A Novel SoC Estimation Method for Supercapacitor Cell Module Based on EKF-MF Hybrid Filtering Algorithm

In order to accurately estimate the State of Charge (SoC) of supercapacitor cell module, a novel SoC estimation method for supercapacitor cell module is proposed based on Extended Kalman Filtering and Median Filtering (EKF-MF) hybrid filtering algorithm. The state space model of supercapacitor cell module is set up based on its three-branch equivalent circuit model, and the parameter matrix of the system discrete state space model is solved, then the SoC estimation and error analysis of the supercapacitor cell module are carried out. The results show that the comprehensive error of this method in the whole process is 0.239%, which is 4.222% lower than the Ampere-Hour Integration (AHI) method, and only 0.004% lower than extended Kalman Filtering (EKF) method. However, the SoC estimated by EKF has a sharp rise due to the abrupt change of the terminal voltage. The EKF-KF hybrid filtering algorithm inherits the advantages of EKF with high estimation accuracy, and effectively solves the problem that the SoC rises sharply due to the abrupt change of the terminal voltage of the supercapacitor cell module by introducing KF. The novel method can more accurately estimate the SoC of the supercapacitor cell module, which will lay the foundation for effectively evaluating the health status of supercapacitor cell module.