As the battery provides the entire propulsion power in electric vehicles (EVs), the utmost importance should be ascribed to the battery management system (BMS) which controls all the activities associated with the battery. This review article seeks to provide readers with an overview of prominent BMS subsystems and their influence on vehicle performance, along with their architectures. Moreover, it collates many recent research activities and critically reviews various control strategies and execution topologies implied in different aspects of BMSs, including battery modeling, states estimation, cell-balancing, and thermal management. The internal architecture of a BMS, along with the architectures of the control modules, is examined to demonstrate the working of an entire BMS control module. Moreover, a critical review of different battery models, control approaches for state estimation, cell-balancing, and thermal management is presented in terms of their salient features and merits and demerits allowing readers to analyze and understand them. The review also throws light on modern technologies implied in BMS, such as IoT (Internet of Things) and cloud-based BMS, to address issues of battery safety. Towards the end of the review, some challenges associated with the design and development of efficient BMSs for E-mobility applications are discussed and the article concludes with recommendations to tackle these challenges.

In response to the evolving transportation landscape, the safety and durability of hybrid electric vehicles (HEVs) necessitate the development of high-performance, reliable health management systems for batteries. The state of health (SOH) provides vital insights about the performance and longevity of batteries, thus enhancing opportunities for efficient energy management in hybrid systems. Despite various research efforts for battery SOH estimation, many of them fall short of the demands for real-time automotive applications. Real-time SOH estimation is crucial for accurate battery fault diagnosis and maintaining precise estimation of the state of charge (SOC) and state of power (SOP), which are essential for the optimal functioning of hybrid systems. In this study, a fuzzy logic estimation method is deployed to determine the tire road friction coefficient (TRFC) and various control strategies are adopted to establish regenerative cut-off points. A MATLAB-based SOH estimation model was developed using a Kalman SOH estimator, which helps to observe the effects of different control strategies on the battery’s SOH. This approach enhances the accuracy and reliability of SOH estimation in real-time applications and improves the effectiveness of battery fault diagnosis. From the results, ANFIS outperformed standard methods, showing approximately 4–6% higher SOH retention across various driving cycles.