Abstract

The escalating adoption of Electric Vehicles (EVs) necessitates accurate and reliable methods for predicting battery health, a critical factor for vehicle safety and longevity. Battery degradation significantly impacts driving range, underscoring the need for predictive analysis. This study comprehensively reviews Machine Learning (ML) models applied to battery health prediction. ML approaches, including regression, Support Vector Machines, and Random Forests, utilize data-driven models to capture complex degradation patterns. Deep learning models, such as convolutional and recurrent neural networks, offer enhanced feature extraction capabilities for high-dimensional and accurate modeling. Furthermore, Digital Twin technology is reviewed for its capacity to integrate real-time data with virtual models to dynamically simulate and forecast degradation behavior. This review highlights the strengths and complementary nature of these techniques. It also addresses current challenges, such as data availability and computational demands, while offering insights into future research directions. The synergy of these ML-based approaches offers transformative opportunities for advancing predictive battery management systems, supporting the widespread adoption and reliability of EVs.