Physics-Informed Machine Learning for Pouch Cell Temperature Estimation

TL;DR

This paper introduces a physics-informed machine learning framework that integrates heat transfer equations into neural networks to accurately and efficiently estimate pouch cell temperatures, outperforming traditional data-driven models.

Contribution

The novel PIML approach combines physics equations with neural networks, achieving faster convergence and higher accuracy in temperature prediction for battery cells.

Findings

49.1% reduction in mean squared error compared to data-driven models

Faster convergence and higher accuracy in temperature estimation

Effective validation on independent test cases

Abstract

Accurate temperature estimation of pouch cells with indirect liquid cooling is essential for optimizing battery thermal management systems for transportation electrification. However, it is challenging due to the computational expense of finite element simulations and the limitations of data-driven models. This paper presents a physics-informed machine learning (PIML) framework for the efficient and reliable estimation of steady-state temperature profiles. The PIML approach integrates the governing heat transfer equations directly into the neural network's loss function, enabling high-fidelity predictions with significantly faster convergence than purely data-driven methods. The framework is evaluated on a dataset of varying cooling channel geometries. Results demonstrate that the PIML model converges more rapidly and achieves markedly higher accuracy, with a 49.1% reduction in mean squared error over the data-driven model. Validation against independent test cases further confirms its superior performance, particularly in regions away from the cooling channels. These findings underscore the potential of PIML for surrogate modeling and design optimization in battery systems.