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Accurate Key Parameters Estimation of PEMFCs’ Models Based on Dandelion Optimization Algorithm

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  • Rabeh Abbassi

    (Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il City 81451, Saudi Arabia
    LaTICE Laboratory, Higher National Engineering School of Tunis (ENSIT), University of Tunis, 5 Avenue Taha Hussein, P.O. Box 56, Tunis 1008, Tunisia
    Institute of Applied Sciences and Technology of Kasserine (ISSATKas), University of Kairouan, P.O. Box 471, Kasserine 1200, Tunisia)

  • Salem Saidi

    (LaTICE Laboratory, Higher National Engineering School of Tunis (ENSIT), University of Tunis, 5 Avenue Taha Hussein, P.O. Box 56, Tunis 1008, Tunisia
    National School of Advanced Sciences and Technologies of Borj Cédria (ENSTAB), University of Carthage, P.O. Box 122, Hammam-Chott 1164, Tunisia)

  • Abdelkader Abbassi

    (Institute of Applied Sciences and Technology of Kasserine (ISSATKas), University of Kairouan, P.O. Box 471, Kasserine 1200, Tunisia
    Engineering Laboratory of Industrial Systems and Renewable Energies (LISIER), National Higher Engineering School of Tunis (ENSIT), University of Tunis, 5 Avenue Taha Hussein, P.O. Box 56, Tunis 1008, Tunisia)

  • Houssem Jerbi

    (Department of Industrial Engineering, College of Engineering, University of Ha’il, Ha’il City 81451, Saudi Arabia)

  • Mourad Kchaou

    (Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il City 81451, Saudi Arabia)

  • Bilal Naji Alhasnawi

    (Department of Computer Technical Engineering, College of Information Technology, Imam Ja’afar Al-Sadiq University, Al-Muthanna 66002, Iraq)

Abstract

With the increasing demand for electrical energy and the challenges related to its production, along with the need to be environmentally friendly to achieve sustainability for future generations, proton exchange membrane fuel cells (PEMFCs) are emerging as a clean energy source that can effectively replace conventional energy sources, in various fields of application and especially in the field of transportation exploiting electric vehicles (EVs). To improve the development and control of the PEMFCs, the precise determination of its mathematical model remains an essential task. Indeed, the accuracy of such a model depends on the ability to overcome the constraints associated with the nonlinearity and the numerous involved unknown parameters. The present paper proposes a new Dandelion Optimizer (DO) to accurately identify, for the first time, the parameters of the PEMFC model. The DO addresses the weaknesses of the majority of metaheuristic algorithms related to the self-adaptation of parameters, the stagnation of convergence to local minima, and the ability to refer to the whole population. The high ability of the proposed method is investigated using both steady-state and dynamic situations. The DO-based parameters estimation approach has been assessed through a specific comparative study with the most recently published techniques including GWO, GBO, HHO, IAEO, VSDE, and ABCDESC is performed using two typical PEMFC modules, namely 250 W PEMFC and NedStack PS6. The results obtained proved that the proposed approach obtained promising achievements and better performances comparatively with well-recognized and competitive methods.

Suggested Citation

  • Rabeh Abbassi & Salem Saidi & Abdelkader Abbassi & Houssem Jerbi & Mourad Kchaou & Bilal Naji Alhasnawi, 2023. "Accurate Key Parameters Estimation of PEMFCs’ Models Based on Dandelion Optimization Algorithm," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1298-:d:1091039
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    References listed on IDEAS

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    Cited by:

    1. Elmamoune Halassa & Lakhdar Mazouz & Abdellatif Seghiour & Aissa Chouder & Santiago Silvestre, 2023. "Revolutionizing Photovoltaic Systems: An Innovative Approach to Maximum Power Point Tracking Using Enhanced Dandelion Optimizer in Partial Shading Conditions," Energies, MDPI, vol. 16(9), pages 1-23, April.
    2. Hassan Ali, Hossam & Fathy, Ahmed, 2024. "Reliable exponential distribution optimizer-based methodology for modeling proton exchange membrane fuel cells at different conditions," Energy, Elsevier, vol. 292(C).
    3. Mustafa Saglam & Xiaojing Lv & Catalina Spataru & Omer Ali Karaman, 2024. "Instantaneous Electricity Peak Load Forecasting Using Optimization and Machine Learning," Energies, MDPI, vol. 17(4), pages 1-22, February.
    4. Rabeh Abbassi & Salem Saidi & Shabana Urooj & Bilal Naji Alhasnawi & Mohamad A. Alawad & Manoharan Premkumar, 2023. "An Accurate Metaheuristic Mountain Gazelle Optimizer for Parameter Estimation of Single- and Double-Diode Photovoltaic Cell Models," Mathematics, MDPI, vol. 11(22), pages 1-21, November.
    5. Ahmad Yasin & Rached Dhaouadi & Shayok Mukhopadhyay, 2024. "A Novel Supercapacitor Model Parameters Identification Method Using Metaheuristic Gradient-Based Optimization Algorithms," Energies, MDPI, vol. 17(6), pages 1-31, March.
    6. Ali M. Eltamaly & Zeyad A. Almutairi & Mohamed A. Abdelhamid, 2023. "Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems," Energies, MDPI, vol. 16(13), pages 1-22, July.

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