Research on Machine Learning Algorithm for Safety and Life Prediction of Lithium Battery Energy Storage System

In Wulanchabu Wind Power Base's first-phase 6 million kilowatts demonstration project, the 90MW/180MWh energy storage system planned by Honggeer No.4 Wind Farm is of great significance for improving power grid stability and high-quality development of new energy industry. This study focuses on the safety and life prediction of lithium battery energy storage system (ESS). By introducing machine learning (ML) algorithm, the operation data of ESS are deeply mined and analyzed, and an accurate and efficient safety evaluation and life prediction model is established. In the research, the safety analysis module adopts the support vector machine (SVM) algorithm, evaluates the safety state of the battery pack based on the working state data collected in real time, and triggers the early warning mechanism when the safety evaluation result is lower than the set threshold. The lifespan prediction model uses Long Short Term Memory (LSTM) networks, which comprehensively consider factors such as battery cycle times, usage environment, and charging and discharging strategies to predict the remaining lifespan of the battery. Through the analysis of actual cases, SVM model can accurately identify more than 90% potential security risks during the simulation experiment, while the prediction accuracy of LSTM model reaches 85%, which significantly improves the prediction accuracy. This study enhances the operational efficiency of ESS, lowers the failure rate, and offers substantial support for the sustainable growth of the new energy sector and the stable functioning of the power grid.