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

Abstract

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 predictions. This is achieved by analysing features such as SOC, energy efficiency, and capacity decline. The IRF algorithm outperforms state-of-the-art methods such as Gradient Boosting and standard Random Forest, obtaining the lowest Root Mean Square Error of 1.575 and a R2 score of 0.9995. This demonstrates exceptional accuracy. Furthermore, the IRF model guarantees real-time adaptability and robust anomaly detection, with a classification accuracy of 99.99% and no false negatives. These developments facilitate proactive interventions, reduce operational risks, and extend battery life by a substantial margin. This innovative framework provides a comprehensive assessment of battery conditions by establishing a connection between empirical data analysis and theoretical modelling. The framework is positioned as a transformative solution for sustainable energy systems, in addition to addressing challenges in scalability and computational efficiency, as the research demonstrates. The results emphasize its potential as a critical tool for assuring reliability, safety, and longevity in contemporary lithium-ion battery applications.