Performance centric review of machine learning techniques for electric vehicle powertrain with battery management and charging systems

The rapid proliferation of electric vehicles (EVs) necessitates advanced intelligent systems to manage their increasingly complex subsystems ranging from powertrain optimization to battery management and charging infrastructure. Machine learning (ML) has emerged as a transformative tool capable of addressing these challenges through data driven prediction, adaptive control, and intelligent decision making. However, existing reviews are still disjointed, broadly limited to point subsystems, and deficient in standardized performance assessment, unified benchmarking, and analytical understanding of algorithmic appropriateness. These limit the comparability, interpretability, and real world implementation of machine learning models in electric vehicle infrastructure. This review uniquely remedies these shortcomings in a performance oriented, cross domain synthesis of machine learning deployments for electric vehicle design, battery systems, and charging infrastructure. Systematically examining 240 peer reviewed publications based on unified statistical metrics like Root Mean Square Error (RMSE) and Coefficient of Determination (R2), it provides a quantitative benchmarking framework linking algorithmic groups to subsystem performance. The analysis reveals that hybrid deep learning models such as Long Short Term Memory with Double Deep Q-Network (LSTM-DDQN), Convolutional Neural Networks-Bidirectional Gated Recurrent Units (CNN-BiGRU), and transformer based frameworks consistently achieve superior accuracy (R2 > 0.95, RMSE < 0.05) compared to traditional algorithms. The paper also clarifies the rationale behind why ensemble hybrid deep learning models systematically outperform conventional methods, thus laying the groundwork for subsystem variant optimization and deployment tactics. Along with consolidation, the survey brings to focus crucial open research issues that include dataset standardization, interpretable models, interface to Controller Area Network (CAN)/On-Board Diagnostics (OBD) communication protocols, and federated learning towards ensuring privacy in electric vehicle networks. Overall, this work advances the field by turning disjoint literature into an analytically informed guide to intelligent, interpretable, and scalable electric vehicles. This review concludes by identifying promising future directions such as explainable artificial intelligence (XAI), digital twins, embedded ML, and federated autonomy that will underpin the next generation of intelligent, scalable, and sustainable EV ecosystems.