SOC estimation of lithium-ion battery based on kalman filter algorithm for energy storage system in microgrids

Abstract

State-of-charge (SOC) is one of the vital factors for the energy storage system (ESS) in the microgrid power systems to guarantee that a battery system is operating in a safe and reliable manner for the system. Many uncertainties and noises, such as nonlinearities in the internal states of a battery, sensor measurement accuracy and bias, temperature effects, calibration errors, and sensor failures, pose a challenge to the accurate estimation of SOC in most applications. This study makes two contributions to the existing literatures. First, a more accurate extended Kalman filter (EKF) algorithm is proposed to estimate the battery nonlinear dynamics. Due to its discrete form and ease of implementation, this straightforward approach could be more suitable for real applications on the ESS. Second, its order selection principle and parameter identification method are illustrated in detail. It can accurately demonstrate the characteristics of the lithium-ion battery to show the feasibility and effectiveness of the algorithm for the ESS.