Review of battery state estimation methods for electric vehicles - Part I: SOC estimation

This study presents a comprehensive review of State of Charge (SOC) estimation methods for Lithium-Ion (Li-Ion) batteries, with a specific focus on Electric Vehicles (EVs). The growing interest in EVs and the need for efficient battery management have driven advancements in SOC estimation techniques. Various approaches, including data-driven techniques, advanced filtering methods, and machine learning algorithms have been explored to enhance SOC estimation accuracy. The integration of artificial intelligence and hybrid models has shown promising results in improving SOC estimation performance. However, challenges remain in dealing with non-linear battery behavior, temperature variations, and diverse operating conditions. Researchers are continuously studying to improve the robustness and adaptability of SOC estimation methods to address these challenges. The primary objective of this study is to provide an up-to-date summary of the latest advancements in SOC estimation, offering insights into innovative approaches and developments in this field. All existing SOC methods, their advantages, challenges, and usage rates have been comprehensively examined with a specific focus on EV battery management systems. As the EV market continues to expand, accurate SOC estimation will remain essential for optimal battery management and overall EV performance. Future research will focus on refining existing algorithms, exploring new data-driven techniques, and integrating advanced sensor technologies to achieve real-time and reliable SOC estimation in EVs. •This paper provides a comprehensive review of state-of-art methods for estimating State of Charge (SOC) in electric vehicle (EV) batteries.•Various SOC estimation methods (data-driven, filtering, and machine learning-based) are critically evaluated.•The importance of accurate SOC estimation for battery management and range optimization in EVs is emphasized.•Presents favorable results achieved by combining artificial intelligence and hybrid models.•The review offers valuable guidance for researchers and practitioners in the field of EV battery management.