Evolution of Battery Management Systems: From Conventional Approaches to Advanced Intelligent Techniques
Keywords:
Battery management system, Cell balancing, Lithium-ion battery, State of charge, State of healthAbstract
The rapid growth of Electric Vehicles (EVs), hybrid electric vehicles, renewable-energy storage systems, and portable electronic devices has increased the need for safe, reliable, and efficient battery management. Lithium-ion batteries are widely used because of their high energy density, power capability, and relatively long cycle life; however, their performance is strongly affected by cell imbalance, temperature, ageing, operating conditions, and measurement uncertainty. A Battery Management System (BMS) therefore performs continuous monitoring, protection, state estimation, cell balancing, thermal management, and communication. This review examines the evolution of BMS technology from conventional approaches, including Coulomb counting, open-circuit-voltage estimation, fixed threshold protection, equivalent-circuit models, and passive cell balancing, to advanced intelligent techniques. Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter methods are discussed together with fuzzy logic, neural networks, machine learning, active balancing, cloud-connected BMS, digital twins, and predictive battery management. The review indicates that conventional methods remain attractive because of their simplicity, low computational requirements, and reliability, whereas advanced methods provide better adaptability and prediction capability. Hybrid methods that combine physical battery models, measured variables, and artificial intelligence provide a promising balance between interpretability and estimation performance. Remaining challenges include computational complexity, data requirements, cybersecurity, thermal safety, and real-world validation.
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