State-of-charge estimation of lithium-ion batteries using extended Kalman filter and unscented Kalman filter

Lithium-ion battery stands for vital segment of the hybrid-electrical vehicles (HEV). Accurate monitoring of battery status, which is the main task of battery management system (BMS), ensures reliable operation and preserve battery performance. Successful management of battery relies on accurate estimation of state-of-charge (SoC) parameter. Proposed methods for SoC estimation tend to consider nonlinear nature of battery system with time and temperature dependent parameters. Kalman-based filters have been widely used for SoC estimation. In this paper, two Kalman-based filters have been used fo SoC estimation, Extended Kalman filter (EKF) and Uscented Kalman filter (UKF). These two methods have been compared and simulation results are presented.