A Particle Swarm Optimized Combined Method for State of Charge Estimation of Lithium-Ion Batteries Against Model Uncertainties

Reliable and accurate state of charge (SOC) estimation is pivotal to enhancing the performance and safety of lithium-ion batteries for battery management system. Extended Kalman filter (EKF) and smooth variable structure filter (SVSF) has alreadly been used for estimating the SOC of lithium-ion batteries. However, either of the two methods only considers the accuracy or robustness of SOC estimation, and cannot give attention to both aspects simultaneously. To this end, this paper proposes a particle swarm optimized combined method that leverages their complementary strengths fully, enhancing the accuracy and robustness of SOC estimation. The effectiveness and superiority of the proposed method are verified by battery test experiments conducted at different ambient temperatures and battery aging states, and comparing to EKF, SVSF, and manual tuning combined method. Experimental results demonstrate that the root mean square error of the proposed method is kept below 6% across all conditions, highlighting its application potential for SOC estimation in complex and variable operational conditions.