AI RESEARCH
Physics-Informed Machine Learning for Pouch Cell Temperature Estimation
arXiv CS.LG
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ArXi:2604.14566v1 Announce Type: new 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.