Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities

Abstract

Real-time battery SOX estimation including the state of charge (SOC), state of energy (SOE), and state of health (SOH) is the crucial evaluation indicator to assess the performance of automotive battery management systems (BMSs). Recently, intelligent models in terms of deep learning (DL) have received massive attention in electric vehicle (EV) BMS applications due to their improved generalization performance and strong computation capability to work under different conditions. However, estimation of accurate and robust SOC, SOH, and SOE in real-time is challenging since they are internal battery parameters and depend on the battery’s materials, chemical reactions, and aging as well as environmental temperature settings. Therefore, the goal of this review is to present a comprehensive explanation of various DL approaches for battery SOX estimation, highlighting features, configurations, datasets, battery chemistries, targets, results, and contributions. Various DL methods are critically discussed, outlining advantages, disadvantages, and research gaps. In addition, various open challenges, issues, and concerns are investigated to identify existing concerns, limitations, and challenges. Finally, future suggestions and guidelines are delivered toward accurate and robust SOX estimation for sustainable operation and management in EV operation.