State of Charge Estimation of Sustainable Low Emission Electrochemical Energy Storage Systems

Demand for accurate state of charge (SOC) estimation of lithium-Ion batteries (LIBs) is essential for safe and efficient power operation. Traditional estimation methods struggle to achieve accuracy under varying operating conditions. A novel hybrid Adaptive Swarm Optimized Kalman SOC Estimator (ASOKSE) is proposed to cope up with varying operation conditions, where an enhanced Swarm Optimization algorithm incorporating nonlinear inertia weight decay with stochastic uncertainty is employed to identify Thevenin’s equivalent circuit model (ECM) parameters that are then integrated into an Extended Kalman Filter (EKF) to estimate SOC under diverse conditions. Extensive experiments conducted at 0°C, 25°C and 45°C with SOC levels of 50% and 80% demonstrate that the proposed method consistently outperforms the traditional EKF and PSOEKF. The proposed ASOKSE achieves average RMSE of 3.11 % ,2.12%, 3.61% and MAE of 2.34 %, 1.46%, 2.64% across all tested conditions. Furthermore, validation on Federal Urban Driving Schedule (FUDS), Dynamic Stress Test (DST) and US06 driving profiles confirms robustness and adaptability of the proposed framework. The results highlight that the ASOKSE approach not only enhances estimation accuracy but also ensures faster convergence and improved reliability and making it a strong candidate for real world EV battery management systems.