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This paper introduces a Physics-Informed LSTM (PI-LSTM) that incorporates heat transfer equations as a regularization term in the loss function to improve thermal runaway forecasting in lithium-ion batteries. By integrating physics-based constraints, the PI-LSTM ensures thermodynamic consistency and enhances generalization across diverse operating conditions. Experiments on 13 datasets demonstrate that PI-LSTM achieves an 81.9% reduction in RMSE and an 81.3% reduction in MAE compared to standard LSTM and outperforms other deep learning baselines.
Force-feeding physics to LSTMs slashes battery thermal runaway prediction errors by over 80%, making your next e-bike less likely to explode.
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.
