As demand for EVs (Electric Vehicles), WSNs (Wireless Sensor Networks), and IoT (Internet of Things) devices continues to grow, efficient battery health monitoring has emerged as a critical requirement. Conventional BMS (Battery Management System) designs rely on wired, centralized architectures, which are not only costly and less scalable but also highly prone to operational failures. To mitigate these inherent drawbacks, recent studies have shifted toward exploring wireless, low-power, and contactless alternatives. This paper reviews emerging sensing solutions and machine learning techniques for battery state and health estimation. It also examines WBMS (Wireless Battery Management System) advancements from theoretical frameworks to prototypes, covering health monitoring, cycle/discharge tracking, thermal management, and second-life reuse. Additionally, we discuss integrating techniques including EIS (electrochemical impedance spectroscopy), ultrasonic sensing with IoT systems and advanced machine learning models. Furthermore, it explores innovative diagnostic approaches and highlights algorithmic frameworks for real-time diagnostics. Overall, this work provides a comprehensive view of intelligent, wireless battery-monitoring technologies and identifies key challenges and research opportunities for scalable deployment in cyber–physical systems.
Srinivasan et al. (2026) studied this question.