Virtual Battery Simulation Tools for Engineering

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Virtual Battery – Simulation Tools for

Engineering

Electrical and thermal modeling of lithium-ion batteries

ECV National Seminar, Espoo Ari Hentunen

Aalto University School of Electrical Engineering, Espoo, Finland 24.9.2014

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Simulation Tools for Engineering

Electrical and Thermal Characterization of Batteries

User need

Battery characterization Dynamic simulations of xEVs Battery performance assessment at end-of-life

Battery emulation at powertrain testbed

Users

Drive train developers Battery system developers Vehicle software developers

Objectives

SOC range of interest: 10–100 % Voltage error less than 2 %

Temperature error less than 2◦C

Computationally lightweight Automated model extraction

Experiments with acell,

module, or pack

Offline time-series I–U–T data

Evaluation of capacity and impedance at end-of-life

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Battery Model

Two Submodels

Inputs

ib Battery current Ta Ambient temperature sQ

h State of health (capacity)

sP_h State of health (power)

Outputs

sQ State of charge u_b Battery voltage uoc Open-circuit voltage Pgh Generated heat P_dh Dissipated heat T Battery temperature Electrical model Thermal model ib sQh sP h Ta Pgh sQ ub uoc T Pdh

Virtual Battery – Simulation Tools for Engineering 3/17

A. Hentunen 24.9.2014

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Electrical Model

Equivalent Circuit

Inputs

Current and temperature

External

Prediction of usable capacity, SOC, OCV, self-discharge, and generated heat

Equivalent circuit

Prediction of terminal voltage

Remark

Resistances and capacitances are functions of the SOC, temperature, rate, current direction, and SOH

− + uoc R0 + u0 ₋ R1 + u1 ₋ C1 Rn + un ₋ Cn ib + ub − ub Battery voltage ib Battery current uoc Open-circuit voltage R0 Ohmic resistance Rn Dynamic resistances Cn Dynamic capacitances

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Electrical Characterization Test

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 −20 0 20 40 60 Time [h] C u rr en t [A ] Current 0 1 2 3 4 5 6 7 8 9 10 11 12 13 −80 −40 0 40 80 Time [h] C u rr en t [A ] Current 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1510 15 20 25 30 35 T em p er a tu re [ ◦C ] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 22 24 26 28 30 Time [h] V o lt a g e [V ] Exp voltage Exp temperature 0 1 2 3 4 5 6 7 8 9 10 11 12 1310 15 20 25 30 35 T em p er a tu re [ ◦C ] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 20 22 24 26 28 30 Time [h] V o lt a g e [V ] Exp voltage Exp temperature

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Model Extraction Procedure

1 0 60 120180240300360420480540600660 −40 0 40 80 120 160 200 C u rr en t [A ] 0 60 120180240300360420480540600660 300 320 340 360 380 400 420 Time [min] V o lt ag e [V ] Exp voltage Exp current 19200198002040021000216002220022800 −40 0 40 80 120 C urr ent[A ] 19200198002040021000216002220022800 345 350 355 360 365 Time [min] V oltage[V ] Exp voltage Exp current 0 10 20 30 40 50 60 70 80 90 100 300 320 340 360 380 400 420 SOC [%] V olt ag e [V ] OCV OCV 0 10 20 30 40 50 60 70 80 90 100 0 100 200 300 400 500 SOC [%] R es is ta n ce [m Ω ] R1 R2 R0 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 SOC [%] C a pa ci ta n ce [k F ] CC12× 50 − + uoc R0 + u0₋ R1 + u1₋ C1 Rn + un₋ Cn ib + ub −

Experiments Parameter maps Parameterized model

1_{Ari Hentunen, Teemu Lehmuspelto, and Jussi Suomela. “Time-Domain Parameter}

Extraction Method for Thévenin Equivalent Circuit Battery Models”. In: IEEE Trans-actions on Energy Conversion 29.3 (2014), pp. 558–566.

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Heat Generation

P_gh₌¡ uoc−ub ¢ i_b | {z } polarization heat +i_bT∂uoc ∂T | {z } entropic heat =P_ph₊P_Sh ib Battery current u_b Battery voltage uoc Open-circuit voltage T Temperature

∂uoc/∂T-mapping can be obtained from literature or from an entropy

change characterization test2

Entropic heat can be either positive or negative

At charge sustaining applications entropic heat can be neglected due to zero net effect

2_{K. Jalkanen, T. Aho, and K. Vuorilehto. “Entropy change effects on the thermal}

behav-ior of a LiFePO4/graphite lithium-ion cell at different states of charge”. In: Journal of Power Sources 243 (2013), pp. 354–360.

A. Hentunen 24.9.2014

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Thermal Model

Equivalent Circuit

Inputs

Generated heat and ambient temperature

Equivalent circuit

Prediction of surface temperature and dissipated heat

Remark

Resistances and capacitances have constant values Pgh + T1 − C1 R1 + θ1_{− P}2 C2 + T2 − R2 + θ2₋ Pn Cn + Tn − Rn + θn_{− P}dh − + Ta P_gh Generated heat Ta Ambient temperature

T Battery surface temperature

Pdh Dissipated heat

θn Temperature rises

Rn Thermal resistances

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Thermal Characterization Test

0 2 4 6 8 10 12 14 16 18 20 −40 −20 0 20 40 Time [min] C u rr en t [A ] Current 0 2 4 6 8 10 12 14 16 18 20 −60 −40 −20 0 20 40 60 Time [h] C u rr en t [A ] 0 2 4 6 8 10 12 14 16 18 20 24 26 28 30 32 34 36 T em p er a tu re [ ◦C ] Current Temperature

Equivalent-circuit parameters can be extracted from the thermal characterization test

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Aging Effect

End-of-life (EOL) criteria for a battery

3

Capacity fade of 20 %, resulting in 80 % of the original capacity Power fade of 20 %, resulting in 80 % of the original power and 25 % increase in impedance

These effects can be included into the usable capacity and equivalent circuit resistances to assess electrical and thermal performance at end-of-life conditions

3_{Electric Vehicle Battery Test Procedures Manual, Revision 2. United States Advanced}

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Battery

Specification: Kokam SLPB 40 Ah (NMC), 26 V Module

Table :Specification of the battery.

Property Unit Cell∗ _Cell _Module

Nominal capacity Ah 40 40 40 Nominal voltage V 3.70 3.70 25.90 Max voltage V 4.20 4.15 29.05 Cut-off voltage V 2.70 3.00 21.00 Charge current A 120 120 120 Discharge current A 320 320 320 Energy kWh 0.15 0.15 1.04 Nominal temperature ◦C 25 25 25

Max temp. (charge) ◦C 45 45 45

Max temp. (discharge) ◦C 60 55 55

Cycle life @ 80 % DOD 1 200 1 200 1 200

A. Hentunen 24.9.2014

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Model Validation

Thermal Characterization Test

0 2 4 6 8 10 12 14 16 18 20 25 25.5 26 26.5 Time [h] V o lt a g e [V ] Exp voltage Sim voltage 0 2 4 6 8 10 12 14 16 18 20 25 25.5 26 26.5 Time [min] V o lt a g e [V ] Exp voltage Sim voltage 0 2 4 6 8 10 12 14 16 18 20 20 25 30 35 40 Time [h] T em p er a tu re [ ◦C ] Exp temperature Sim temperature Amb temperature 0 2 4 6 8 10 12 14 16 18 20 20 25 30 35 40 Time [min] T em p er a tu re [ ◦C ] Exp temperature Sim temperature Amb temperature

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Model Validation

Constant-Current Charge @ 25 °C / (C/3) 0 1 2 3 4 5 6 −40 −20 0 20 40 Time [h] C u rr en t [A ] Current 0 1 2 3 4 5 6 −40 −20 0 20 40 Time [h] C u rr en t [A ] Current 0 1 2 3 4 5 6 20 22 24 26 28 30 Time [h] V o lt a g e [V ] Exp voltage Sim voltage 0 1 2 3 4 5 6 24 26 28 30 32 Time [h] T em p er a tu re [ ◦C ] Exp Sim without entropic heat Sim with entropic heat

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Performance Assessment

Real-World Duty-Cycle / 25 °C 0 60 120 180 240 300 360 420 −30 −20 −10 0 10 20 30 T i me [ s ] P o w e r [k W ] P owe r 30 35 40 T em p er a tu re [ ◦C ] Exp

Sim without entropic heat Sim with entropic heat

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Performance Assessment at End-of-Life

Real-World Duty-Cycle 0 1 2 3 4 20 22 24 26 28 30 Time [h] V o lt a g e [V ]

Exp with SOH(P&Q) = 100 % Sim with SOH(P&Q) = 100 % Sim with SOH(P&Q) = 80 %

0 1 2 3 4 25 30 35 40 Time [h] T em p er a tu re [ ◦C ]

Exp with SOH(P&Q) = 100 % Sim with SOH(P&Q) = 100 % Sim with SOH(P&Q) = 80 %

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Conclusion

Electrical and thermal battery model was presented and parameterized for a Li-ion battery module with NMC chemistry

Automated parameter extraction from experimental I–U–T time-series data

Cell-, module-, or pack-level experiments

Electrical and thermal performance can be assessed Cooling requirement can be assessed

Performance degradation due to aging can be evaluated

Entropic heat generation is significant for charge depleting (CD) cycles Heat generation and dissipation must be balanced

Battery module performance was evaluated for a specific duty-cyle

Thermal performance was poor → Cooling would be needed

Model can also be used in conjunction with a battery cycler to emulate a battery in full-scale hardware-in-the-loop testing to accelerate powertrain development

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Summary

Key findings of the research

A systematic method for empirical electrical and thermal characterization of a battery cell, module, and pack was developed, in which also performance degradation due to aging can be evaluated.

Benefits for participating companies

Knowledge and methods for battery-system development and battery performance assessment.

New business opportunities

Battery characterization and emulation services aimed for machine manufacturers and battery-system integrators.

A. Hentunen 24.9.2014