Physics-Enhanced Deep Learning for Proactive Thermal Runaway Forecasting in Li-Ion Batteries

2026-04-22

AI Summary

This study addresses the limitations of conventional data-driven approaches, which often violate thermodynamic principles in predicting lithium-ion battery thermal runaway, and pure physics-based models, which incur high computational costs and hinder real-time deployment. To bridge this gap, the authors propose a physics-informed long short-term memory network (PI-LSTM) that uniquely embeds the heat conduction equation directly into the LSTM loss function as a regularization term. By integrating multidimensional time-series features—including state of charge, voltage, current, mechanical stress, and surface temperature—the model preserves the expressive power of deep learning while enforcing physical consistency. Experiments on 13 battery datasets demonstrate that PI-LSTM reduces RMSE and MAE by 81.9% and 81.3%, respectively, compared to standard LSTM, significantly outperforming CNN-LSTM and MLP baselines while effectively suppressing non-physical oscillations and enhancing generalization.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• thermal runaway
• lithium-ion batteries
• physics-informed learning
• deep learning
• thermal modeling

Innovation

• Physics-Informed Deep Learning
• Thermal Runaway Prediction
• PI-LSTM
• Battery Thermal Management
• Thermodynamic Constraints