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

Abstract

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

Keywords

• State of charge
• lithium-ion battery
• extended Kalman filter
• battery parameter
• battery model
• battery management system