Parameter Estimation of Fuel Cell Using Chaotic Mayflies Optimization Algorithm

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.

A novel hybrid particle swarm optimization rat search algorithm for parameter estimation of solar PV and fuel cell model

“…Equations (3)–(5) explain the electrochemical reactions that take place at the electrodes of PEMFCs (Singla et al , 2019; Singla et al , 2021; Singla et al , 2021; Gupta et al 2021; Gupta et al , 2021):…”

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

“…Furthermore, the following parameters of ξ 1 , ξ 2 , ξ 3 , ξ 4 , β, R C , and λ should be accurately estimated through the tested optimisation algorithms. The estimation process considers the search ranges of Table 1 for each parameter [7–9, 13, 15, 16, 19, 22, 29, 42].…”

Integration of Fopid and Mayfly Algorithm for Sepic with Multi-Objective Functions

“…First, certain studies attempt to connect MA with other theories and techniques for optimization. For an instance, a new enhanced mayfly optimization technique by fusing theory of chaos [40] and the mayfly algorithm is suggested to handle the global optimization problems. Similar to this, Chaotic Mayfly optimization [41] algorithm uses a recently created optimization technique to evaluate the needed parameters of membrane fuel cells for proton interchange.…”