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CALENDAR AGEING MODELING OF LITHIUM-ION BATTERIES
MAT4BAT SUMMER SCHOOL June 2nd – 4th 2015
Philippe GYAN
Thusday June 4th 2015, 11:00
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01 INTRODUCTIONChallenges on battery durability
02 COLLABORATIVE PROJECTS ON BATTERY AGEINGSIMSTOCK - SIMCAL
03 APPLICATION OF AGEING MODELSBattery ageing scenario simulator
04 APPLICATION TO MAT4BAT MEASURESKOKAM Cell
05 CONCLUSIONS AND PERSPECTIVESFurther Developments
OUTLINE
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INTRODUCTION
Challenges on battery durability
01
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Context and challenges of battery durability
� Growing market of electrified vehicles
� Alliance Renault-Nissan
� Full offer of electric vehicles
� Over 200000 vehicles sold worldwide
� 58% of world market
1.1 INTRODUCTION
Twizy Zoe Fluence Kangoo
http://evworld.com/
Nissan Leaf
Nissan EV200
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���� Make Electric Vehicle more affordable
� Battery, Costly component
� 400 to 1200 € / kWh lithium ion
� Around 10000 € for a 24 kWh -pack
� Battery rental economic model
� Bringing down costs Cost of an electric vehicle without batteries ~ cost of a thermal vehicle
Cost of battery rental : starting at 49 €/month (small drivers) to 102 €/month ~ gas expenses
Cost of electric energy : ~2 € for 180 km of range
� Assistance and warrantiesInstallation of charger
Road assistance
Replacement of the battery when remaining capacity below 75 %
Sustainable mobility accessible to all
1.2 INTRODUCTION
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� Battery : energy source
� Strongly non-linear behavior
Capacity = f(N cycles)
Capacity = f(Temperature)
Capacity = f(Current)
Capacity = t(Time)
Voltage = f(State of Charge)
Voltage = f(Time)
Battery very sensitive to
• time (calendar)
• usage (cycling)
• external conditions
�Ageing
1.3 INTRODUCTION
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Challenges of battery rental
� Financial risk of battery degradation supported by Renault
� Article 5.2.3 When the diagnosis performed reveals a level [ capacity ] less than the threshold above, the renter agrees - either to replace the battery - or repair the battery - or set up any other means necessary to overcome this reduction in capacity.
1.4 INTRODUCTION
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Conditions encountered by batteries : distance, temperature
1.5 INTRODUCTION
Representative temperatures in some cities
High
maximum
Temperature
Mean
maximum
Temperature
Mean
Temperature
Mean
minimum
Temperature
Low
minimum
Temperature
Very large diversity of usages and operating condit ions
Distribution (%) according to the yearly annual distance
Classes of distances (in km)
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Thermal Challenges
� Impacts of temperature on usage and battery life
1.6 INTRODUCTION
Challenges:
� Ensure vehicle performances over time
� Quantify precisely the degradation of the battery
Durability
Refroidir la batterie
Détérioration chimie batterie
0°C-20°C-30°C
Réchauffer la batterie
electricmachine
60°C
Maximum
Entretenir la thermique batterie
☺☺☺☺
Cool downbattery
degradationyBattery chemistry
0°C-20°C-30°C
Warm up the battery
Power
60°C
Maximum
Entretenir la thermique batterie
☺☺☺☺
Maintain theBattery temperature
☺☺☺☺
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� For the vehicle user
� Reduction of available energy
� Reduction of driving range
� Drop of performances
� Increase of energy consumption
Consequences of battery ageing
� For the Battery
� Capacity loss
� Self discharge
� Power loss
� Increase of internal resistance
1.7 INTRODUCTION
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� Needs:
� Rely on accurate ageing models to evaluate battery durability
� Integrate ageing into design phase
� Objectives : ensure profitability of the battery ec onomic model
� Determine the accurate value of the rental
� Adapt the rental cost to the usage
� Maximize battery durability
� Evaluate residual value for secondary life applicat ions
Objectives and needs
1.8 INTRODUCTION
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Approaches on ageing modeling
1.8 INTRODUCTION
� Need for simulation tools relying on measurements� High cost and difficulties to get data ( time of te sting > 2 years)
���� Collaborative work on battery ageing
� System approach
� Parameter Identification from measurements : chemis tries geometries already defined
� Objective : Behavior prediction for conditions not measured
� Reduced computation time, integration into vehicle simulation platforms
� No interpretation of parameter values according to chemistries
� Physico-Chemical approach
� Precise description of ageing mechanisms
� Partial differential equations, Newman Model
� Objective : Cell design (geometry, chemistry)
� Large computation time, parameters difficult to cal ibrate
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Collaborative projects on battery ageing
SIMSTOCK / SIMCAL
02
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SIMSTOCK – SIMCAL : Collaborative Projects on Battery Ageing
� Objectives
SIMSTOCK (2007-2011)
Cycling Ageing
3 technologies Li-ion
1 NiMH
3 SC
SIMCAL (2009-2012)
Calendar Ageing
6 technologies Li-ion
1 NiMH
Experimental Database Library of Models Ageing Mechanisms
HEV HEV / PHEV / EV
� Applications
2.1 Collaborative projects on battery ageing
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SIMSTOCK – SIMCAL : Collaborative Projects on Battery Ageing
� 15 partners
� 7 Industrial Partners� BATSCAP (Simstock)� EDF� LMS-Imagine� PSA Peugeot Citroën � Renault� SAFT� VALEO
� 7 Research Laboratories� CEA-Liten� EIGSI� IFP EN� IMS-Bordeaux� IFSTTAR (ex-INRETS)� LEC-UTC� LRCS-Amiens
� 1 Competitiveness Cluster� Pôle MTA
� Financial Support� ADEME� Programme PREDIT� ANR
Total Budget (€ million)
SIMSTOCK SIMCAL
4.2 3.6
2.2 Collaborative projects on battery ageing
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� Supercapacitors
SIMSTOCK : Batteries Tested
� Li-Ion Batteries � NiMH Batteries
SAFT VL6P 7 Ah SAFT NR6LG CHEM 5.3 Ah LIFEBATT 8 AhNCA NiMHLMO / NCA LFP
BATSCAPACN 2000F Un 2.7V
MAXWELL ACN 2000F Un 2.7V
BATSCAPPC 2600F Un 2.5V
2.3 Collaborative projects on battery ageing
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SIMCAL Batteries Tested
Supplier Chemistry Capacity Application Analysis
Tec-1 Saft VL6P C/NCA 7Ah HEV
Tec-2 Saft NR6 NiMH 6Ah HEV
Tec-3 Kokam polymer C/NMC 12Ah EV/ HEV Post-mortem
Tec-4 LGChem polymer LMO 5,3Ah HEV
Tec-5 Lifebatt C/LFP 8Ah HEV Post-mortem
Tec-6 Lifebatt C/LFP 15Ah EV Post-mortem
Tec-7 A123Systems C/LFP 2,3Ah Post-mortem
Tec-1 Tec-2 Tec-3 Tec-4 Tec-5 Tec-6 Tec-7
NCA NiMH NMC LMO/ NMC LFP
2.4 Collaborative projects on battery ageing
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SIMCAL Testing conditions
� State of Charge
� 3 Levels representing the usage of the vehicle
� Temperature
� 3 Levels
� Thermal cycling
Between 30°C and 45 °C
SOC1 SOC2 SOC330 65 100
State Of Charge (%)
T°1 T°2 T°330 45 60
Temperature (°C)
Thermal Cycling Calendar Ageing
20253035404550
0 4 8 12 16 20 24Time (h)
Te
mp
era
ture
(°C
)
2.5 Collaborative projects on battery ageing
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SIMCAL : Results
Common behavior for a same chemistry
Calendar Ageing mostly affected by Temperature and State of Charge
2.6 Collaborative projects on battery ageing
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� Literature review (Based on NREL 1 model)
(1) K. Smith & A. Pesaran, ECEN5017 Guest Lecture, september 2012
Capacity Fade Modeling
2.7 Collaborative projects on battery ageing
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� Model with simple behavior
Simple expression in 1/√t
Capacity Fade Modeling
2.8 Collaborative projects on battery ageing
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Improvement of the calendar Model
� Literature representation
R =
Q =
a1t1/2
QLi = d0+d1t1/2
QLi
Resistance Growth
Relative Capacity
Calendar Ageing
•SEI Growth
•Loss of cyclable lithium
•a1, d1 (SoC, T)
min( )
+
Capacity Loss
•Degradation of active material structure and mechanical fractures
•a2, e1 (SoC, T)
Qactive,
a2N+
Qactive,
Qactive = e0+e1N
2.9 Collaborative projects on battery ageing
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Improvement of the calendar Model
� SIMCAL representation
�Good Correlation between measures and model
� degradation of active material structure as a function of TIME
R =
Q =
a1t1/2
QLi = d0+d1t1/2
QLi
Resistance Growth
Relative Capacity
Calendar Ageing
•SEI Growth
•Loss of cyclable lithium
•a1, d1 (SoC, T)
min( )
a2t+
Capacity Loss
•Degradation of active material structure and mechanical fractures
•a2, e1 (SoC, T)
Qactive,
Qactive = e0+e1t
Better consideration of the parking mode in durability model
Cycling effect is not overestimated
2.10 Collaborative projects on battery ageing
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� Complex model
Combination of expression in 1/√t and linear expression
24
Capacity Fade Modeling
2.11 Collaborative projects on battery ageing
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Application to the other cells
� Same formalism, specific calibration for each refer ence
2.12 Collaborative projects on battery ageing
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� Capacity Loss coefficient = f(SOC, Temperature)
00.2
0.40.6
0.81
-20
0
20
40
60-0.02
0
0.02
0.04
0.06
SOC (%)
Capacity Loss Coefficient(-)
Temperature (°C)
Agi
ng C
oeff
icie
nt(-
)
-0.01
-0.005
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
Modeling of calendar ageing, capacity loss
New approach, more detailed representation of batte ry life
2.13 Collaborative projects on battery ageing
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Modeling of calendar ageing
� Open circuit Voltage
� Voltage vs Capacity after 100 days of aging
� Resistance
� Resistance ageing with respect to time
� A check up between 30 and 60 days
� Measure : dots
� Model : full line
� Check up 7 : Estimation after the model
2.14 Collaborative projects on battery ageing
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APPLICATION OF AGEING MODELS
Battery ageing scenario simulator
03
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Applications : Case study
� Application example :
� NEDC driving
� 3 trips per day, 12000 km/year
� Average yearly temperature 15 °°°°C
� Amplitude +/- 20 °°°°C (35°°°°C in summer,-5°°°°C in winter)
-10
-5
0
5
10
15
20
25
30
35
40
01/01/2012 01/03/2012 01/05/2012 01/07/2012 31/08/2012 31/10/2012 31/12/2012
Tem
pera
ture
(°C
)
Time (dates)
Yearly Temperatures seen by the battery
Average 15°C
Amplitude 20 °C
3.1 Application of ageing models
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Philippe GYAN Cellule LGCHEMDREAM / DELTA / 68580 kilométrage annuelloyen mensuelPhilippe GYAN Capacité initiale 0.90 10000 80
Vie initiale (mois) 0.00 Architecture batterie 20000 100
Choix du cycle 1 1 ECE * Dépendance de l'Energie à la température Capacité vieillie 0.71 Nombre cellules 30000 135
2 NEDC * alpha 1.75 Nombre de mois 84.46 192 Taux d'actualisationkilométrage réelloyer réel
3 Artemis Embouteillage a1 -0.2177 Coût pack brut 8000 nombre mois 1 Capacité individuelle Ah 12 1113.25 80
4 Artemis Urbain a2 -0.0121 Subvention véhicule 2000 nombre mois 2 28
5 Artemis Routier a3 1.1606 Coût pack net 6000
6 Artemis Autoroutier entier Coût location mensuelle 80 Mois Ans VieillissementRecharges%SOC/100kmLoyers actualisésEnergie nominale du trajet Energie initiale Recharge Coût total location 4162.92 12 1 0.8733 11 1.1380 844.8
Température moyenne. 0.099915098 26 Amortissement (€) -1837.08 24 2 0.8467 10 0.2392 743.424durée trajet secondes 195.00 15 Correction haut SOCEnergie déchargéeEnergie rechargée(Ah) coût location mini 115.30 36 3 0.8197 10 0.2421 654.21312distance km 1.02 Amplitude 5 0.1 1691.589794 1699.366673 nombre mois mini 180 48 4 0.7917 11 0.2515 575.7075456Vitesse moyenne (km/h) 18.76 Période 365.25 Correction Bas SOCDelta soc par trajet unique 0.0038 Capacité vieillie d'amortissement 0.000 60 5 0.7648 10 0.2421 506.6226401Accessoires W 400 Phase -90 0.1 72 6 0.7373 11 0.2469 445.8279233Coefficient Climatisation (W/°C) 20 crenaux 0 Mode Viei llissement Energie SOC min SOC MAX Soc initial Plage Soc Initiale 84 7 0.7111 12 0.2354 392.3285725Temperature habitacle 25 Température Max 60 1 0.15 0.8 0.3 0.65 96 8 0.6857 13 0.2281 345.2491438Gain temps usage climat mode parking (min) 10 Température Min 0 T° seuil haute 50 40 108 9 0.6609 14 0.2229 303.8192466Energie du trajet unique 0.100 Limite SOC T° haute 0.05 0.8 120 10 0.6366 15 0.2177 267.360937nombre trajets par jour 1 Offset temperature 0 Amplitude T° haute 0.01 0.01 132 960Nombre de trajets consécutifs 3 Début (mois) 0 T° seuil basse -20 -20 144 960Roulage 1 Fin (mois) 36 Limite SOC T° basse 0.25 0.79 0.242307692 156 960
Roulage week ends 1 Mode parking 0 Amplitude T°basse 0.01 0.01 168 960Roulage jours fériés 0 Température parking 20 Début dérive (mois) 12 64 180 960Km parcourus par an 1113.25 Pente dérive (SOC/an) 0.03 -0.02 192 960Temps roulage par an (heures) 59.313 Limite absolue 0.1 0.9 204 960% temps en roulage 2.000
Battery ageing estimation on a usage scenario
Choice of a driving profile
Driving Conditions
Annual Temperature
conditions
Energy = f(Temperature)
Business model
Charging conditions Capacity Losses
3.2 Application of ageing models
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Case N°°°°1 : no strategies
� SOC MAX : 0.95
� SOC min : 0.1
� Initial driving range : 140 km
� Final driving range : 130 km
� ���� End of life in 46 months
End of life
Time (months)
3.3 Application of ageing models
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Case N°°°°2 : Combinations of multiple strategies
� SOC MAX : 0.75
� SOC min : 0.1
� Initial driving range : 113 km
� Final driving range : 92 km
� ���� End of life in 83 months
End
of l
ife
3.4 Application of ageing models
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APPLICATION TO MAT4BAT MEASURES
KOKAM CELL
04
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1. Calendar ageing conditions in MAT4BAT project
� Experimental conditions and distribution of calendar tests
� 29 cells divided into 11 calendar conditions, 4 exp erimenters
� *4 conditions with autopsies (WP2)
� Capacity measurements in CC and CV conditions
SOC [%]
T [°C]50 90 100 Total
60 x2 |VITO x2 |VITO x3 |VITO 7
45 x2 | CEA x3 | CEA x3 | CEA 8
25 x3 | EIGSI X3 | EIGSI x3 | EIGSI 9
5 x2 |CIDETEC x3 |CIDETEC 5
Total 9 8 12 29
***
*
4.1 Application to MAT4BAT measures
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Capacity Calendar Ageing
� KOKAM Cell MAT4BAT SOC 100% CV Capacity
� 05 °°°°C
� 25 °°°°C
4.2 Application to MAT4BAT measures
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Capacity Calendar Ageing
� KOKAM Cell MAT4BAT SOC 100% CV Capacity
� 45 °°°°C
� 60 °°°°C
4.3 Application to MAT4BAT measures
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Capacity Calendar Ageing
� KOKAM Cell MAT4BAT SOC 100% CV Capacity
� Ageing Coefficient corresponds to a Arrhenius Behav ior
4.4 Application to MAT4BAT measures
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Capacity Calendar Ageing
� KOKAM Cell MAT4BAT CV Capacity
� Ongoing processing of Data at other SOC levels
� Sensitivity on initial capacity, and the initial ti me (T= 0 day)
� Non linear behavior of the degradation coefficient according to SOC
4.5 Application to MAT4BAT measures
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Capacity Calendar Ageing
� Calendar Ageing Simulation, SOC 100 %
� Variable temperature conditions• Daily variation : +/- 5°C• Yearly variation : +/- 10°C• Hourly time step, over 10 years• Average temperatures : 5°C, 25°C and 45 °C
4.6 Application to MAT4BAT measures
����Model applicable for any scenario study with variab le temperature���� Extension in process for all SOC range
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Resistance Ageing Modeling
� Discharge Resistance 10s, SOC 100, 45 °°°°C, initial state
� Resistance identification based on 10 measured valu es
4.7 Application to MAT4BAT measures
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Resistance Ageing Modeling
� Ageing Data available only for SOC 95, 90, 40, 20 a nd 5
� Resistance computed from these values
4.8 Application to MAT4BAT measures
Good matching between identification with 10 values , and identification with 5 values
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Resistance Ageing Modeling
� Resistance Ageing Data
� Irrelevant Data at SOC 5% : resistance decreasing w ith ageing
4.9 Application to MAT4BAT measures
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Resistance Ageing Modeling
� Resistance modeled during ageing using only SOC 95, 90, 40, 20
4.10 Application to MAT4BAT measures
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Resistance Ageing Modeling
� Calculation of the effective SOC for the measuremen t @ SOC 5%
High Sensitivity of Resistance to SOC Accuracy at low SO C
4.11 Application to MAT4BAT measures
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Resistance Ageing Modeling
� Futher work
� Processing of more data
� Check data consistency
� Determine model consistent with SOC and temperature
� Comparison with EIS measurements
� Comparison with cycling data
4.12 Application to MAT4BAT measures
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CONCLUSIONS AND PERSPECTIVES
05
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Conclusions
� Challenge of ageing modeling � Accurate ageing models are crucial to ensure profitability of electrified vehicles
� Warranty costs, product design
� Calendar Ageing model developed in previous project s� Ageing as function of time
� Same equations, specific calibrations for each battery
� Model from SIMCAL applicable to MAT4BAT� Capacity measured with Constant Voltage
� Further developments� Extensions to other SOC and temperatures
� Investigation of initial conditions and start time
� Accuracy and consistency in measures
� Coupling of calendar and cycling ageing
� Synergy on Methodologies with other projects.
5.1 Conclusions and Perspectives
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Other developments on Ageing Modeling : MOBICUS Project
� MOBICUS (2014-2017)
� MOdeling of Batteries Including the coupling between Calendar and USage ageing
� 16 partners, budget 4.3 M€
� Financial support of Conseil Général des Yvelines, CR Nord Pas de Calais, BPI France
� Project Leader : Renault
� Main objectives of the collaborative project :� To test , understand and model the coupling between Calendar and Usage ageing
� To integrate the battery models into a vehicle simulation platform
� To design and validate strategies enabling to extend battery life according to real vehicle usage
5.2 Conclusions and Perspectives
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Thank you for your attention !