal Capacity CNominal Capacity CNominal Capacity Cnomnomnomnom (Ah)(Ah)(Ah)(Ah) EnergyEnergyEnergyEnergy (Wh)(Wh)(Wh)(Wh)
Battery aging leads to a degradation of theBattery aging leads to a degradation of theBattery aging leads to a degradation of theBattery aging leads to a degradation of the
system performances :system performances :system performances :system performances :
Rise of Resistance RRise of Resistance RRise of Resistance RRise of Resistance R Power lossPower lossPower lossPower loss
Reduction of cell capacity CReduction of cell capacity CReduction of cell capacity CReduction of cell capacity C Energy lossEnergy lossEnergy lossEnergy loss
Spotnitz et al. , JPS 72 (2006) 113
EOL criterion #
Closs = -20% Cnom
Capacity fade curve is impacted by operating stress factorsCapacity fade curve is impacted by operating stress factorsCapacity fade curve is impacted by operating stress factorsCapacity fade curve is impacted by operating stress factors (T,(T,(T,(T, ΔΔΔΔDOD, SOC, I)DOD, SOC, I)DOD, SOC, I)DOD, SOC, I)
tEOL
Systems simulations can help understand the impact of stress factorsSystems simulations can help understand the impact of stress factorsSystems simulations can help understand the impact of stress factorsSystems simulations can help understand the impact of stress factors
and usage strategies on the battery life but requires advancedand usage strategies on the battery life but requires advancedand usage strategies on the battery life but requires advancedand usage strategies on the battery life but requires advanced
electroelectroelectroelectro----thermal battery models integrating aging predictionthermal battery models integrating aging predictionthermal battery models integrating aging predictionthermal battery models integrating aging prediction

3
OUTLINE
I. Electro-thermal capacity loss model development within
the Electric Storage Library on AMESim
II. Model experimental validation
III. Simulations study for PHEV battery pack lifetime :
Impact of usage strategies and V2G capability
IV. Conclusions and Perspectives
OUTLINE

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AMESim Library of Electric Storage
Systems Models (Rev. 12)
ESS models on AMESim Rev 12ESS models on AMESim Rev 12ESS models on AMESim Rev 12ESS models on AMESim Rev 12
Generic Models
for Batteries
and
Ultracapacitors
Validated models (Ni-MH,
Li-ion, EDLC) of real
industrial storage systems!
Battery Assitant Tool :Battery Assitant Tool :Battery Assitant Tool :Battery Assitant Tool :
Generic model calibration toolGeneric model calibration toolGeneric model calibration toolGeneric model calibration tool
User can model its battery systemsUser can model its battery systemsUser can model its battery systemsUser can model its battery systems
with own datasetswith own datasetswith own datasetswith own datasets
Safety Control
Unit (SCU) for
BMS security
functions
Empirical capacity loss modeling (1/2)
Aging model coupled with an ElectroAging model coupled with an ElectroAging model coupled with an ElectroAging model coupled with an Electro----thermal RC Model from ESSthermal RC Model from ESSthermal RC Model from ESSthermal RC Model from ESS
Library Revision 12Library Revision 12Library Revision 12Library Revision 12
I
Cdt
dSOC
res36
1
=
Dynamic Electro-thermal
Equivalent circuit model
from ES Library (Rev 12)
Empiricial Capacity loss
model accounting for the
impact of Temperature (T),
Current Intensity (I) and
SOC (Rev13) (LFP/C)
I
SOC
Cres
Ta Tcell
I
Vcell
SOH
( ) ( ) ( )( )tQ
C
tC
tSOH totloss
nom
res
,100100 −==
StateStateStateState----OfOfOfOf----Health : SOHHealth : SOHHealth : SOHHealth : SOH
Discharge Rest
HEV Duty Cycle
Rest
QQQQloss,totloss,totloss,totloss,tot is the state variable to be computedis the state variable to be computedis the state variable to be computedis the state variable to be computed

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Empirical capacity loss modeling (2/2)
Capacity loss during storageCapacity loss during storageCapacity loss during storageCapacity loss during storage (1)(1)(1)(1) ::::
( )








−



















 −




 −
=
Calz
Cala
Cal
totlossCala
CalCal
totloss
RT
E
B
Q
RT
E
SOCBz
dt
dQ
1
1
,
,,,
exp
exp
Capacity loss during constant current cyclingCapacity loss during constant current cyclingCapacity loss during constant current cyclingCapacity loss during constant current cycling (2)(2)(2)(2): (: (: (: (Constant ChargeConstant ChargeConstant ChargeConstant Charge))))
),,,,,( ,
,
Cyc
CycCycaCyc
totloss
BEzTIf
dt
dQ
α=
( ) ( )( )tQ
C
tC totloss
nom
res ,100
100
−=
Total Capacity Loss,Total Capacity Loss,Total Capacity Loss,Total Capacity Loss, QQQQloss,totloss,totloss,totloss,tot and Residual Capacity,and Residual Capacity,and Residual Capacity,and Residual Capacity, CCCCresresresres
( )
( )



>=
=
h
C
Iand
dt
dI
iffromcomputed
fromcomputed
dt
dQ
nom
totloss
1
02
1
,
Model Parameters (7)Model Parameters (7)Model Parameters (7)Model Parameters (7)
zzzzCalCalCalCal, E, E, E, Ea,Cala,Cala,Cala,Cal, B, B, B, BCalCalCalCal
zzzzCycCycCycCyc, E, E, E, Ea,Cyca,Cyca,Cyca,Cyc, B, B, B, BCycCycCycCyc,,,, ααααCycCycCycCyc
Parameters are calibrated with Calendar tests and conventional
Charge/Discharge Cycling tests
OUTLINE
III. Simulations study for PHEV battery pack lifetime: Impact
of usage strategies and V2G capability

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Aging Model Validation (1/2)
PHEV Duty cycle at 45PHEV Duty cycle at 45PHEV Duty cycle at 45PHEV Duty cycle at 45°°°°CCCC
The current intensity sollicitation is representative of a PHEV dailyThe current intensity sollicitation is representative of a PHEV dailyThe current intensity sollicitation is representative of a PHEV dailyThe current intensity sollicitation is representative of a PHEV daily
mission profile including storage, driving cycles and rechargemission profile including storage, driving cycles and rechargemission profile including storage, driving cycles and rechargemission profile including storage, driving cycles and recharge
Driving cycles
Depleting
Mode
Charging
Mode
Storage
Mode
Simulation vs ExperimentsSimulation vs ExperimentsSimulation vs ExperimentsSimulation vs Experiments
HEV duty cycles and impulses impact on battery lifetimeHEV duty cycles and impulses impact on battery lifetimeHEV duty cycles and impulses impact on battery lifetimeHEV duty cycles and impulses impact on battery lifetime (Savoye et al.)(Savoye et al.)(Savoye et al.)(Savoye et al.)
Good results until SOH = 80% within the calibration range
Limits of the approach near EOL and for power fade correlation
Need of more complex physics-based models also developed at IFPEN
Aging Model Validation (2/2)

7
OUTLINE
III. Simulations study for PHEV battery pack lifetime: Impact
of usage strategies and V2G capability
Simulations of PHEV Battery pack Lifetime (1/3)
Impact of usage strategy : How to monitor the storage SOC of a PHEV?Impact of usage strategy : How to monitor the storage SOC of a PHEV?Impact of usage strategy : How to monitor the storage SOC of a PHEV?Impact of usage strategy : How to monitor the storage SOC of a PHEV?
Strategy 1: (Classic Usage)Strategy 1: (Classic Usage)Strategy 1: (Classic Usage)Strategy 1: (Classic Usage)
The Battery is recharged atThe Battery is recharged atThe Battery is recharged atThe Battery is recharged at
the end of the daily missionthe end of the daily missionthe end of the daily missionthe end of the daily mission
profile. It is stored at theprofile. It is stored at theprofile. It is stored at theprofile. It is stored at the
"CHARGED" state"CHARGED" state"CHARGED" state"CHARGED" state
SOC = 90%SOC = 90%SOC = 90%SOC = 90%
Strategy 2: ("JustStrategy 2: ("JustStrategy 2: ("JustStrategy 2: ("Just----InInInIn----Time")Time")Time")Time")
The Battery is rechargedThe Battery is rechargedThe Battery is rechargedThe Battery is recharged
just before the dailyjust before the dailyjust before the dailyjust before the daily
mission profile (Justmission profile (Justmission profile (Justmission profile (Just----InInInIn----
Time). It is stored at theTime). It is stored at theTime). It is stored at theTime). It is stored at the
"DISCHARGED" state"DISCHARGED" state"DISCHARGED" state"DISCHARGED" state
SOC = 30%SOC = 30%SOC = 30%SOC = 30%
Impact of "Classic Charging" vs "Just-In-Time Charging" scenario
Charging
Mode
Charging
Mode

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Battery Pack Lifetime depends on the vehicle usageBattery Pack Lifetime depends on the vehicle usageBattery Pack Lifetime depends on the vehicle usageBattery Pack Lifetime depends on the vehicle usage
Lowering the storage SOC reduces the aging (for this LiLowering the storage SOC reduces the aging (for this LiLowering the storage SOC reduces the aging (for this LiLowering the storage SOC reduces the aging (for this Li----ion Technology)ion Technology)ion Technology)ion Technology)
Lowering the temperature T reduces the aging as wellLowering the temperature T reduces the aging as wellLowering the temperature T reduces the aging as wellLowering the temperature T reduces the aging as well
Impact of storage SOC on a Daily MissionImpact of storage SOC on a Daily MissionImpact of storage SOC on a Daily MissionImpact of storage SOC on a Daily Mission
Simulations are performed at 15Simulations are performed at 15Simulations are performed at 15Simulations are performed at 15°°°°C, 25C, 25C, 25C, 25°°°°C, and 35C, and 35C, and 35C, and 35°°°°C until SOH = 80%C until SOH = 80%C until SOH = 80%C until SOH = 80%
Simulation ResultsSimulation ResultsSimulation ResultsSimulation Results
x 2,2
Impact of usage strategy : Is V2G technology able to extend battery life?Impact of usage strategy : Is V2G technology able to extend battery life?Impact of usage strategy : Is V2G technology able to extend battery life?Impact of usage strategy : Is V2G technology able to extend battery life?
(1) Kempton et al.,(1) Kempton et al.,(1) Kempton et al.,(1) Kempton et al., J. Power Sources,144 (2005) 268J. Power Sources,144 (2005) 268J. Power Sources,144 (2005) 268J. Power Sources,144 (2005) 268----279279279279
Towards optimal profiles design to extend battery life and
improve energy production management to/from the grid!
Simulation ResultsSimulation ResultsSimulation ResultsSimulation Results
12,78 y
Strat 1
15,05 y
Strat 2 14,6 y
Strat 3
V2G could extend battery
lifetime in this simulation.
Home to Work trip = H W
Work to Home trip = W H
HHHH WWWW HHHH WWWWWWWW HHHH WWWW HHHH

9
OUTLINE
Conclusions and Perspectives (1/2)
A dynamic capacity loss model is developed for Li-ion batteries
and is implemented into AMESim software and coupled with an
electro-thermal model of the Electric Storage library Revision 12
(for a LiFePOLiFePOLiFePOLiFePO4444/C/C/C/C technology).
The aging model is calibrated on calendar and conventional
cycling tests and accounts, within the calibration range, for the
impact of :
Temperature, T
State of Charge, SOC
Current intensity for constant current profiles, I (for charge only,
dynamic current is accounted for through temperature activation)
The model is validated on :
PHEV duty cycles
Dynamic HEV profiles

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The impact of vehicle usage strategies on the battery pack
lifetime has been explored by simulation. Lowering the SOC
during storage of Li-ion batteries could extend the lifetime ("Just-
In-Time" charging could be a good solution).
The impact of V2G is discussed as well. Extension of battery
lifetime is expected through V2G technology in this case
Future work will deal with the development of a generic multi-
physics (electrochemical, thermal, mechanical) and multi-
chemistry aging model for Li-ion technologies to extend
prediction capabilities
Conclusions and Perspectives (2/2)
Join the future AGIL(ES)² Consortium* launched by
IFPEN on this topic!
* AGIL(ES)² : AGing modeling of Industrial Li-ion Electrochemical Energy Storage Systems
Thank you for your attentionThank you for your attentionThank you for your attentionThank you for your attention
Feel free to contact usFeel free to contact usFeel free to contact usFeel free to contact us for any questions andfor any questions andfor any questions andfor any questions and
for the upcoming AGIL(ES)² Consortium*!for the upcoming AGIL(ES)² Consortium*!for the upcoming AGIL(ES)² Consortium*!for the upcoming AGIL(ES)² Consortium*!
eric.prada@ifpen.freric.prada@ifpen.freric.prada@ifpen.freric.prada@ifpen.fr
+33 4 37 70 23 52+33 4 37 70 23 52+33 4 37 70 23 52+33 4 37 70 23 52
valerie.sauvant@ifpen.frvalerie.sauvant@ifpen.frvalerie.sauvant@ifpen.frvalerie.sauvant@ifpen.fr
+33 4 37 70 26 85+33 4 37 70 26 85+33 4 37 70 26 85+33 4 37 70 26 85
* AGIL(ES)² :* AGIL(ES)² :* AGIL(ES)² :* AGIL(ES)² : AGAGAGAGing modeling ofing modeling ofing modeling ofing modeling of IIIIndustrialndustrialndustrialndustrial LLLLiiii----ionionionion EEEElectrochemicallectrochemicallectrochemicallectrochemical EEEEnergynergynergynergy SSSStoragetoragetoragetorage SSSSystemsystemsystemsystems

LMS French Users Conference 2013 - Li-ion qging Modeling and battery pack sizing

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