IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v185y2021icp218-237.html
   My bibliography  Save this article

Robust parameter estimation of a PEMFC via optimization based on probabilistic model building

Author

Listed:
  • Blanco-Cocom, Luis
  • Botello-Rionda, Salvador
  • Ordoñez, L.C.
  • Valdez, S. Ivvan

Abstract

In this work, we approximated a set of unknown physical parameters for a semi-empirical mathematical model of a PEMFC. We used an Estimation of Distribution Algorithm (EDA) known as UMDAG to find the tuple that best reproduces the experimental polarization curve. We tackled non-derivable objective functions to perform robust parameter estimation. We compared the sum of the squared error with published results, and the sum and the median of the absolute error values were used to diminish or remove the effect of possible noise or outliers. Since the UMDAG requires a single user-given parameter (the population size) and presents a natural reduction of the variance, it was possible to introduce a variance-based stopping criterion. The obtained results were compared with the most up-to-date evolutionary algorithms, demonstrating that this proposal is competitive. We used four previously reported experimental datasets to get the parameters or validate them. Two of them were used to test the method and to compare it with reported results of recent bio-inspired metaheuristics. Then, we used the identified parameters to simulate the cases of the remaining data sets validating the correct estimation. Finally, we introduced a posterior statistical analysis (hypothesis test), which provided further information about dependencies and the impact of each parameter on the cell performance.

Suggested Citation

  • Blanco-Cocom, Luis & Botello-Rionda, Salvador & Ordoñez, L.C. & Valdez, S. Ivvan, 2021. "Robust parameter estimation of a PEMFC via optimization based on probabilistic model building," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 218-237.
    • Handle: RePEc:eee:matcom:v:185:y:2021:i:c:p:218-237
    DOI: 10.1016/j.matcom.2020.12.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475420304766
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2020.12.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Kandidayeni, M. & Macias, A. & Khalatbarisoltani, A. & Boulon, L. & Kelouwani, S., 2019. "Benchmark of proton exchange membrane fuel cell parameters extraction with metaheuristic optimization algorithms," Energy, Elsevier, vol. 183(C), pages 912-925.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pan, Mingzhang & Li, Chao & Liao, Jinyang & Lei, Han & Pan, Chengjie & Meng, Xianpan & Huang, Haozhong, 2020. "Design and modeling of PEM fuel cell based on different flow fields," Energy, Elsevier, vol. 207(C).
    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. Mohamed Louzazni & Sameer Al-Dahidi & Marco Mussetta, 2020. "Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique," Sustainability, MDPI, vol. 12(19), pages 1-23, October.
    5. 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).
    6. Seleem, Sameh I. & Hasanien, Hany M. & El-Fergany, Attia A., 2021. "Equilibrium optimizer for parameter extraction of a fuel cell dynamic model," Renewable Energy, Elsevier, vol. 169(C), pages 117-128.
    7. Samuel Raafat Fahim & Hany M. Hasanien & Rania A. Turky & Abdulaziz Alkuhayli & Abdullrahman A. Al-Shamma’a & Abdullah M. Noman & Marcos Tostado-Véliz & Francisco Jurado, 2021. "Parameter Identification of Proton Exchange Membrane Fuel Cell Based on Hunger Games Search Algorithm," Energies, MDPI, vol. 14(16), pages 1-21, August.
    8. 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.
    9. Kandidayeni, M. & Macias, A. & Boulon, L. & Kelouwani, S., 2020. "Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies," Applied Energy, Elsevier, vol. 274(C).
    10. Kandidayeni, M. & Macias, A. & Khalatbarisoltani, A. & Boulon, L. & Kelouwani, S., 2019. "Benchmark of proton exchange membrane fuel cell parameters extraction with metaheuristic optimization algorithms," Energy, Elsevier, vol. 183(C), pages 912-925.
    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).
    12. En-Jui Liu & Yi-Hsuan Hung & Che-Wun Hong, 2021. "Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction," Energies, MDPI, vol. 14(3), pages 1-16, January.
    13. 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.
    14. 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).
    15. Hsun-Heng Tsai & Chyun-Chau Fuh & Jeng-Rong Ho & Chih-Kuang Lin, 2021. "Design of Optimal Controllers for Unknown Dynamic Systems through the Nelder–Mead Simplex Method," Mathematics, MDPI, vol. 9(16), pages 1-14, August.
    16. Jian Sun & Ling Wang & Dianxuan Gong, 2023. "A Joint Optimization Algorithm Based on the Optimal Shape Parameter–Gaussian Radial Basis Function Surrogate Model and Its Application," Mathematics, MDPI, vol. 11(14), pages 1-20, July.
    17. 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).
    18. Saeideh Mahdinia & Mehrdad Rezaie & Marischa Elveny & Noradin Ghadimi & Navid Razmjooy, 2021. "Optimization of PEMFC Model Parameters Using Meta-Heuristics," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    19. Hasanien, Hany M. & Shaheen, Mohamed A.M. & Turky, Rania A. & Qais, Mohammed H. & Alghuwainem, Saad & Kamel, Salah & Tostado-Véliz, Marcos & Jurado, Francisco, 2022. "Precise modeling of PEM fuel cell using a novel Enhanced Transient Search Optimization algorithm," Energy, Elsevier, vol. 247(C).
    20. Ćalasan, Martin & Abdel Aleem, Shady H.E. & Hasanien, Hany M. & Alaas, Zuhair M. & Ali, Ziad M., 2023. "An innovative approach for mathematical modeling and parameter estimation of PEM fuel cells based on iterative Lambert W function," Energy, Elsevier, vol. 264(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:185:y:2021:i:c:p:218-237. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.