AI RESEARCH
Physics-Enhanced Deep Learning for Proactive Thermal Runaway Forecasting in Li-Ion Batteries
arXiv CS.LG
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ArXi:2604.20175v1 Announce Type: new 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.