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Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm

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  • Yang, Zixuan
  • Liu, Qian
  • Zhang, Leiyu
  • Dai, Jialei
  • Razmjooy, Navid

Abstract

In this paper, a new optimal method is proposed to select unknown parameters of the proton exchange membrane fuel cell (PEMFC) models. The method was based on minimizing the sum of squared error (SSE) value between the experimental output voltage and the estimated output voltage for the PEMFC stack. The minimization is based on employing a new improved design of the Barnacles Mating Optimization (IBMO) algorithm for increasing the system accuracy and robustness. The method is then validated based on two different case studies, including Horizon 500W PEMFC and NedSstack PS6 PEMFCs by comparing its results by the real data and also some well-known methods including Emperor Penguin Optimizer (EPO), Elephant Herding behavior Optimization (EHO) Algorithm, and world cup optimization algorithm (WCO). The results show that the suggested IBMO with 2.11 SSE has the minimum error and the EPO, EHO, and the WCO with 2.13, 2.26, and 2.29 SSE are in the next ranks. Also, for the Horizon 500W, the SSE value of the IBMO, EPO, WCO, and BMO are 0.012, 0.019, 0.029, and 0.031, respectively which shows the suggested method’s superiority. Simulation results indicate that the suggested method has the best agreement with the empirical data.

Suggested Citation

  • Yang, Zixuan & Liu, Qian & Zhang, Leiyu & Dai, Jialei & Razmjooy, Navid, 2020. "Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm," Energy, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:energy:v:212:y:2020:i:c:s0360544220318454
    DOI: 10.1016/j.energy.2020.118738
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    5. Yang, Zirong & Jiao, Kui & Wu, Kangcheng & Shi, Weilong & Jiang, Shangfeng & Zhang, Longhai & Du, Qing, 2021. "Numerical investigations of assisted heating cold start strategies for proton exchange membrane fuel cell systems," Energy, Elsevier, vol. 222(C).
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    7. 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.
    8. 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).
    9. Cai, Yonghua & Wu, Di & Sun, Jingming & Chen, Ben, 2021. "The effect of cathode channel blockages on the enhanced mass transfer and performance of PEMFC," Energy, Elsevier, vol. 222(C).
    10. Abdel-Basset, Mohamed & Mohamed, Reda & El-Fergany, Attia & Chakrabortty, Ripon K. & Ryan, Michael J., 2021. "Adaptive and efficient optimization model for optimal parameters of proton exchange membrane fuel cells: A comprehensive analysis," Energy, Elsevier, vol. 233(C).
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    12. 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).
    13. 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).
    14. 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).
    15. Fan, Lixin & Tu, Zhengkai & Chan, Siew Hwa, 2022. "Technological and Engineering design of a megawatt proton exchange membrane fuel cell system," Energy, Elsevier, vol. 257(C).

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