Battery Management Systems for Next-Generation Electric Vehicles: A Comprehensive Review of Architectures, Standards, and Optimization Techniques

Battery Management Systems for Next-Generation Electric Vehicles: A Comprehensive Review of Architectures, Standards, and Optimization Techniques

Lithium-ion battery packs in contemporary electric vehicles (EVs) depend on Battery Management Systems (BMS) for long-term, safe, and effective functioning. This study summarizes the state of the art in BMS hardware and software architectures, worldwide stan¬dards, and safety laws that influence BMS testing and design, and optimization methods for key issues such as state estimation and cell balancing. We compare passive and active cell balancing techniques, examine communication, functional safety, and test/regulatory land¬scapes (ISO 26262, IEC 62660 series, UNECE R100, ISO 15118), and survey both conventional and sophisticated estimation algorithms, including coulomb counting, equivalent-circuit-model plus Kalman filters, particle filters, and hybrid data-driven/model-based approaches. The survey is concluded by discussing optimization approaches applied to BMS tasks, ranging from traditional parameter tuning to meta¬heuristics and machine learning. This study also highlights the research gaps and future goals for next-generation BMS for EVs.