Review of machine learning method for safety management of lithium-ion battery energy storage

Abstract

With the broad implementation of electrochemical energy storage technology, the noteworthy issue of ensuring safe operation and maintenance of battery energy storage power plants has become more and more prominent. The conventional battery management system solely acquires data on the voltage, current, and temperature of individual battery cells. Constrained by hardware processing capabilities, limitations in data transmission bandwidth, and latency issues, effectively monitoring the health and safety of large-scale battery energy storage systems has become a critical technological challenge. The implementation of machine learning techniques in predicting the operating conditions of lithium-ion batteries has provided opportunities for enhancing the safety management of energy storage systems. To address the safety management requirements of lithium-ion batteries, this paper firstly introduces research related to the risk mechanism of abusive use and thermal runaway of such batteries. Next, the architecture and application characteristics of the lithium-ion battery management system will be discussed. The implementation of machine learning techniques for analyzing the health and safety status of lithium-ion batteries is extensively discussed. Finally, a safety assessment of lithium-ion batteries for energy storage power stations is anticipated.