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 method-ology outperformed all other methods, achieving the best mean, median, variance, and standard devi-ation. 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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

An improved design approach for fuzzy logic control systems for MPPT control of PEMFCs is proposed in this paper, utilizing the Equilibrium Optimizer method for parameter optimization. Through comparisons with other optimization methods and statistical tests, the efficiency and reliability of the proposed method are demonstrated, providing a promising solution for MPPT control in PEMFCs.