Parameter Identification and Test of Dynamic Model for Supercapacitors Based on Extended Kalman Filter Method

Considering the complexity of the supercapacitors model structure and the accuracy of the external feature description, the dynamic model is established as the equivalent model of the supercapacitors. The model reduces inductance on the basis of the dynamic model and determines the number of RC (Rwsistor-Capacotance) networks. Aiming at the parameter identification process, the EKF (Extended Kalman Filter) algorithm is proposed to replace the EIS (Electrochemical Impedance Spectroscopy) method, and the algorithm is simulated and experimentally verified. The simulation and experimental results show that under UDDS (Urban Dynamometer Driving Schedule) conditions, the simulated voltage value of the supercapacitors model can follow the measured voltage value well, and the error is small. The average relative error of the supercapacitors model under UDDS conditions is less than 2.14%, which verifies the accuracy and effectiveness of the proposed model and algorithm.