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Proton exchange membrane fuel cell model parameter identification based on dynamic differential evolution with collective guidance factor algorithm

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  • Sun, Zhe
  • Cao, Dan
  • Ling, Yawen
  • Xiang, Feng
  • Sun, Zhixin
  • Wu, Fan

Abstract

This paper firstly proposes a dynamic differential evolution algorithm (DDE-CGF) with a collective guiding factor to solve the problem of parameter identification and optimization of proton exchange membrane fuel cell (PEMFC) model. Inspired by the swarm intelligence scheme, a collective guidance factor is designed to accelerate the convergence speed without affecting the convergence accuracy. Moreover, a dynamic scaling factor and the dynamic crossover probability based on evolutionary mechanism are introduced to enhance the diversity of population as well as improve the global searching performance. Through testing eight benchmark functions, the DDE-CGF algorithm exhibits superior performance in both convergence accuracy and speed. Based on the excellent global performance, applying DDE-CGF algorithm to the parameter identification of the PEMFC model, and more accurate parameter values are obtained. Comparing with other algorithms, the result proves that the DDE-CGF algorithm could accurately estimate model parameters and the identified model could greatly describe the dynamical characteristic of the PEMFC model.

Suggested Citation

  • Sun, Zhe & Cao, Dan & Ling, Yawen & Xiang, Feng & Sun, Zhixin & Wu, Fan, 2021. "Proton exchange membrane fuel cell model parameter identification based on dynamic differential evolution with collective guidance factor algorithm," Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220321630
    DOI: 10.1016/j.energy.2020.119056
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    References listed on IDEAS

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    Cited by:

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    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. 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).
    4. Hassan Ali, Hossam & Fathy, Ahmed, 2024. "Reliable exponential distribution optimizer-based methodology for modeling proton exchange membrane fuel cells at different conditions," Energy, Elsevier, vol. 292(C).
    5. 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).
    6. Fan Yang & Xiaoming Xu & Yuehua Li & Dongfang Chen & Song Hu & Ziwen He & Yi Du, 2023. "A Review on Mass Transfer in Multiscale Porous Media in Proton Exchange Membrane Fuel Cells: Mechanism, Modeling, and Parameter Identification," Energies, MDPI, vol. 16(8), pages 1-24, April.
    7. Zhang, Bo & Wang, Rongjie & Jiang, Desong & Wang, Yichun & lin, Anhui & Wang, Jianfeng & Ruan, Bingcong, 2023. "Parameter identification of proton exchange membrane fuel cell based on swarm intelligence algorithm," Energy, Elsevier, vol. 283(C).
    8. Ćalasan, Martin & Micev, Mihailo & Hasanien, Hany M. & Abdel Aleem, Shady H.E., 2024. "PEM fuel cells: Two novel approaches for mathematical modeling and parameter estimation," Energy, Elsevier, vol. 290(C).
    9. Peng He & Xin Zhou & Mingqun Liu & Kewei Xu & Xian Meng & Bo Yang, 2023. "Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell," Energies, MDPI, vol. 16(14), pages 1-30, July.
    10. 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).
    11. 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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