IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i16p9882-d884786.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/16/9882/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/16/9882/
    Download Restriction: no
    ---><---

    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. 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).
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    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. Liu, Lijun & Qian, Jin & Hua, Li & Zhang, Bin, 2022. "System estimation of the SOFCs using fractional-order social network search algorithm," Energy, Elsevier, vol. 255(C).
    2. 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).
    3. Li, Tianyu & Liu, Huiying & Wang, Hui & Yao, Yongming, 2020. "Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles," Energy, Elsevier, vol. 198(C).
    4. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    5. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    6. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization," Energy, Elsevier, vol. 204(C).
    7. Da Liu & Kun Sun & Han Huang & Pingzhou Tang, 2018. "Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
    8. Olabi, A.G. & Wilberforce, Tabbi & Abdelkareem, Mohammad Ali, 2021. "Fuel cell application in the automotive industry and future perspective," Energy, Elsevier, vol. 214(C).
    9. Wang, Erlei & Xia, Jiangying & Li, Jia & Sun, Xianke & Li, Hao, 2022. "Parameters exploration of SOFC for dynamic simulation using adaptive chaotic grey wolf optimization algorithm," Energy, Elsevier, vol. 261(PA).
    10. Abdelmonem Draz & Mahmoud M. Elkholy & Attia El-Fergany, 2022. "Over-Current Relays Coordination Including Practical Constraints and DGs: Damage Curves, Inrush, and Starting Currents," Sustainability, MDPI, vol. 14(5), pages 1-22, February.
    11. Sun, Xianke & Wang, Gaoliang & Xu, Liuyang & Yuan, Honglei & Yousefi, Nasser, 2021. "Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm," Energy, Elsevier, vol. 237(C).
    12. 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).
    13. Abdul Ghani Olabi & Nabila Shehata & Hussein M. Maghrabie & Lobna A. Heikal & Mohammad Ali Abdelkareem & Shek Mohammod Atiqure Rahman & Sheikh Khaleduzzaman Shah & Enas Taha Sayed, 2022. "Progress in Solar Thermal Systems and Their Role in Achieving the Sustainable Development Goals," Energies, MDPI, vol. 15(24), pages 1-31, December.
    14. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
    15. Lu, Xiaohui & Yang, Yang & Wang, Peifang & Fan, Yiming & Yu, Fangzhong & Zafetti, Nicholas, 2021. "A new converged Emperor Penguin Optimizer for biding strategy in a day-ahead deregulated market clearing price: A case study in China," Energy, Elsevier, vol. 227(C).
    16. Wu, Cong & Li, Jiaxuan & Liu, Wenjin & He, Yuzhe & Nourmohammadi, Samad, 2023. "Short-term electricity demand forecasting using a hybrid ANFIS–ELM network optimised by an improved parasitism–predation algorithm," Applied Energy, Elsevier, vol. 345(C).
    17. Muhammad Ali & Krishneel Prakash & Carlos Macana & Ali Kashif Bashir & Alireza Jolfaei & Awais Bokhari & Jiří Jaromír Klemeš & Hemanshu Pota, 2022. "Modeling Residential Electricity Consumption from Public Demographic Data for Sustainable Cities," Energies, MDPI, vol. 15(6), pages 1-16, March.
    18. Zhimin Guo & Zhiyuan Ye & Pengcheng Ni & Can Cao & Xiaozhao Wei & Jian Zhao & Xing He, 2023. "Intelligent Digital Twin Modelling for Hybrid PV-SOFC Power Generation System," Energies, MDPI, vol. 16(6), pages 1-21, March.
    19. Rajabion, Lila & Khorraminia, Marieh & Andjomshoaa, Amineh & Ghafouri-Azar, Mona & Molavi, Homa, 2019. "A new model for assessing the impact of the urban intelligent transportation system, farmers’ knowledge and business processes on the success of green supply chain management system for urban distri," Journal of Retailing and Consumer Services, Elsevier, vol. 50(C), pages 154-162.
    20. Enas Taha Sayed & Hegazy Rezk & Abdul Ghani Olabi & Mohamed R. Gomaa & Yahia B. Hassan & Shek Mohammad Atiqure Rahman & Sheikh Khaleduzzaman Shah & Mohammad Ali Abdelkareem, 2022. "Application of Artificial Intelligence to Improve the Thermal Energy and Exergy of Nanofluid-Based PV Thermal/Nano-Enhanced Phase Change Material," Energies, MDPI, vol. 15(22), pages 1-13, November.

    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:14:y:2022:i:16:p:9882-:d:884786. 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.