Performance analysis of state of charge and state of health prediction using Kalman filter techniques with battery parameter variation

Accurate estimation of the State of Charge (SOC), State of Health (SOH), and Terminal Resistance (TR) is crucial for the effective operation of Battery Management Systems (BMS) in lithium-ion batteries. This study conducts a comprehensive comparative analysis of four Kalman filter variants Extended Kalman Filter (EKF), Extended Kalman-Bucy Filter (EKBF), Unscented Kalman Filter (UKF), and Unscented Kalman-Bucy Filter (UKBF)—under varying battery parameter conditions. These include temperature fluctuation, self-discharge, current direction, cell capacity, process noise, and measurement noise. Our findings reveal significant variations in the performance of SOC and SOH predictions across filters, emphasizing that UKF demonstrates superior robustness to noise, while EKF performs better under accurate system dynamics. The study underscores the need for adaptive filtering strategies that can dynamically adjust to evolving battery parameters, thereby enhancing BMS reliability and extending battery lifespan.