A Review on Battery Management System (BMS) State of Charge (SOC) Estimation for Li-ion Batteries in Electric Vehicles using Intelligent Techniques
Keywords:
Battery Management System (BMS), Electric Vehicle (EV), Intelligent estimation, Kalman filter, Li-ion battery, Machine Learning, Neural network, State of Charge (SOC)Abstract
The adoption of electric vehicles is accelerating rapidly, leading to stricter requirements for Battery Management Systems to ensure the safe and efficient monitoring of lithium-ion batteries, with accurate State of Charge estimation being a key function. Traditional methods such as Coulomb counting, open-circuit voltage approaches, and equivalent-circuit or observer-based models are favored for their simplicity and low computational burden; however, their performance degrades under practical EV operating conditions characterized by dynamic current profiles, temperature fluctuations, sensor inaccuracies, and battery ageing. This review synthesises recent progress in intelligent SOC estimators, covering Kalman filter families, machine‑learning and neural‑network models, recurrent and deep architectures, fuzzy and neuro‑fuzzy schemes, and hybrid combinations that fuse physical models with data‑driven components. Reported studies from 2023–2025 are organised into comparative tables that highlight typical SOC error ranges, RMSE, response time, robustness to temperature and noise, and on‑board feasibility for automotive BMS hardware. The article also examines practical deployment barriers, including measurement uncertainty, limited cross‑chemistry generalisation of trained models, and processing and memory limits of embedded controllers. On this basis, it outlines key open problems and future directions, stressing the importance of common benchmark datasets, temperature‑ and ageing‑aware estimators, model‑compression and TinyML strategies, and self‑adapting hybrid algorithms capable of sustaining high‑accuracy SOC estimation over the full battery life in next‑generation EV platforms.
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