Battery Safety: Mechanisms, Monitoring, and Machine Intelligence

ABSTRACT Batteries constitute the foundation of electronic devices and electrified transportation. Nevertheless, aging and sudden faults can precipitate thermal runaway, making battery safety a global concern. This review elucidates failure triggering and evolution from the perspectives of multiphysics coupling and multiscale failure propagation, with emphasis on chemistry‐specific heterogeneity in next‐generation battery systems. To mitigate these risks, intrinsic‐safety materials and structural designs are systematically examined, together with a graded evaluation of their maturity. System‐level active protection is further discussed, highlighting the role of cloud‐based Battery Management Systems in data governance and cloud‐edge collaborative monitoring and control. Building on this architecture, an artificial intelligence‐empowered monitoring and control framework is synthesized across four dimensions: (1) perception, which uses multimodal fusion to overcome the limitations of single‐variable monitoring and enable holistic mapping of internal states; (2) algorithms, which adopt data‐efficient paradigms such as self‐supervised learning to address data scarcity in extreme fault scenarios; (3) mechanisms, which integrate physics‐informed neural networks and digital twins to enhance interpretability and physical consistency; and (4) deployment, which leverages edge computing and federated learning to enable cloud‐edge collaboration and swarm intelligence under privacy constraints. Finally, this review outlines prospects for next‐generation safety testing standards, autonomous closed‐loop safety management, self‐healing technologies, and cross‐domain safety management.