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Design optimization and parameter estimation of a PEMFC using nature-inspired algorithms

Soft Computing volume 27, pages 3765–3784 (2023)Cite this article

Abstract

Numerical simulation of proton-exchange membrane fuel cells (PEMFCs) requires an adequate model and precise parameters for reproducing their operational performance quantified by the polarization curve. Bioinspired algorithms are well-suited for optimization. The simulator is stressed by inputting thousands of randomly generated parameters, and hence, a robust numerical model is required. Once the proper model and parameters reproduce the experimental data, they can be used for design improvement. This article proposes a reformulation of a macrohomogeneous mathematical model to provide higher numerical stability to the solutions. We introduce optimization problems for parameter estimation and design optimization by applying three bioinspired algorithms to maximize its performance and minimize the platinum mass loading \(m_\mathrm{{Pt}}\). The results are validated by comparing the experimental polarization curves with those simulated from the estimated parameters. We compare a base design’s performance with the optimized design for maximum performance. We also compare a base design with the optimized design for minimum \(m_\mathrm{{Pt}}\). The results show that the particle swarm optimization requires the lowest computational cost and performs the best in most cases, fitting the experimental data with errors lesser than \(10^{-17}\). The minimization of \(m_\mathrm{{Pt}}\) reduces the amount by 42% compared to the base case.

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Acknowledgements

This article was supported by CONACYT, Mexico, under Grant 100236. S. Ivvan Valdez is supported by Cátedra-CONACYT Grant 7795.

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Author notes

  1. Salvador Botello-Rionda, L. C. Ordoñez, and S. Ivvan Valdez have contributed equally to this work.

Authors and Affiliations

  1. Centro de Investigación en Matemáticas, A.C., Jalisco S/N, Valenciana, 36023, Guanajuato, Guanajuato, Mexico

    Luis Blanco-Cocom & Salvador Botello-Rionda

  2. Unidad de Energía Renovable, Centro de Investigación Científica de Yucatán, Parque Científico Tecnológico de Yucatán, 97302, Merida, Yucatán, Mexico

    L. C. Ordoñez

  3. CONACYT-Centro de Investigación en Ciencias de Información Geoespacial, CENTROGEO, A.C., Parque Tecnológico Sanfandila, 76703, Querétaro, Querétaro, Mexico

    S. Ivvan Valdez

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Appendix A: Parameter values for simulations

Appendix A: Parameter values for simulations

See Tables 7 and 8.

Table 7 Parameter values used in the baseline calculation used in (Song et al. 2004) for dataset 1
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Table 8 Parameter values used in the baseline calculation used in (Khajeh-Hosseini-Dalasm et al. 2010) for dataset 2
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Table 9 Parameter values used in this work to adjust dataset 3 obtained from Salva et al. (2015)
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Blanco-Cocom, L., Botello-Rionda, S., Ordoñez, L.C. et al. Design optimization and parameter estimation of a PEMFC using nature-inspired algorithms. Soft Comput 27, 3765–3784 (2023). https://doi.org/10.1007/s00500-022-07520-y

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Keywords

  • PEM fuel cell
  • Macrohomogeneous mathematical model
  • Bioinspired optimization algorithms
  • Performance maximization
  • Minimization of platinum mass loading