Parameter Estimation of Fuel Cell Using Chaotic Mayflies Optimization Algorithm. Issue 12 (5th November 2021) - Gupta, Jyoti; Nijhawan, Parag; Ganguli, Souvik
Abstract: Proton exchange membrane fuel cells (PEMFCs) have been known to be a feasible method for sustainable power generation to meet the ever‐increasing electricity demand with no greenhouse gases emission. Thus, under varying operating circumstances, the PEMFCs have gained considerable importance as an alternative energy source. The optimal operation of PEMFC needs to be ensured to exploit its advantages to the maximum limit. In this direction, there is an extensive need for parameter extraction of PEMFC. Many researchers have applied evolutionary optimization approaches to estimate the PEMFCs parameters as the precise modeling of these cells are not possible. To optimize unknown parameters of PEMFCs, a metaheuristic algorithm is proposed, that is, chaotic mayfly algorithm (CMA) is being proposed in this manuscript. The model's statistical effects are more aligned with the actual experimental findings, according to an experimental finding. The results so obtained suggest that CMA variants are a valuable and efficient method of estimating the parameters of PEMFCs models. Even the nonparametric tests like the Friedman ranking test, Wilcoxon's rank‐sum test, and Mood's median test suggest that the proposed algorithm shows better accuracy of the extracted parameters as compared to the other algorithms namely considered in this work. Abstract : Chaotic mayfly algorithm metaheuristic efficient algorithm is introduced for the parameter estimation of proton exchange membrane fuel cell. These chaotic maps rule out the chances of local minima hence enhancing the accuracy of proposed algorithm. To signify the parameter outcome the computation time and nonparametric test is also performed and compared with rest of the algorithms.