用物理约束提升电池温度预测精度与速度
Physics-Informed Machine Learning for Pouch Cell Temperature Estimation
- 将热传导方程嵌入神经网络损失函数，融合物理规律与数据
- 相比纯数据模型，均方误差降低49.1%，收敛更快
- 适合电池热管理设计优化，尤其冷却通道外区域
具有间接液冷的袋式电池温度精确估计对交通电动化中的电池热管理系统优化至关重要。然而，由于有限元模拟计算成本高，且数据驱动模型存在局限性，实现起来颇具挑战。本文提出一种物理信息机器学习（PIML）框架，用于高效可靠地估计稳态温度分布。该方法将控制热传导的微分方程直接融入神经网络的损失函数中，使模型在显著更快速度下实现高保真预测，优于纯数据驱动方法。框架在不同冷却通道几何结构的数据集上进行评估，结果表明，PIML模型收敛更快，精度显著更高，相较数据驱动模型均方误差降低49.1%。独立测试案例验证进一步确认其优越性能，尤其在远离冷却通道的区域表现更佳。这些发现突显了PIML在电池系统代理建模与设计优化中的潜力。
原文摘要 · Abstract (English)
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.
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