Role of deep learning in battery management system (BMS) for electric vehicles – A review

Electric vehicles (EVs) have received rapid development that has put more pressure on the development of advanced Battery Management System (BMS) to be safer, reliable and have extended performance. Nonlinear dynamics, the presence of a variety of loads, and real-time adaptability is poorly suited to traditional BMS approaches, including Coulomb counting, Kalman filters and ECMs, resulting in poor state estimation and fault detection capabilities. Recent developments in Deep learning (DL) represent a potentially transformative approach since, by modelling complex electrochemical and thermal processes in response to raw sensor data, sophisticated modelling becomes possible. This review gives a comprehensive discussion of applications of DL based BMS, with further detail on State of Charge (SOC), State of Health (SOH) estimation, Remaining Useful Life (RUL) prediction, fault detection, thermal management and energy optimisation. A comparative contrast of LSTM, Convolutional Neural Networks (CNN), DNN and GRU-based models as well as the reinforcement learning (RL) methods, is also provided, with the experience of industrial applications on Tesla, BMW, CATL, and Bosch. Among the critical concerns are the scarcity of data, its interpretability, computational load, and safety certification. The review also discusses emerging solutions such as hybrid physics-informed neural networks (PINNs), federated learning, Timmy, and explainable AI as potential future directions for scalable, transparent and regulation-compliant BMS. It highlights the fact that in addition to improving the prediction accuracy, DL enables autonomous, adaptive and next-generation EV battery systems. • Surveys in deep learning to SOC, SOH, and RUL, fault detection, and thermal management in BMS. • Comparisons between LSTM, CNN, GRU, DNN, and hybrid models based on the accuracy and cost of computation. • Associates the performance of academic models with performance in industries and safety concerns. • Recognizes data, interpretability, and regulatory issues of the adoption of AI-based BMS. • Provides such future directions as hybrid models, TinyML, federated learning, and XAI.