Physics-Informed Machine Learning for Pouch Cell Temperature Estimation

arXiv cs.LG / 4/17/2026

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Key Points

The study targets accurate pouch cell temperature estimation under indirect liquid cooling, which is important for optimizing battery thermal management in transportation electrification.
It proposes a physics-informed machine learning framework that embeds governing heat-transfer equations into the neural network loss function to improve reliability and efficiency.
Compared with purely data-driven models, the PIML approach converges faster and delivers substantially higher accuracy, achieving a 49.1% reduction in mean squared error.
Evaluations on multiple cooling channel geometries and validation on independent test cases confirm strong generalization, especially in areas farther from the cooling channels.
The authors suggest the method could enable efficient surrogate modeling and support battery thermal design optimization.
Point 1

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.