Physics-Informed Neural Network-Based Modeling and Prediction of Electrochemical-Thermal Coupled Thermal Runaway in Lithium-ion Batteries

Authors:
Open Access
Publisher website
Google Scholar
Abstract
The safety of lithium-ion batteries is a critical bottleneck restricting their further development in the fields of new energy vehicles and energy storage, with safety incidents caused by thermal runaway being particularly severe. To achieve accurate prediction of the electro-thermal behavior of lithium-ion batteries under normal and extreme operating conditions, this study proposes an electrochemical-thermal coupled modeling method based on a Physics-Informed Neural Network (PINN). This method first establishes an electrochemical-thermal coupled mechanistic model that combines the Single Particle Model (SPM) with a lumped thermal model. To address the challenge of sparse experimental data on thermal runaway, we used COMSOL Multiphysics to generate simulation data under multiple operating conditions and combined it with partial high-fidelity experimental data to form a hybrid training dataset. Based on this, a dual-channel PINN architecture consisting of an electrochemical network and a thermal network was designed, embedding the governing partial differential equations of the coupled model into the neural network’s loss function as a physical constraint, thereby achieving a deep fusion of model-driven and data-driven approaches. Finally, the model’s performance was validated through conventional charge-discharge experiments at different C-rates and heating-induced thermal runaway experiments at various states of charge (SOC). The results show that the proposed model can predict the battery’s voltage and temperature responses with high accuracy under both normal charge-discharge and extreme thermal runaway conditions, demonstrating excellent robustness and high fidelity.

This publication has 10 references indexed in Scilit: