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, signifificant 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 real-time (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 identifification is performed using two methods: recursive least squares method and Kalman fifilter. 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 fifilter are more accurate for both resistance and capacitance.

SOC, SOH and RUL Estimation for Supercapacitor Management System: Methods, Implementation Factors, Limitations and Future Research Improvements

“…The model-based SOH estimation of the supercapacitor can be classified into empirical model based [55][56][57], filter-based [58][59][60], and other methods [61][62][63]. The SOH estimation techniques have been reviewed under the guidelines of methodological aspect, strength, weakness, and research gap.…”

“…Several state-of-the-art data-driven techniques such as recursive least square (RLS) [58,59,64] and the ANN model [65] have been implemented for the SOH estimation of supercapacitor in SMS. The determination of SOH estimation in a supercapacitor is carried out with two critical parameters, which are capacitance and internal resistance.…”

“…Additionally, the presented model showed a remarkably low computational burden, making it suitable for real-time applications. Bououchma and Sabor [59] conducted a comparative analysis with the RLS method, and KF was employed for the SOH estimation of the supercapacitor.…”