Key technologies for safety status monitoring and intelligent early warning of lithium-ion battery energy storage stations

The deployment of lithium-ion batteries in energy storage systems (ESS) has raised concerns about operational safety, especially under complex, long-term conditions. This review explores key technologies for safety monitoring and intelligent early warning in lithium-ion battery ESS. It examines multi-physics failure mechanisms, including mechanical, electrical, thermal abuse, and internal defects, emphasizing their interrelated evolution and internal short circuits triggering thermal runaway. Advances in multi-physical sensing techniques—such as electrical, thermal, gas, and acoustic monitoring—are summarized. The paper also compares model-driven, data-driven, and hybrid fault diagnosis approaches, including electrochemical models, machine learning, and physics-informed networks. A unified safety monitoring framework, incorporating sensing, cloud-edge collaboration, and multi-level responses, is proposed. Future trends, including sensor fusion, intrinsic safety, fault data standardization, and AI-enabled diagnosis, are also discussed.