State-of-Charge Estimation Method for Lithium-Ion Batteries Using Extended Kalman Filter with Adaptive Battery Parameters

Accurate battery state-of-charge (SOC) estimation is important for the efficient and reliable operation of application systems. The extended Kalman filter (EKF), which based on model, widely used as a real-time SOC algorithm; furthermore, its accuracy depends model accuracy. However, conventional EKF uses one value each parameter ( Ri , xmlns:xlink="http://www.w3.org/1999/xlink">Rd xmlns:xlink="http://www.w3.org/1999/xlink">Cd ) regardless SOC, even though their values change according to SOC. To address this problem, study proposes an improved that applies parameters model. In proposed method, entire was divided into several sections considering deviation Subsequently, average section were calculated, updated with verify performance EKF, commercial Li-ion batteries extracted dis-charge currents 1C- 2C-rates at ambient temperatures 0 °C, 25 45 MATLAB simulations performed. Compared estimated more accurately under all simulation conditions. maximum reduced root-mean-square error method 49.37% 56.41%, respectively.