Hybrid machine learning framework for predictive maintenance and anomaly detection in lithium-ion batteries using enhanced random forest

The critical necessity for sophisticated predictive maintenance solutions to optimize performance and extend lifespan is underscored by the widespread adoption of lithium-ion batteries across industries, including electric vehicles and energy storage systems. This study introduces a comprehensive predictive maintenance framework that incorporates real-time health diagnostics with state-of-charge (SOC) estimation, utilizing an Improved Random Forest (IRF) algorithm to address the current limitations in battery management systems. The framework integrates physics-informed methodologies with data-driven machine learning models to facilitate the dynamic assessment of battery health and the production of precise…