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Optimal Parameter Identification of a PEM Fuel Cell Using Recent Optimization Algorithms

Author

Listed:
  • Hegazy Rezk

    (Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
    Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia 2431436, Egypt)

  • Tabbi Wilberforce

    (Mechanical Engineering and Design, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK)

  • A. G. Olabi

    (Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates)

  • 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)

  • Enas Taha Sayed

    (Department of Chemical Engineering, Faculty of Engineering, Minia University, Minia 2431436, Egypt)

  • Mohammad Ali Abdelkareem

    (Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
    Department of Chemical Engineering, Faculty of Engineering, Minia University, Minia 2431436, Egypt)

Abstract

The parameter identification of a PEMFC is the process of using optimization algorithms to determine the ideal unknown variables suitable for the development of an accurate fuel-cell-performance prediction model. These parameters are not always available from the manufacturer’s datasheet, so they need to be determined to accurately model and predict the fuel cell’s performance. Five optimization methods—bald eagle search (BES) algorithm, equilibrium optimizer (EO), coot (COOT) algorithm, antlion optimizer (ALO), and heap-based optimizer (HBO)—are used to compute seven unknown parameters of a PEMFC. During optimization, these seven parameters are used as decision variables, and the fitness function to be minimized is the sum square error (SSE) between the estimated cell voltage and the actual measured cell voltage. The SSE obtained for the BES algorithm was noted to be 0.035102. The COOT algorithm recorded an SSE of 0.04155, followed by ALO with an SSE of 0.04022 and HBO with an SSE of 0.056021. BES predicted the performance of the fuel cell accurately; hence, it is suitable for the development of a digital twin for fuel-cell applications and control systems for the automotive industry. Furthermore, it was deduced that the convergence speed for BES was faster compared to the other algorithms investigated. This study aims to use metaheuristic algorithms to predict fuel-cell performance for the development and commercialization of digital twins in the automotive industry.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5246-:d:1189675
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    References listed on IDEAS

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    1. Hegazy Rezk & A. G. Olabi & Enas Taha Sayed & Tabbi Wilberforce, 2023. "Role of Metaheuristics in Optimizing Microgrids Operating and Management Issues: A Comprehensive Review," Sustainability, MDPI, vol. 15(6), pages 1-27, March.
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    Cited by:

    1. Gojmir Radica & Ivan Tolj & Mykhaylo V. Lototskyy & Sivakumar Pasupathi, 2023. "Air Mass Flow and Pressure Optimization of a PEM Fuel Cell Hybrid System for a Forklift Application," Energies, MDPI, vol. 17(1), pages 1-18, December.
    2. Antonio Nicolò Mancino & Carla Menale & Francesco Vellucci & Manlio Pasquali & Roberto Bubbico, 2023. "PEM Fuel Cell Applications in Road Transport," Energies, MDPI, vol. 16(17), pages 1-27, August.

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