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Extracting Accurate Parameters from a Proton Exchange Membrane Fuel Cell Model Using the Differential Evolution Ameliorated Meta-Heuristics Algorithm

Author

Listed:
  • Badreddine Kanouni

    (Electrical Engineering Department, University of Setif 1, Sétif 19000, Algeria)

  • Abdelbaset Laib

    (Department of Automatic, Faculty of Electrical Engineering, University of Science and Technology Houari Boumediene, Algiers 16111, Algeria)

Abstract

The electrochemical proton exchange membrane fuel cell (PEMFC) is an electrical generator that utilizes a chemical reaction mechanism to produce electricity, serving as a sustainable and environmentally friendly energy source. To thoroughly analyze and develop the features and performance of a PEMFC, it is essential to use a precise model that incorporates exact parameters to effectively suit the polarization curve. In addition, parameter extraction plays a crucial role in the simulation analysis, evaluation, optimum control, and fault detection of the proton exchange membrane fuel cell (PEMFC) system. Despite the development of many algorithms for parameter extraction in PEMFC, obtaining accurate and trustworthy results rapidly remains a challenge. This study presents a hybridized algorithm, namely differential evolution ameliorated (DEA) for reliably estimating PEMFC model parameters. To evaluate the proposed DEA-based parameter identification, a comparison analysis with previously published methods is conducted using MATLAB/Simulink TM (R2016b, MathWorks, Natick, MA, USA) in terms of system correctness and convergence process. The proposed DEA algorithm is tested to extract the parameters of two PEMFC models: SR-12 500 W and 250 W. The sum of the squared errors (SSE) between the experimental and the obtained voltage data is defined as an objective function. The simulation results prove that the suggested DEA algorithm is capable of identifying the optimal PEMFC parameters rapidly and accurately in comparison with other optimization algorithms.

Suggested Citation

  • Badreddine Kanouni & Abdelbaset Laib, 2024. "Extracting Accurate Parameters from a Proton Exchange Membrane Fuel Cell Model Using the Differential Evolution Ameliorated Meta-Heuristics Algorithm," Energies, MDPI, vol. 17(10), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2333-:d:1393155
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    References listed on IDEAS

    as
    1. Hegazy Rezk & Tabbi Wilberforce & A. G. Olabi & Rania M. Ghoniem & Enas Taha Sayed & Mohammad Ali Abdelkareem, 2023. "Optimal Parameter Identification of a PEM Fuel Cell Using Recent Optimization Algorithms," Energies, MDPI, vol. 16(14), pages 1-20, July.
    2. Yang, Bo & Li, Danyang & Zeng, Chunyuan & Chen, Yijun & Guo, Zhengxun & Wang, Jingbo & Shu, Hongchun & Yu, Tao & Zhu, Jiawei, 2021. "Parameter extraction of PEMFC via Bayesian regularization neural network based meta-heuristic algorithms," Energy, Elsevier, vol. 228(C).
    3. Wilberforce, Tabbi & Rezk, Hegazy & Olabi, A.G. & Epelle, Emmanuel I. & Abdelkareem, Mohammad Ali, 2023. "Comparative analysis on parametric estimation of a PEM fuel cell using metaheuristics algorithms," Energy, Elsevier, vol. 262(PB).
    4. Seleem, Sameh I. & Hasanien, Hany M. & El-Fergany, Attia A., 2021. "Equilibrium optimizer for parameter extraction of a fuel cell dynamic model," Renewable Energy, Elsevier, vol. 169(C), pages 117-128.
    5. Moreira, Marcos V. & da Silva, Gisele E., 2009. "A practical model for evaluating the performance of proton exchange membrane fuel cells," Renewable Energy, Elsevier, vol. 34(7), pages 1734-1741.
    6. Chakraborty, Uday K. & Abbott, Travis E. & Das, Sajal K., 2012. "PEM fuel cell modeling using differential evolution," Energy, Elsevier, vol. 40(1), pages 387-399.
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