Fuzzy Modelling and Optimization to Decide Optimal Parameters of the PEMFC

Hegazy Rezk, Tabbi Wilberforce (), A. G. Olabi, Rania M. Ghoniem, Mohammad Ali Abdelkareem and Enas Taha Sayed
Additional contact information
Hegazy Rezk: Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Tabbi Wilberforce: Department of Engineering, Faculty of Natural, Mathematical & Engineering Sciences, King’s College London, Strand Campus, London WC2R 2LS, UK
A. G. Olabi: Mechanical Engineering and Design, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK
Rania M. Ghoniem: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Mohammad Ali Abdelkareem: Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
Enas Taha Sayed: Department of Chemical Engineering, Faculty of Engineering, Minia University, Minia 61111, Egypt

Energies, 2023, vol. 16, issue 12, 1-16

Abstract: The main target is the maximization of the output power of PEM “proton exchange membrane” fuel cell via fuzzy modelling and optimization. In the beginning, using the experimental data, a robust fuzzy model is designed for simulating the PEM fuel cell using the relative humidity (%) and stoichiometric ratio at the anode and cathode. Then, the artificial ecosystem optimiser (AEO) is applied to determine the best values of the controlling input parameters. During the optimization process, the four controlling input parameters of the PEMFC are used as the decision variables, whereas as the cost function is required to be at the maximum of the output power density of the PEMFC. For the fuzzy model of the power, the RMSE values are 1.5588 and 3.1906, respectively, for training and testing data. The coefficient of determination values are 0.9826 and 0.8743 for training and testing, respectively. This confirms a successful modelling phase. Finally, the integration between fuzzy and AEO boosted the power of the PEMFC from 57.95 W to 78.44 W (by around 35%). Under this optimal condition, the controlling input parameters values are 26.65%, 56.77%, 1.14, and 1.68, respectively, for anode relative humidity, cathode relative humidity, anode stoichiometric ratio and cathode stoichiometric ratio. The present study, however, intends to highlight the importance of fuzzy modelling and metaheuristic algorithms in the development of digital twins to accelerate the commercialization of fuel cells as well as its applicability in diverse global economic sectors where a higher power requirement is needed. It is also aimed at informing the fuel cell research community and policy makers on strategies that could be adopted in boosting fuel cell performance and therefore could be a good reference source in decision-making for fuel cell commercialization and its practical implementation.

Keywords: PEM fuel cell; optimization; fuzzy modelling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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