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An Accurate Parameter Estimation Method of the Voltage Model for Proton Exchange Membrane Fuel Cells

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  • Jian Mei

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
    Suzhou Research Institute, Harbin Institute of Technology, Suzhou 215104, China)

  • Xuan Meng

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
    Suzhou Research Institute, Harbin Institute of Technology, Suzhou 215104, China)

  • Xingwang Tang

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Heran Li

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
    Suzhou Research Institute, Harbin Institute of Technology, Suzhou 215104, China)

  • Hany Hasanien

    (Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
    Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Mohammed Alharbi

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Zhen Dong

    (Suzhou SEEEx (Sustainable Electrical Energy Expert) Technology Company, Suzhou 215000, China)

  • Jiabin Shen

    (General Motors Canada Company, Oshawa, ON L1J 0C5, Canada)

  • Chuanyu Sun

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
    Suzhou Research Institute, Harbin Institute of Technology, Suzhou 215104, China)

  • Fulin Fan

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Jinhai Jiang

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
    Suzhou Research Institute, Harbin Institute of Technology, Suzhou 215104, China)

  • Kai Song

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
    Suzhou Research Institute, Harbin Institute of Technology, Suzhou 215104, China)

Abstract

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm 2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

Suggested Citation

  • Jian Mei & Xuan Meng & Xingwang Tang & Heran Li & Hany Hasanien & Mohammed Alharbi & Zhen Dong & Jiabin Shen & Chuanyu Sun & Fulin Fan & Jinhai Jiang & Kai Song, 2024. "An Accurate Parameter Estimation Method of the Voltage Model for Proton Exchange Membrane Fuel Cells," Energies, MDPI, vol. 17(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2917-:d:1414228
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    References listed on IDEAS

    as
    1. Hegazy Rezk & Tabbi Wilberforce & A. G. Olabi & Rania M. Ghoniem & Enas Taha Sayed & Mohammad Ali Abdelkareem, 2023. "Optimal Parameter Identification of a PEM Fuel Cell Using Recent Optimization Algorithms," Energies, MDPI, vol. 16(14), pages 1-20, July.
    2. Ali, M. & El-Hameed, M.A. & Farahat, M.A., 2017. "Effective parameters’ identification for polymer electrolyte membrane fuel cell models using grey wolf optimizer," Renewable Energy, Elsevier, vol. 111(C), pages 455-462.
    3. Kui Jiao & Jin Xuan & Qing Du & Zhiming Bao & Biao Xie & Bowen Wang & Yan Zhao & Linhao Fan & Huizhi Wang & Zhongjun Hou & Sen Huo & Nigel P. Brandon & Yan Yin & Michael D. Guiver, 2021. "Designing the next generation of proton-exchange membrane fuel cells," Nature, Nature, vol. 595(7867), pages 361-369, July.
    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. Abdul Ghani Olabi & Hegazy Rezk & Mohammad Ali Abdelkareem & Tabbi Awotwe & Hussein M. Maghrabie & Fatahallah Freig Selim & Shek Mohammod Atiqure Rahman & Sheikh Khaleduzzaman Shah & Alaa A. Zaky, 2023. "Optimal Parameter Identification of Perovskite Solar Cells Using Modified Bald Eagle Search Optimization Algorithm," Energies, MDPI, vol. 16(1), pages 1-14, January.
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