基于物理的深度学习用于锂离子电池主动热失控预测
Physics-Enhanced Deep Learning for Proactive Thermal Runaway Forecasting in Li-Ion Batteries
AI总结：
本文提出一种融合热传导方程的PI-LSTM框架，通过物理约束提升预测精度，实验显示其在RMSE和MAE上分别减少81.9%和81.3%。
摘要：
准确预测锂离子电池热失控对现代储能系统安全至关重要。传统数据驱动方法如LSTM虽能捕捉复杂时间依赖性，但违反热力学原理导致物理不一致。相反，基于物理的热模型具有可解释性但计算成本高且难以实时应用。为此，本文提出一种将热传导方程直接整合到深度学习架构中的物理信息LSTM（PI-LSTM）框架，通过损失函数中的物理约束项实现。模型利用多特征输入序列（包括SOC、电压、电流、机械应力和表面温度）预测电池温度演变并施加热扩散约束。在十三个锂离子电池数据集上的实验表明，PI-LSTM相比标准LSTM基准在RMSE和MAE上分别减少81.9%和81.3%，并显著优于CNN-LSTM和多层感知机（MLP）模型。物理约束的引入增强了模型在多样化运行条件下的泛化能力并消除了非物理温度振荡。这些结果证实了基于物理的深度学习在下一代电池系统中实现可解释、准确和实时热管理的可行性。
英文摘要：
Accurate prediction of thermal runaway in lithium-ion batteries is essential for ensuring the safety, efficiency, and reliability of modern energy storage systems. Conventional data-driven approaches, such as Long Short-Term Memory (LSTM) networks, can capture complex temporal dependencies but often violate thermodynamic principles, resulting in physically inconsistent predictions. Conversely, physics-based thermal models provide interpretability but are computationally expensive and difficult to parameterize for real-time applications. To bridge this gap, this study proposes a Physics-Informed Long Short-Term Memory (PI-LSTM) framework that integrates governing heat transfer equations directly into the deep learning architecture through a physics-based regularization term in the loss function. The model leverages multi-feature input sequences, including state of charge, voltage, current, mechanical stress, and surface temperature, to forecast battery temperature evolution while enforcing thermal diffusion constraints. Extensive experiments conducted on thirteen lithium-ion battery datasets demonstrate that the proposed PI-LSTM achieves an 81.9% reduction in root mean square error (RMSE) and an 81.3% reduction in mean absolute error (MAE) compared to the standard LSTM baseline, while also outperforming CNN-LSTM and multilayer perceptron (MLP) models by wide margins. The inclusion of physical constraints enhances the model's generalization across diverse operating conditions and eliminates non-physical temperature oscillations. These results confirm that physics-informed deep learning offers a viable pathway toward interpretable, accurate, and real-time thermal management in next-generation battery systems.
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