Advanced Battery Management: Forecasting Health, State of Charge & Maintenance Needs Using AI & ML Models (LSTM, Gradient Boosting, SVR, Random Forest)

Abstract

The rapid expansion of renewable energy sources and the widespread adoption of electric vehicles underscore the critical demand for efficient energy storage systems. This conference paper explores cutting-edge predictive models tailored for forecasting battery health, State of Charge (SOC), and anticipating maintenance requirements. Employing advanced machine learning [1,2] techniques, innovative feature engineering, and rigorous evaluation metrics, the study achieves robust performance in predicting key aspects of battery behavior. Key methodologies include Stacked LSTM networks, Random Forests, Gradient Boosting, and SVR. Alongside advanced time series analysis methods like ARIMA and SARIMA. The results demonstrate significant advancements in SOC prediction accuracy and provide valuable insights into overall battery health assessment. The models effectively identify potential maintenance needs, representing a substantial integration of machine learning [1,2] and time series analysis for enhanced battery management. These developments hold profound implications for energy storage and management, benefiting industries reliant on energy-intensive processes such as manufacturing, IT Infrastructure & Data Centers etc. They optimize energy usage, reduce costs, and enhance service efficiency and uptime in the retail sector, particularly for electric vehicle servicing. This research underscores the transformative impact of advanced predictive modeling on energy storage and management, supporting sustainable practices and fostering innovation across industries.