Optimal parameter identification of supercapacitor model using bald eagle search optimization algorithm

Obtaining the accurate values of the supercapacitor (SC) parameters is one of the most vital stages. To provide an exact simulation for the supercapacitor behavior, these parameters have to be identified properly. Therefore, a new identification method based on the blade eagle search algorithm (BES) is proposed in this research to extract the optimal parameters of the supercapacitor model. BES is a recent metaheuristic optimizer that provided exceptional performance in many applications. In addition, this algorithm provides an accurate solution due to its convergence mechanism. Two Electric Double Layer Capacitor (EDLC) supercapacitors are considered in the simulation with values of 470 F and 1500 F. However, the values of eight parameters that affect the SC performance have to be well-identified. The objective function is proposed to be the sum squared error (SSE) between the actual and estimated output voltages. To assess the qualification of the proposed methodology, the results of the BES are compared to seven different well-known optimization approaches. The BES resulting values showed that the proposed approach is not only promising in constructing a reliable equivalent circuit of supercapacitors but also estimates the SC parameters' values very close to the real ones. Moreover, based on several and extensive statistical tests, the robustness of the proposed BES is affirmed in comparison to other metaheuristic techniques.

Automated summary

In this research, a new identification method based on the blade eagle search algorithm (BES) is proposed to extract the optimal parameters of the supercapacitor model. The results show that the proposed approach is promising in constructing a reliable equivalent circuit of supercapacitors and estimates the parameters' values very close to the real ones.