Next-generation battery safety management: machine learning assisted life-time prediction and performance enhancement

Abstract

Batteries play a crucial role in the storage and application of sustainable energy, yet their inherent safety risks are non-negligible. Traditional monitoring methods often suffer from high costs, time consumption, and limited scalability, making it increasingly difficult to meet the evolving demands of modern society. In this context, recent advancements in machine learning technology have emerged as a promising solution for predicting and monitoring battery states, offering innovative approaches to battery management systems (BMS). By transforming raw operational data into actionable insights, machine learning has shifted the paradigm from reactive to predictive battery safety management, significantly enhancing system reliability and risk mitigation capabilities. This review delves into the implementation of machine learning in battery state prediction, including dataset selection, feature extraction, and model training. It also highlights the latest progress of these models in key applications such as state of health (SOH), state of charge (SOC), thermal runaway warning, fault detection, and remaining useful life (RUL). Finally, we critically examined the challenges and opportunities associated with leveraging machine learning to improve battery safety and performance, providing a comprehensive perspective for future research in this rapidly advancing field.