Kinetic modelling of thermal decomposition in lithium-ion battery components during thermal runaway

TY - JOUR

T1 - Kinetic modelling of thermal decomposition in lithium-ion battery components during thermal runaway

AU - Sadeghi, Hosein

AU - Restuccia, Francesco

N1 - Publisher Copyright: © 2024 The Authors

PY - 2025/2/15

Y1 - 2025/2/15

N2 - This study presents kinetic models for the thermal decomposition of 18650-type lithium-ion battery components during thermal runaway, including the SEI layer, anode, separator, cathode, electrolyte, and binder. The decomposition kinetics were sourced from the literature. The approach used inverse modelling, employing a Genetic Algorithm to estimate kinetic and stoichiometric parameters. Experimental thermogravimetry data from the literature served as the reference benchmarks. The optimisation errors ranged from 0.039% to 1.531%, and the algorithm performed well in terms of reaction temperatures, with errors between 0.51% and 11.07%. The models were validated for calculating the mass loss of a full cell at 100% state of charge during thermal runaway. The early stages of thermal runaway, including the decomposition of the anode and separator, were considered in an electrochemical-thermal simulation of charge/discharge cycling using PyBaMM solver. The results showed that these decompositions could advance temperature and voltage profiles by 0.07 C over 20 cycles, aiding early prediction of thermal runaway in battery management systems. This work introduces novel models to calculate mass losses, identify reactions, quantify heat release, and estimate thickness or volume reductions in battery components during thermal runaway.

AB - This study presents kinetic models for the thermal decomposition of 18650-type lithium-ion battery components during thermal runaway, including the SEI layer, anode, separator, cathode, electrolyte, and binder. The decomposition kinetics were sourced from the literature. The approach used inverse modelling, employing a Genetic Algorithm to estimate kinetic and stoichiometric parameters. Experimental thermogravimetry data from the literature served as the reference benchmarks. The optimisation errors ranged from 0.039% to 1.531%, and the algorithm performed well in terms of reaction temperatures, with errors between 0.51% and 11.07%. The models were validated for calculating the mass loss of a full cell at 100% state of charge during thermal runaway. The early stages of thermal runaway, including the decomposition of the anode and separator, were considered in an electrochemical-thermal simulation of charge/discharge cycling using PyBaMM solver. The results showed that these decompositions could advance temperature and voltage profiles by 0.07 C over 20 cycles, aiding early prediction of thermal runaway in battery management systems. This work introduces novel models to calculate mass losses, identify reactions, quantify heat release, and estimate thickness or volume reductions in battery components during thermal runaway.

KW - Lithium-ion battery

KW - Thermal runaway

KW - Thermogravimetry

KW - Kinetic modelling

KW - Thermal decomposition

KW - Genetic algorithms

UR - https://www.sciencedirect.com/science/article/pii/S0378775324019785#d1e6778

UR - http://www.scopus.com/inward/record.url?scp=85211978797&partnerID=8YFLogxK

U2 - 10.1016/j.jpowsour.2024.236026

DO - 10.1016/j.jpowsour.2024.236026

M3 - Article

SN - 0378-7753

VL - 629

JO - JOURNAL OF POWER SOURCES

JF - JOURNAL OF POWER SOURCES

M1 - 236026

ER -