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Modeling and optimization for proton exchange membrane fuel cell stack using aging and challenging P systems based optimization algorithm

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  • Yang, Shipin
  • Chellali, Ryad
  • Lu, Xiaohua
  • Li, Lijuan
  • Bo, Cuimei

Abstract

Accurate models of PEM (proton exchange membrane) fuel cells are of great significance for the analysis and the control for power generation. We present a new semi-empirical model to predict the voltage outputs of PEM fuel cell stacks. We also introduce a new estimation method, called AC-POA (aging and challenging P systems based optimization algorithm) allowing deriving the parameters of the semi-empirical model. In our model, the cathode inlet pressure is selected as an additional factor to modify the expression of concentration over-voltage Vcon for traditional Amphlett's PEM fuel cell model. In AC-POA, the aging-mechanism inspired object updating rule is merged in existing P system. We validate through experiments the effectiveness of AC-POA and the fitting accuracy of our model. Modeling comparison results show that the predictions of our model are the best in terms of fitting to actual sample data.

Suggested Citation

  • Yang, Shipin & Chellali, Ryad & Lu, Xiaohua & Li, Lijuan & Bo, Cuimei, 2016. "Modeling and optimization for proton exchange membrane fuel cell stack using aging and challenging P systems based optimization algorithm," Energy, Elsevier, vol. 109(C), pages 569-577.
    • Handle: RePEc:eee:energy:v:109:y:2016:i:c:p:569-577
    DOI: 10.1016/j.energy.2016.04.093
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    2. Tafaoli-Masoule, M. & Bahrami, A. & Elsayed, E.M., 2014. "Optimum design parameters and operating condition for maximum power of a direct methanol fuel cell using analytical model and genetic algorithm," Energy, Elsevier, vol. 70(C), pages 643-652.
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    5. Xu, Shuhui & Wang, Yong & Wang, Zhi, 2019. "Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder-Mead simplex method," Energy, Elsevier, vol. 173(C), pages 457-467.
    6. Cai, Shanshan & Li, Xu & Yang, Ling & Hua, Zhipeng & Li, Song & Tu, Zhengkai, 2024. "Demand flexibility and its impact on a PEM fuel cell-based integrated energy supply system with humidity control," Renewable Energy, Elsevier, vol. 228(C).
    7. Ángel Encalada-Dávila & Samir Echeverría & Jordy Santana-Villamar & Gabriel Cedeño & Mayken Espinoza-Andaluz, 2021. "Optimization Algorithms: Optimal Parameters Computation for Modeling the Polarization Curves of a PEFC Considering the Effect of the Relative Humidity," Energies, MDPI, vol. 14(18), pages 1-21, September.
    8. Priya, K. & Sathishkumar, K. & Rajasekar, N., 2018. "A comprehensive review on parameter estimation techniques for Proton Exchange Membrane fuel cell modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 121-144.
    9. Wenshang Chen & Yang Liu & Ben Chen, 2022. "Numerical Simulation on Pressure Dynamic Response Characteristics of Hydrogen Systems for Fuel Cell Vehicles," Energies, MDPI, vol. 15(7), pages 1-18, March.
    10. Yang, Zirong & Du, Qing & Jia, Zhiwei & Yang, Chunguang & Xuan, Jin & Jiao, Kui, 2019. "A comprehensive proton exchange membrane fuel cell system model integrating various auxiliary subsystems," Applied Energy, Elsevier, vol. 256(C).
    11. Barzegari, Mohammad M. & Alizadeh, Ebrahim & Pahnabi, Amir H., 2017. "Grey-box modeling and model predictive control for cascade-type PEMFC," Energy, Elsevier, vol. 127(C), pages 611-622.
    12. El-Fergany, Attia A., 2018. "Extracting optimal parameters of PEM fuel cells using Salp Swarm Optimizer," Renewable Energy, Elsevier, vol. 119(C), pages 641-648.
    13. El-Hay, Enas A. & El-Hameed, Mohamed A. & El-Fergany, Attia A., 2018. "Performance enhancement of autonomous system comprising proton exchange membrane fuel cells and switched reluctance motor," Energy, Elsevier, vol. 163(C), pages 699-711.
    14. 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).
    15. Fathy, Ahmed & Rezk, Hegazy, 2018. "Multi-verse optimizer for identifying the optimal parameters of PEMFC model," Energy, Elsevier, vol. 143(C), pages 634-644.
    16. Yang, Zirong & Du, Qing & Jia, Zhiwei & Yang, Chunguang & Jiao, Kui, 2019. "Effects of operating conditions on water and heat management by a transient multi-dimensional PEMFC system model," Energy, Elsevier, vol. 183(C), pages 462-476.

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