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Developed Design of Battle Royale Optimizer for the Optimum Identification of Solid Oxide Fuel Cell

Author

Listed:
  • Keyvan Karamnejadi Azar

    (Department of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia 57169-63896, Iran)

  • Armin Kakouee

    (Department of Mechanical Engineering, Amoli Branch, Islamic Azad University Ayatollah, Amol 46351-43358, Iran)

  • Morteza Mollajafari

    (Automotive Electrical and Electronics Laboratory, School of Automotive Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran)

  • Ali Majdi

    (Department of Building and Construction Techniques, Al-Mustaqbal University College, Hillah 51001, Iraq)

  • Noradin Ghadimi

    (Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil 56157-31567, Iran
    Department of Industrial Engineering, Ankara Yıldırım Beyazıt University, Ankara 06760, Turkey)

  • Mojtaba Ghadamyari

    (Department of Computer Engineering, Lebanese French University, Erbil 44001, Iraq
    Department of Electrical Engineering, Shahid Beheshti University, Tehran 19839-69411, Iran)

Abstract

One of the most appropriate electricity production systems is solid oxide fuel cells (SOFCs), which are important because they are highly efficient, flexible to fuel, and have fewer environmental degradation effects. A new optimum technique has been provided for providing well-organized unknown parameters identification of the solid oxide fuel cell system. The main idea is to achieve the lowest amount of the sum of square error between the model’s output voltage and the empirical voltage datapoints. To get efficient results, the minimum error value has been achieved by designing a new metaheuristic algorithm, called the Developed version of Battle Royale algorithm. The reason for using this version of Battle Royale algorithm is to achieve results with higher accuracy and better convergence. The proposed technique was then applied to a 96-cell SOFC stack under different temperature and pressure values and its achievements were compared with several different latest methods to show the proposed method’s efficiency.

Suggested Citation

  • Keyvan Karamnejadi Azar & Armin Kakouee & Morteza Mollajafari & Ali Majdi & Noradin Ghadimi & Mojtaba Ghadamyari, 2022. "Developed Design of Battle Royale Optimizer for the Optimum Identification of Solid Oxide Fuel Cell," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9882-:d:884786
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    References listed on IDEAS

    as
    1. Nassef, Ahmed M. & Fathy, Ahmed & Sayed, Enas Taha & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Tanveer, Waqas Hassan & Olabi, A.G., 2019. "Maximizing SOFC performance through optimal parameters identification by modern optimization algorithms," Renewable Energy, Elsevier, vol. 138(C), pages 458-464.
    2. Cai, Wei & Mohammaditab, Rasoul & Fathi, Gholamreza & Wakil, Karzan & Ebadi, Abdol Ghaffar & Ghadimi, Noradin, 2019. "Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach," Renewable Energy, Elsevier, vol. 143(C), pages 1-8.
    3. Wang, Jian & Xu, Yi-Peng & She, Chen & Xu, Ping & Bagal, Hamid Asadi, 2022. "Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm," Energy, Elsevier, vol. 240(C).
    4. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    5. Saeideh Mahdinia & Mehrdad Rezaie & Marischa Elveny & Noradin Ghadimi & Navid Razmjooy, 2021. "Optimization of PEMFC Model Parameters Using Meta-Heuristics," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
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    Cited by:

    1. Lu, Xinyu & Gang, Wenjie & Tu, Zhengkai, 2025. "Recent developments in control and integration of solid oxide fuel cells: From stack to system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 223(C).
    2. Fan, Jiayi & Ni, Yiyang & Qi, Liang & Yuan, Wei & Soh, Yeng Chai, 2025. "A two-stage hybrid stochastic-robust policy of decentralized distributionally energy management for offshore oil and gas platform energy hub coupled with shared energy marine transport fleets," Energy, Elsevier, vol. 328(C).

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