State of Power Estimation Method for Hybrid Capacitor Battery Based on PSO Algorithm

Abstract

Hybrid Capacitive Battery (HCB) is an emerging electrochemical energy storage device that holds immense potential in the application of future energy storage systems (ESSs). When the ESS composed of HCBs is controlled and scheduled, it is necessary to understand its ability to release or absorb power. Therefore, accurate power prediction of batteries is crucial. This paper introduces a method for estimating the state of power (SOP) in HCB using the particle swarm optimization (PSO) algorithm. The method mainly consists of three parts: first, an equivalent circuit model (ECM) is employed to accurately represent the HCB, then an H-∞ filter algorithm is used to estimate its state of energy (SOE). In the third step, an optimization objective function is established based on the HCB model to describe the terminal voltage changes during its charging and discharging process, and use PSO algorithm to solve and obtain the estimated SOP results. Finally, the reference values of the SOP were obtained through constant power pulse testing experiments, proving that this method can effectively predict SOP under constant power conditions.