ELECTRONICS & ELECTRICAL DOMAIN
Published September 14, 2026 | Version v1
Journal article Open

Battery Management System: Evolution from Conventional Methods to Advanced Intelligent Techniques

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The rapid development of electric vehicles (EVs), hybrid electric vehicles (HEVs), renewable-energy storage systems, and portable electronic devices has increased the demand for reliable and efficient battery energy-storage systems. Lithium-ion batteries have become one of the most widely used energy-storage technologies because of their high energy density, high power capability, and relatively long cycle life. However, safe and efficient operation of lithium-ion batteries requires accurate monitoring and control of battery voltage, current, temperature, State of Charge (SOC), State of Health (SOH), and State of Power (SOP). A Battery Management System (BMS) performs these functions and protects the battery against overcharging, over-discharging, excessive current and abnormal temperature. Conventional BMS technologies generally employ methods such as Coulomb counting, open-circuit-voltage (OCV) estimation, threshold-based protection, passive cell balancing, and equivalent-circuit battery models. Although these methods are relatively simple and inexpensive, they suffer from limitations such as accumulated SOC error, dependence on battery operating conditions, energy loss during balancing, parameter variation, and limited prediction capability. Modern BMS technologies incorporate advanced observers, adaptive estimation, Kalman-filter-based algorithms, machine learning, artificial intelligence, cloud connectivity, digital twins, active balancing, and cloud/edge-based battery analytics. These approaches can improve battery-state estimation, fault diagnosis, lifetime prediction, and overall battery utilization. This paper presents a comprehensive review of the evolution of BMS technologies from conventional methods to modern intelligent approaches. The operating principles, advantages, limitations, comparative performance, and future research directions of these techniques are discussed, with particular emphasis on electric-vehicle applications.

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References

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