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This paper introduces a physics-informed machine learning (PIML) framework to estimate steady-state temperature profiles in pouch cells with indirect liquid cooling, addressing the limitations of purely data-driven models and computationally expensive finite element simulations. The PIML approach incorporates heat transfer equations into the neural network's loss function, improving prediction accuracy and convergence speed. Empirical results demonstrate a 49.1% reduction in mean squared error compared to data-driven models, highlighting PIML's potential for battery thermal management system design and optimization.
Physics-informed machine learning slashes temperature estimation errors in pouch cells by nearly 50%, outperforming purely data-driven models.
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.