CALENDAR AGEING MODELING OF LITHIUM-ION BATTERIES · PDF filePROPERTY OF RENAULT GROUP C 1...

DEA-IREB Battery InnovationsService 68083Philippe GYAN June 4th, 2015

CONFIDENTIALPROPERTY OF RENAULT GROUP

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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




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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




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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




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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

)




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SIMCAL : Results

Common behavior for a same chemistry

Calendar Ageing mostly affected by Temperature and State of Charge




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� Literature review (Based on NREL 1 model)

(1) K. Smith & A. Pesaran, ECEN5017 Guest Lecture, september 2012

Capacity Fade Modeling




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� Model with simple behavior

Simple expression in 1/√t





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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




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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




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� Complex model

Combination of expression in 1/√t and linear expression

24





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Application to the other cells

� Same formalism, specific calibration for each refer ence




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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




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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




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APPLICATION OF AGEING MODELS

Battery ageing scenario simulator

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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




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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)




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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




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APPLICATION TO MAT4BAT MEASURES

KOKAM CELL

04



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Capacity Calendar Ageing

� KOKAM Cell MAT4BAT SOC 100% CV Capacity

� 05 °°°°C

� 25 °°°°C




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� 45 °°°°C

� 60 °°°°C




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� Ageing Coefficient corresponds to a Arrhenius Behav ior




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� 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




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� 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


��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




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� Ageing Data available only for SOC 95, 90, 40, 20 a nd 5

� Resistance computed from these values


Good matching between identification with 10 values , and identification with 5 values



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� Resistance Ageing Data

� Irrelevant Data at SOC 5% : resistance decreasing w ith ageing




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� Resistance modeled during ageing using only SOC 95, 90, 40, 20




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� Calculation of the effective SOC for the measuremen t @ SOC 5%

High Sensitivity of Resistance to SOC Accuracy at low SO C




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� Futher work

� Processing of more data

� Check data consistency

� Determine model consistent with SOC and temperature

� Comparison with EIS measurements

� Comparison with cycling data




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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 !

CALENDAR AGEING MODELING OF LITHIUM-ION BATTERIES · PDF filePROPERTY OF RENAULT GROUP C 1...

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