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An Evolutionary Computation Approach for the Online/On-Board Identification of PEM Fuel Cell Impedance Parameters with A Diagnostic Perspective

Author

Listed:
  • Walter Zamboni

    (DIEM—Università Degli Studi di Salerno—Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy)

  • Giovanni Petrone

    (DIEM—Università Degli Studi di Salerno—Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy)

  • Giovanni Spagnuolo

    (DIEM—Università Degli Studi di Salerno—Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy)

  • Davide Beretta

    (European Institute for Energy Research (EIFER), 76131 Karlsruhe, Germany)

Abstract

Online/on-board diagnosis would help to improve fuel cell system durability and output power. Therefore, it is a feature the manufacturers may wish to provide for final users to increase the attractiveness of their product. This add-on requires suitable stack models, parametric identification tools and diagnostic algorithms to be run on low-cost embedded systems, ensuring a good trade-off between accuracy and computation time. In this paper, a computational approach for the impedance parameter identification of polymer electrolyte membrane fuel cell stack is proposed. The method is based on an evolutionary algorithm including sub-population and migration features, which improves the exploration capability of the search space. The goal of the evolutionary algorithm is to find the set of parameters that minimizes an objective function, representing the mismatch between two impedance plots in a normalized plane. The first plot is associated with experimental impedance and the second is computed on the basis of the identified parameters using a circuit model. Three kinds of impedance models, characterized by increasing computational complexity, are used, depending on the experimental data—a linear model made of resistors and capacitors, the Fouquet model and the Dhirde model. Preliminary analysis of the experimental impedance data may evidence correlations among parameters, which can be exploited to reduce the search space of an evolutionary algorithm. The computational approach is validated with literature data in a simulated environment and with experimental data. The results show good accuracy and a computational performance that fits well with the commercial embedded system hardware resources. The implementation of the approach on a low-cost off-the-shelf device achieves small computation times, confirming the suitability of such an approach to online/on-board applications. From a diagnostic perspective, the paper outlines a diagnostic approach based on the identified impedance parameters, on the basis of a small set of experimental data including fuel cell stack faulty conditions.

Suggested Citation

  • Walter Zamboni & Giovanni Petrone & Giovanni Spagnuolo & Davide Beretta, 2019. "An Evolutionary Computation Approach for the Online/On-Board Identification of PEM Fuel Cell Impedance Parameters with A Diagnostic Perspective," Energies, MDPI, vol. 12(22), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4374-:d:287903
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    References listed on IDEAS

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    1. Facci, Andrea L. & Ubertini, Stefano, 2018. "Analysis of a fuel cell combined heat and power plant under realistic smart management scenarios," Applied Energy, Elsevier, vol. 216(C), pages 60-72.
    2. Samuel Simon Araya & Fan Zhou & Simon Lennart Sahlin & Sobi Thomas & Christian Jeppesen & Søren Knudsen Kær, 2019. "Fault Characterization of a Proton Exchange Membrane Fuel Cell Stack," Energies, MDPI, vol. 12(1), pages 1-17, January.
    3. Zhang, Tong & Wang, Peiqi & Chen, Huicui & Pei, Pucheng, 2018. "A review of automotive proton exchange membrane fuel cell degradation under start-stop operating condition," Applied Energy, Elsevier, vol. 223(C), pages 249-262.
    4. Jouin, Marine & Bressel, Mathieu & Morando, Simon & Gouriveau, Rafael & Hissel, Daniel & Péra, Marie-Cécile & Zerhouni, Noureddine & Jemei, Samir & Hilairet, Mickael & Ould Bouamama, Belkacem, 2016. "Estimating the end-of-life of PEM fuel cells: Guidelines and metrics," Applied Energy, Elsevier, vol. 177(C), pages 87-97.
    5. Chakraborty, Uday K. & Abbott, Travis E. & Das, Sajal K., 2012. "PEM fuel cell modeling using differential evolution," Energy, Elsevier, vol. 40(1), pages 387-399.
    6. Antonio Guarino & Giovanni Petrone & Walter Zamboni, 2019. "Improving the Performance of a Dual Kalman Filter for the Identification of PEM Fuel Cells in Impedance Spectroscopy Experiments," Energies, MDPI, vol. 12(17), pages 1-18, September.
    7. Gong, Wenyin & Yan, Xuesong & Liu, Xiaobo & Cai, Zhihua, 2015. "Parameter extraction of different fuel cell models with transferred adaptive differential evolution," Energy, Elsevier, vol. 86(C), pages 139-151.
    8. Mengbo Ji & Zidong Wei, 2009. "A Review of Water Management in Polymer Electrolyte Membrane Fuel Cells," Energies, MDPI, vol. 2(4), pages 1-50, November.
    9. Uday K. Chakraborty, 2019. "Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm," Energies, MDPI, vol. 12(16), pages 1-26, August.
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    Cited by:

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    2. 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).

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