Data-Driven Thermal Runaway Warning for Batteries: Research Progress and Prospects of Machine Learning Approaches

As lithium-ion batteries are widely deployed, thermal runaway (TR) poses severe safety risks, making early and accurate warning systems critical. While machine learning (ML) has advanced data-driven TR prediction, challenges remain regarding model interpretability, generalization under unseen conditions, and real-time deployment. This review evaluates recent progress in ML-driven TR warning technologies, moving beyond a mere compilation of algorithms to provide an organized synthesis of the field. As a key contribution, we critically analyze the paradigm shift toward physics-informed ML, demonstrating how embedding electrochemical and thermodynamic principles into neural networks reduces prediction errors by 40–60% while enhancing robustness. Furthermore, we synthesize a Battery Digital Twin (BDT) framework integrating Internet of Things (IoT), cloud computing, and on-board master BMS for closed-loop collaboration, effectively balancing low-latency control with high-precision health assessment. Finally, we outline strategic pathways for future breakthroughs: advancing physics-informed cross-scale modeling, optimizing cloud-edge architectures, and establishing open access benchmark databases. By calling for standardized evaluation protocols to break down data silos, this review provides a comprehensive roadmap and actionable insights to accelerate the industrial implementation of next-generation intelligent battery safety management.