Online Parameter Identification of Supercapacitor Models using Recursive Least Squares Algorithm

Abstract

Supercapacitor-based energy storage systems have been widely applied in various power-level applications in recent years. Owing to their ultra-high capacitance and capacitor-like behavior, supercapacitors are particularly well-suited for high-power, short-duration scenarios, such as regenerative braking in electric vehicles and transient frequency regulation in power grids. For all such applications, having an accurate supercapacitor model is crucial for analyzing their dynamic behavior under diverse operating conditions. To enhance modeling accuracy, several equivalent circuit models have been proposed in the literature, including simple RC, extended RC, two-branch RC, and three-branch RC models. In addition to model selection, parameter identification techniques play a crucial role in improving the model precision. This paper reviews commonly used supercapacitor models and discusses the applicability of each under specific operating conditions. Furthermore, a parameter identification method based on the Recursive Least Squares (RLS) algorithm is proposed. This approach improves the accuracy of capturing the discharge behavior of the supercapacitor. The effectiveness of the method is validated through mathematical modeling and experimental comparisons.