Physics-Informed Machine Learning for Pouch Cell Temperature Estimation

2026-04-15

AI Summary

This study addresses the challenge of efficiently and accurately estimating the temperature field of pouch cells under indirect liquid cooling conditions. To this end, a physics-informed machine learning approach is proposed, which embeds the governing heat conduction equation into the loss function of a neural network, thereby constructing a hybrid model that synergistically combines physical constraints with data-driven learning. Compared to purely data-driven methods, the proposed framework substantially reduces reliance on training data, accelerates convergence, and enhances prediction accuracy—particularly in regions distant from the cooling channels. Independent testing demonstrates a 49.1% reduction in mean squared error, underscoring the model’s superior generalization capability and reliability.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Human-in-the-loop Machine Learning
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Security and privacy of machine learning and AI applications

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.

Problem

• pouch cell
• temperature estimation
• battery thermal management
• indirect liquid cooling
• steady-state temperature

Innovation

• Physics-Informed Machine Learning
• Temperature Estimation
• Battery Thermal Management
• Surrogate Modeling
• Heat Transfer Equations