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Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis

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  • Gouda, Eid A.
  • Kotb, Mohamed F.
  • El-Fergany, Attia A.

Abstract

In this paper, a novel attempt to employ the Jellyfish search algorithm (JSA) for solving parameters’ identifications problem of polymer exchange membrane fuel cells (PEMFCs) model is addressed. The minimization of the sum of squared errors (SSEs) between measured and estimated voltage dataset points define the fitness function to be optimized by the JSA subject to set of self-constrained inequality bounds. Three test cases are demonstrated complete with necessary verifications, comparisons and subsequent discussions. It can be quantified here that the best cropped values of SSEs are equal to 0.011699, 0.33598 and 2.14570 V2 for BCS 500-W module, 250-W stack and NedStack type PS6 unit; respectively. It can be confirmed that the maximum value of percentage voltage biased error is ±1% for all test cases under study. In addition, various performance measures are made to signify the robustness of the cropped results. At a later stage, various operating principal characteristics under changeable temperatures and varied regulating pressures are illustrated. It can be established that the JSA proves its ability to tackle this problem competently compared to others.

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  • Gouda, Eid A. & Kotb, Mohamed F. & El-Fergany, Attia A., 2021. "Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis," Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s0360544221000852
    DOI: 10.1016/j.energy.2021.119836
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    Cited by:

    1. Gang Hu & Jiao Wang & Min Li & Abdelazim G. Hussien & Muhammad Abbas, 2023. "EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications," Mathematics, MDPI, vol. 11(4), pages 1-32, February.
    2. 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).
    3. Ibrahim Attiya & Laith Abualigah & Samah Alshathri & Doaa Elsadek & Mohamed Abd Elaziz, 2022. "Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling," Mathematics, MDPI, vol. 10(11), pages 1-23, June.
    4. 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.
    5. 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).
    6. Huang, Ruike & Peng, Yiqiang & Yang, Jibin & Xu, Xiaohui & Deng, Pengyi, 2022. "Correlation analysis and prediction of PEM fuel cell voltage during start-stop operation based on real-world driving data," Energy, Elsevier, vol. 260(C).
    7. Alaa A. Zaky & Rania M. Ghoniem & F. Selim, 2023. "Precise Modeling of Proton Exchange Membrane Fuel Cell Using the Modified Bald Eagle Optimization Algorithm," Sustainability, MDPI, vol. 15(13), pages 1-16, July.
    8. Guarino, Antonio & Trinchero, Riccardo & Canavero, Flavio & Spagnuolo, Giovanni, 2022. "A fast fuel cell parametric identification approach based on machine learning inverse models," Energy, Elsevier, vol. 239(PC).
    9. 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).
    10. 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).

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