A novel strategy based on recent equilibrium optimizer to enhance the performance of PEM fuel cell system through optimized fuzzy logic MPPT

In this paper, an improved design approach for fuzzy logic control (FLC) systems is proposed for MPPT control of PEMFCs. The proposed design is based on the recent Equilibrium Optimizer method for determining the optimum parameters to fully benefit the inherent flexibility and freedom of FLC systems in addition to achieve fast and accurate tracking. During the optimization process, gains of membership functions of FLC is used as a decision variable, whereas the integral of error is used as a cost function. The obtained results are compared with particle swarm optimization (PSO), genetic algorithm (GA), electric charged particles optimization (ECPO) and spotted hyena optimizer (SHO). The proposed EO methodology outperformed all other methods, achieving the best mean, median, variance, and standard deviation. Moreover, statistical tests including Wilcoxon, Holm-Bonferroni correction, Kruskal Wallis, and Friedman tests are performed to prove efficiency of the proposed strategy. In last, different scenarios of changing operating temperature and water content are used to prove the reliability of the optimized FLC. The obtained results are compared with conventional FLC and hill-climbing method. The main findings confirm that the proposed design using combined features of EO and FLC presents a promising solution for MPPT in PEMFCs.