Thermal runaway of lithium batteriesJulian Mehne, Felix Bode, Wolfgang Nowak, Universität Stuttgart...

Hochschule Offenburg University of Applied Sciences

Thermal runaway of lithium batteries

2006: DELL recalls 4 mio. laptop batteries

(China)

2013: Boeing dreamliner battery

May 2010: Hewlett Packard recalls 54.000 laptop batteries “Since the May 2009 recall, HP has received 38 additional reports of batteries that overheated and ruptured resulting in 11 instances of minor personal injury and 31 instances of minor property damage”

14.12.2011


15.02.2013

Wolfgang G. Bessler, Hochschule Offenburg

Nanako Tanaka, Deutsches Zentrum für Luft- und Raumfahrt, Stuttgart

Michael Danzer, Harry Döring, Zentrum für Sonnenenergie- und Wasserstoffforschung, Ulm

Julian Mehne, Felix Bode, Wolfgang Nowak, Universität Stuttgart

Project funded by VolkswagenStifung, 01.08.2011-31.07.2014


Motivation: Battery safety

2013: Boeing dreamliner battery

2006: DELL recalls 4 mio. laptop batteries

Material decomposition Heat

Thermal Runaway

Triggering event

Manufacturing (e.g. particles)

Dendrite formation

Internal short-circuit

Thermal runaway mechanism:

Crash

Over- charge

Over- discharge External

short-circuit

Over- heating

Runaway = Chemistry + Heat transport

[email protected] 15.02.2013


The highly energetic active materials are separated by a porous film that is thinner than a human‘s hair.

CT image of a Li-ion battery, DLR Stuttgart

Repeat unit Separator Cylindrical cell

150 µm 15-25 µm 2.6 cm

Arora & Zhang, Chem. Rev. 2004

Safety versus energy

15.02.2013 [email protected]



Li+e–

Microscale

Macroscale

3D thermal model

Electrochemistry & heating model

T TQ

Electro-chemical and micro-structural parameters

Thermal and macro-structural parameters

Trigger and runaway simulation

Monte Carlo

Stochastic para-meter variation

Scale interface

Experiments

Trigger of extreme events

Model validation

Model validation

Goal: Early-alert risk-aware battery management system Stochastic and optimal model-predictive control with constraints

Our approach


Li+e–

Microscale

Macroscale

3D thermal model

Electrochemistry & heat model

T TQ




Monte Carlo


Scale interface

Experiments


Model validation

Model validation

Microscale heat source model



Microscale model components

• Performance model for standard operating conditions

– Heat sources due to charge, discharge, cycling

Runaway trigger

• High-temperature degradation model

– Additional heat sources due to thermal decomposition reactions

Runaway chemistry



Performance model for standard operation

• Thermodynamics • Half-cell potential

• Kinetics • Butler-Volmer kinetics • Concentration overpotential

• Transport in particles • Mass conservation • Spherical diffusion

• Electrolyte transport • Nernst-Planck equation • Charge neutrality

• Cell voltage

−−−

= actact0)1(expexp ηαηα

RTF

RTFii

concLieqact )()( ηφ∆φ∆η −−= ct

( ) Viiii

iii

i sMy

cDyRT

FzycD

ytc +

∂∂

+

∂∂

=∂∂

∂∂

∂∂ φε

Diffusion Migration Chemistry

( ) 0=∑i

ii zc

=

)(ln 0

tcc

zFRT

concη

E = φcathode – φanode

zFcSTcH

zFGc )(Δ)(ΔΔ)( LiLi

Lieq−

−=−=φ∆

izFM

rDr

rrtLiLi2

2Li 1

−

∂∂

∂∂

=∂∂ ρρ

Diffusion Chemistry


Results: Discharge at various C-rates

• Different discharge (C) rates

• Relatively flat discharge curve, voltage variation mainly from C6 electrode

• Decrease of voltage and slight capacity loss upon increasing discharge rate

0.0 0.5 1.0 1.5 2.0 2.52.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

Experiment Simulation

Capacity [Ah]

0.1C 1C 2C 4.6C

Cell V

olta

ge [V

]

[email protected]

LiFePO4 cell

15.02.2013


Results: Discharge at various temperatures

• 1C discharge at different temperatures

• Loss of performance at decreasing temperature:

– Increasing polarization losses

– Decreasing capacity

• Good agreement between model and experiment

0.0 0.5 1.0 1.5 2.0 2.52.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

50°C 30°C 20°C 10°C 0°C -10°C -20°C

Cell V

olta

ge [V

]

Capacity [Ah]

Polarization losses

Capacity losses

[email protected]

LiFePO4 cell

15.02.2013


High-temperature degradation model

• There are a large number of potential high-temperature degradation reactions.

• Included so far in our model:

– Solid electrolyte interface (SEI) decomposition (CH2OCO2Li)2 Li2CO3 + C2H4 + CO2 + 0.5 O2

– SEI re-formation 2 C3H4O3 (EC) + 2 e– + 2 Li+ (CH2OCO2Li)2 + C2H4

– Electrolyte evaporation C3H4O3 (liquid) C3H4O3 (gas)

• Parameterization of thermodynamic and kinetic parameters performed



Solid electrolyte interface (SEI)

• What’s SEI (Solid Electrolyte Interface)? − Passivating layer between electrode and

electrolyte − Arises from the reductive decompositions

of a small amount of organic electrolytes − Composes mostly during the first several

cycles of a working cell

• Why SEI decomposition?

− Triggering event for reaction of electrolyte and electrode which possibly leads thermal runaway •http://www.cmt.anl.gov

US department of Energy

Graphite


http://www.cmt.anl.gov


Results: Heat sources upon cell heating

• Numerical experiment: Constant heat-up of cell, simulation of heat source (differential scanning calorimetry, DSC). Cell is assumed isothermal.

SEI decomposition

SEI re- formation and decomposition



Thermal runaway example simulation

• Runaway induced by thermal SEI decomposition:

(CH2OCO2Li)2 Li2CO3 + C2H4 + CO2 + ½ O2

• Self-accelarating reaction until SEI is completely consumed



Li+e–

Microscale

Macroscale

3D thermal model


T TQ




Monte Carlo


Scale interface

Experiments


Model validation

Model validation

Macroscale heat transport model



Modeling approach

• 3D, 2D and 1D model of single cell

• Solution of heat transport equation

• Boundary conditions: Convection and radiation

• Implementation in COMSOL


( ) ( ) TP QT

tTC +∇∇=

∂∂ λρ


Coupling the scales

• 6 representative points chosen for coupling of macroscale with microcale


Li+e–

Microscale

Macroscale

3D thermal model


T TQScale interface


Exemplary simulations

• Full 3D simulation compared to 1D simulation

• Here: Surface temperature vs. time

• Nominal operation, discharge in 1 h (1C rate)

• Complex temperature behavior


1D simulation

3D simulation


Li+e–

Microscale

Macroscale

3D thermal model


T TQ




Monte Carlo


Scale interface

Experiments


Model validation

Model validation

Stochastic modeling and analysis



Uncertainty and model error

● Parameter uncertainty

– Incomplete knowledge about model parameters

– For a “perfect” model: still uncertain results

● Model error

– Model per definition ≠ reality

– Structural error (wrong equations, wrong numerical implementation, …)

• Problem: Uncertain model → prediction and reality deviate increasingly with time

• Measurement errors

Concept: Measurement updates



𝑝 𝑥0,…,𝑡 𝑦0,…,𝑡 𝑝(𝑥0,…,𝑡|y0,…,t−1)

Complete sequential procedure (simplified):

Update of uncertain model predictions with measurements via Bayes‘ theorem:

System model: 𝑥𝑡 = 𝑓(𝑥𝑡−1,𝜇𝑡)

Measurement model: 𝑦𝑡 = 𝑔(𝑥𝑡, 𝜈𝑡)

𝑥𝑡 … model state at time t 𝑦𝑡 … measurement at time t 𝜇𝑡 …. model error 𝜈𝑡 … measurement error

𝑝(𝑥0,…,𝑡−1|𝑦0,…,𝑡−1)

𝑝 𝑥𝑡 𝑦0, … ,𝑦𝑡 =𝑝 𝑦0, … ,𝑦𝑡 𝑥𝑡 ⋅ 𝑝(𝑥𝑡)

𝑝 𝑦0, … ,𝑦𝑡

prediction

information loss

information gain

update

Solution of model equations with a particle filter: - Continuous probability density is discretized by particles (individual model runs) - Measurement update via reweighting of the particles

𝑡 = 𝑡 + 1


Approach: Bayesian filtering


Explanatory power of measurements

• How much does a measurement tell about a quantity of interest?

• Here: Prediction of cell core temperature as function of surface temperature for a number of simulation runs under uncertainty



Li+e–

Microscale

Macroscale

3D thermal model


T TQ




Monte Carlo


Scale interface

Model validation

Model validation

Experiments


Thermal runaway experiments



Investigated cells

• Three different commercial cell types investigated.

• Influence of cell chemistry and design on runaway propensity?



Types of cell tests

• General characterisation (weight, volume, capacity, power and energy densities)

• Nominal operation characteristics as function of temperature and charge/discharge rate

• Abuse experiments – External heating – Short circuit – Nail penetration – Overcharge



Exemplary results: Temperature during cycling

• Here: Sony cell



Nail penetration tests



Summary



Summary

• Thermal runaway is an outstanding example for an extreme event in a complex technical system

• Ongoing project using combined modeling and experimental approach

• Microscale model of heat sources and runaway chemistry

• Macroscale model of heat transport

• Stochastic model of model and measurement uncertainty

• Comprehensive experiments using three different battery types


Thank you for your attention!


15.02.2013

Wolfgang G. Bessler, Hochschule Offenburg

Nanako Tanaka, Deutsches Zentrum für Luft- und Raumfahrt, Stuttgart

Michael Danzer, Harry Döring, Zentrum für Sonnenenergie- und Wasserstoffforschung, Ulm

Julian Mehne, Felix Bode, Wolfgang Nowak Universität Stuttgart

Project funded by VolkswagenStifung, 01.08.2011-31.07.2014



Example simulation runs: Surface temperature update

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Thermal runaway of lithium batteriesJulian Mehne, Felix Bode, Wolfgang Nowak, Universität Stuttgart...

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