IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i15p11741-d1206447.html
   My bibliography  Save this article

An Efficient White Shark Optimizer for Enhancing the Performance of Proton Exchange Membrane Fuel Cells

Author

Listed:
  • Ahmed Fathy

    (Electrical Engineering Department, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt)

  • Abdulmohsen Alanazi

    (Electrical Engineering Department, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

Abstract

This study investigates the substantial contribution of the recent numerical optimization technique known as the White Shark Optimizer (WSO) to evaluate the performance of proton exchange membrane fuel cell (PEMFC) design parameters that play a considerable role in boosting its effectiveness. A numerical code was developed and implemented via MATLAB software to achieve the research goal. The proposed WSO was employed to identify the unknown parameters of the PEMFC equivalent circuit, considering experimental data. The analyzed objective function was the root mean squared error (RMSE) between the measured and estimated fuel cell terminal voltages. Additionally, the proposed WSO was compared with other intelligent approaches such as the salp swarm algorithm (SSA), Harris hawks optimization (HHO), atom search optimization (ASO), dung beetle optimization algorithm (DBOA), stochastic paint optimizer (SPO), and comprehensive learning Archimedes optimization algorithm (HCLAOA). The numerical simulations revealed that the RMSE values varied between lower and higher values of 0.009095329 and 0.028663611, respectively. Additionally, the results indicated that the mean fitness value recorded in the considered PEMFC 250 W stack was 0.020057775. Moreover, the minimum fitness value was obtained using the proposed WSO, with an operating temperature of 353.15 K and working anode and cathode pressures are 3 bar and 5 bar, respectively. The proposed WSO offered the best results in terms of absolute errors compared to the other optimizers, confirming the robustness of the results in all considered cases.

Suggested Citation

  • Ahmed Fathy & Abdulmohsen Alanazi, 2023. "An Efficient White Shark Optimizer for Enhancing the Performance of Proton Exchange Membrane Fuel Cells," Sustainability, MDPI, vol. 15(15), pages 1-21, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11741-:d:1206447
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/15/11741/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/15/11741/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Daniel Gabaldón-Estevan & Elisa Peñalvo-López & David Alfonso Solar, 2018. "The Spanish Turn against Renewable Energy Development," Sustainability, MDPI, vol. 10(4), pages 1-16, April.
    3. Siavash Asiaban & Nezmin Kayedpour & Arash E. Samani & Dimitar Bozalakov & Jeroen D. M. De Kooning & Guillaume Crevecoeur & Lieven Vandevelde, 2021. "Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System," Energies, MDPI, vol. 14(9), pages 1-41, May.
    4. Hasanien, Hany M. & Shaheen, Mohamed A.M. & Turky, Rania A. & Qais, Mohammed H. & Alghuwainem, Saad & Kamel, Salah & Tostado-Véliz, Marcos & Jurado, Francisco, 2022. "Precise modeling of PEM fuel cell using a novel Enhanced Transient Search Optimization algorithm," Energy, Elsevier, vol. 247(C).
    5. Ibrahim Alsaidan & Mohamed A. M. Shaheen & Hany M. Hasanien & Muhannad Alaraj & Abrar S. Alnafisah, 2021. "Proton Exchange Membrane Fuel Cells Modeling Using Chaos Game Optimization Technique," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    6. Samuel Raafat Fahim & Hany M. Hasanien & Rania A. Turky & Abdulaziz Alkuhayli & Abdullrahman A. Al-Shamma’a & Abdullah M. Noman & Marcos Tostado-Véliz & Francisco Jurado, 2021. "Parameter Identification of Proton Exchange Membrane Fuel Cell Based on Hunger Games Search Algorithm," Energies, MDPI, vol. 14(16), pages 1-21, August.
    7. Noor H. Jawad & Ali Amer Yahya & Ali R. Al-Shathr & Hussein G. Salih & Khalid T. Rashid & Saad Al-Saadi & Adnan A. AbdulRazak & Issam K. Salih & Adel Zrelli & Qusay F. Alsalhy, 2022. "Fuel Cell Types, Properties of Membrane, and Operating Conditions: A Review," Sustainability, MDPI, vol. 14(21), pages 1-48, November.
    8. Hachana, Oussama & El-Fergany, Attia A., 2022. "Efficient PEM fuel cells parameters identification using hybrid artificial bee colony differential evolution optimizer," Energy, Elsevier, vol. 250(C).
    9. Dario Maradin, 2021. "Advantages and Disadvantages of Renewable Energy Sources Utilization," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 176-183.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fernando Lepe-Silva & Broderick Crawford & Felipe Cisternas-Caneo & José Barrera-Garcia & Ricardo Soto, 2024. "A Binary Chaotic White Shark Optimizer," Mathematics, MDPI, vol. 12(20), pages 1-35, October.
    2. Xilong Lin & Yisen Niu & Zixuan Yan & Lianglin Zou & Ping Tang & Jifeng Song, 2024. "Hybrid Photovoltaic Output Forecasting Model with Temporal Convolutional Network Using Maximal Information Coefficient and White Shark Optimizer," Sustainability, MDPI, vol. 16(14), pages 1-20, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Andrew J. Riad & Hany M. Hasanien & Rania A. Turky & Ahmed H. Yakout, 2023. "Identifying the PEM Fuel Cell Parameters Using Artificial Rabbits Optimization Algorithm," Sustainability, MDPI, vol. 15(5), pages 1-17, March.
    3. Ćalasan, Martin & Micev, Mihailo & Hasanien, Hany M. & Abdel Aleem, Shady H.E., 2024. "PEM fuel cells: Two novel approaches for mathematical modeling and parameter estimation," Energy, Elsevier, vol. 290(C).
    4. 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).
    5. Mohamed Abdel-Basset & Reda Mohamed & Victor Chang, 2021. "An Efficient Parameter Estimation Algorithm for Proton Exchange Membrane Fuel Cells," Energies, MDPI, vol. 14(21), pages 1-23, November.
    6. Fathy, Ahmed & Rezk, Hegazy & Alharbi, Abdullah G. & Yousri, Dalia, 2023. "Proton exchange membrane fuel cell model parameters identification using Chaotically based-bonobo optimizer," Energy, Elsevier, vol. 268(C).
    7. Rezk, Hegazy & Olabi, A.G. & Ferahtia, Seydali & Sayed, Enas Taha, 2022. "Accurate parameter estimation methodology applied to model proton exchange membrane fuel cell," Energy, Elsevier, vol. 255(C).
    8. Abdel-Basset, Mohamed & Mohamed, Reda & Abouhawwash, Mohamed, 2023. "On the facile and accurate determination of the highly accurate recent methods to optimize the parameters of different fuel cells: Simulations and analysis," Energy, Elsevier, vol. 272(C).
    9. Hasanien, Hany M. & Shaheen, Mohamed A.M. & Turky, Rania A. & Qais, Mohammed H. & Alghuwainem, Saad & Kamel, Salah & Tostado-Véliz, Marcos & Jurado, Francisco, 2022. "Precise modeling of PEM fuel cell using a novel Enhanced Transient Search Optimization algorithm," Energy, Elsevier, vol. 247(C).
    10. Athanasios Ioannis Arvanitidis & Vivek Agarwal & Miltiadis Alamaniotis, 2023. "Nuclear-Driven Integrated Energy Systems: A State-of-the-Art Review," Energies, MDPI, vol. 16(11), pages 1-23, May.
    11. Yuzgec, Ugur & Dokur, Emrah & Balci, Mehmet, 2024. "A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting," Energy, Elsevier, vol. 300(C).
    12. Abel Rubio & Wilton Agila & Leandro González & Jonathan Aviles-Cedeno, 2023. "Distributed Intelligence in Autonomous PEM Fuel Cell Control," Energies, MDPI, vol. 16(12), pages 1-25, June.
    13. Julia Maria Wittmayer & Tessa de Geus & Bonno Pel & F. Avelino & Sabine Hielscher & Thomas Hoppe & Marie Susan Mühlemeier & Agata Stasik & Sem Oxenaar & Karoline K.S. Rogge & Vivian Visser & Esther Ma, 2020. "Beyond instrumentalism: Broadening the understanding of social innovation in socio-technical energy systems," ULB Institutional Repository 2013/312323, ULB -- Universite Libre de Bruxelles.
    14. Dzido, Aleksandra & Krawczyk, Piotr & Wołowicz, Marcin & Badyda, Krzysztof, 2022. "Comparison of advanced air liquefaction systems in Liquid Air Energy Storage applications," Renewable Energy, Elsevier, vol. 184(C), pages 727-739.
    15. Srikantaswamy Shivanna & K. S. Rangappa, 2024. "Clean energy for sustainable development in India," Journal of Social and Economic Development, Springer;Institute for Social and Economic Change, vol. 26(1), pages 139-147, October.
    16. Dario Maradin & Bojana Olgić Draženović & Saša Čegar, 2023. "The Efficiency of Offshore Wind Energy Companies in the European Countries: A DEA Approach," Energies, MDPI, vol. 16(9), pages 1-16, April.
    17. Olawale Fatoki, 2022. "Determinants of Intention to Purchase Photovoltaic Panel System: An Integration of Technology Acceptance Model and Theory of Planned Behaviour," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 432-440, May.
    18. Elisa Peñalvo-López & Ángel Pérez-Navarro & Elías Hurtado & F. Javier Cárcel-Carrasco, 2019. "Comprehensive Methodology for Sustainable Power Supply in Emerging Countries," Sustainability, MDPI, vol. 11(19), pages 1-22, September.
    19. Vangelis Marinakis & Alexandros Flamos & Giorgos Stamtsis & Ioannis Georgizas & Yannis Maniatis & Haris Doukas, 2020. "The Efforts towards and Challenges of Greece’s Post-Lignite Era: The Case of Megalopolis," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    20. Pei, Pucheng & Meng, Yining & Chen, Dongfang & Ren, Peng & Wang, Mingkai & Wang, Xizhong, 2023. "Lifetime prediction method of proton exchange membrane fuel cells based on current degradation law," Energy, Elsevier, vol. 265(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11741-:d:1206447. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.