Advanced Strategies in Battery Management Systems for Electric Vehicles: Modelling, State Estimation, And Emerging Technologies

Battery Management Systems (BMS) are pivotal to the evolution and adoption of electric vehicles (EVs), acting as the cornerstone for ensuring the safety, reliability, and efficiency of battery operations. This review systematically explores the latest advancements in BMS technologies, with a focus on modeling, state estimation, and emerging innovations tailored to meet the complex demands of modern EVs. The study begins by dissecting the various battery modeling techniques, including equivalent circuit models (ECMs) such as Rint, Thevenin, PNGV, and advanced fractional-order models, which offer enhanced accuracy and dynamic performance. These models form the foundation for robust state estimation methods, enabling precise calculations of the state of charge (SOC), state of health (SOH), state of power (SOP), and remaining useful life (RUL). The paper examines the integration of algorithmic advancements, such as Kalman filters and data-driven techniques, which have significantly improved the predictive and diagnostic capabilities of BMS. The review also delves into the emerging integration of hybrid energy storage systems (HESS), combining batteries with supercapacitors to optimize energy density, power output, and lifespan. Further, advancements in thermal management systems are analyzed, highlighting active and passive techniques to mitigate temperature-induced degradation and enhance battery safety. Emphasis is placed on the development of cost-effective BMS designs using modern microcontrollers, such as Cortex-M4, and efficient communication protocols to ensure scalability and real-time monitoring. Despite these technological strides, challenges persist, including scalability for large battery packs, cost reduction for widespread adoption, and sustainable disposal or recycling of Li-ion batteries. This review provides a comprehensive roadmap of the current state of BMS research and identifies key areas for future exploration, such as the incorporation of artificial intelligence, machine learning, and universal BMS platforms. By synthesizing insights from cutting-edge research, this paper aims to guide the development of next-generation BMS solutions that are scalable, efficient, and aligned with the global push for sustainable transportation.