A novel method of parameter identification and state of charge estimation for lithium-ion battery energy storage system

Abstract

Battery energy storage system (BESS) has been widely used for the expansion of the electricity grid. Accurate state of charge (SOC) estimation of BESS is essential for ensuring system safety and reliability. However, it is still a challenging work to improve the model accuracy and supress the cumulative error during the SOC estimation. This paper develops a parameter identification method based on the dynamic voltage responses in the practical constant current (CC) discharging process to identify the battery model parameters with the particle swarm optimization (PSO) method. Furthermore, to minimize system errors caused by the model, algorithm, and measurement system, a hybrid SOC estimation method is proposed. In the proposed hybrid method, an improved extended Kalman filter (EKF) method with constructed compensation error is employed to suppress system errors. PSO is again adopted to determine the dynamic compensation error based on the reliable increment of the ampere-hour counting (AHC) method over the whole SOC range. To validate the effectiveness of the proposed method, three CC discharging tests are carried out at 25 and 5 ℃. The results show the proposed model parameter identification method and the hybrid SOC estimation method can jointly provide more accurate SOC estimation.