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

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  • Rezk, Hegazy
  • Aly, Mokhtar
  • Fathy, Ahmed

Abstract

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.

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  • Rezk, Hegazy & Aly, Mokhtar & Fathy, Ahmed, 2021. "A novel strategy based on recent equilibrium optimizer to enhance the performance of PEM fuel cell system through optimized fuzzy logic MPPT," Energy, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s0360544221015152
    DOI: 10.1016/j.energy.2021.121267
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    Cited by:

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    2. Büyük, Mehmet & İnci, Mustafa, 2023. "Improved drift-free P&O MPPT method to enhance energy harvesting capability for dynamic operating conditions of fuel cells," Energy, Elsevier, vol. 267(C).
    3. Fathy, Ahmed & Babu, Thanikanti Sudhakar & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Yousri, Dalia, 2022. "Recent approach based heterogeneous comprehensive learning Archimedes optimization algorithm for identifying the optimal parameters of different fuel cells," Energy, Elsevier, vol. 248(C).
    4. Zhao, Chengxuan & Yang, Xiao & Yu, Jie & Yang, Minghan & Wang, Jianye & Chen, Shuai, 2023. "Interval type-2 fuzzy logic control for a space nuclear reactor core power system," Energy, Elsevier, vol. 280(C).
    5. Gao, Fang & Hu, Rongzhao & Yin, Linfei, 2023. "Variable boundary reinforcement learning for maximum power point tracking of photovoltaic grid-connected systems," Energy, Elsevier, vol. 264(C).
    6. Muhammad Majid Gulzar, 2023. "Maximum Power Point Tracking of a Grid Connected PV Based Fuel Cell System Using Optimal Control Technique," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
    7. Li, Longquan & Liu, Zhiqiang & Deng, Chengwei & Xie, Nan & Ren, Jingzheng & Sun, Yi & Xiao, Zhenyu & Lei, Kun & Yang, Sheng, 2022. "Thermodynamic and exergoeconomic analyses of a vehicular fuel cell power system with waste heat recovery for cabin heating and reactants preheating," Energy, Elsevier, vol. 247(C).
    8. Yuan, Yongliang & Yang, Qingkang & Ren, Jianji & Mu, Xiaokai & Wang, Zhenxi & Shen, Qianlong & Zhao, Wu, 2024. "Attack-defense strategy assisted osprey optimization algorithm for PEMFC parameters identification," Renewable Energy, Elsevier, vol. 225(C).

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