Comparison Between Recursive Least Squares Method and Kalman Filter for Online Identification of Supercapacitor State of Health

Abstract

Supercapacitors are electrochemical components with high-power density and an intermediate energy density between batteries and conventional capacitors. They are characterized by low series resistance, significant equivalent capacitance, and long service life. Nowadays, they have become an attractive alternative storage device for several applications. However, supercapacitors are subject to degradation due to aging, in addition to other factors, such as temperature and high voltage. Therefore, it is very important to be able to estimate their State-of-Health during operation. Electrochemical Impedance Spectroscopy and Maxwell test are very recognized techniques to determine supercapacitors’ state-of-health. However, these methods require the interruption of system operation and thus cannot be performed in realtime (online). The purpose of this paper is the real-time estimation of supercapacitor resistance and capacitance, which are the main indicators of supercapacitor state-of-health. The electrical behavior of the supercapacitor is modeled using equivalent RC circuit model and the identification is performed using two methods: recursive least squares method and Kalman filter. The resistance and the capacitance values obtained with the two methods are compared with capacitance and resistance values using Maxwell experimental test. The values obtained by Kalman filter are more accurate for both resistance and capacitance.