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Online Modeling of a Fuel Cell System for an Energy Management Strategy Design

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  • Mohsen Kandidayeni

    (Department of Electrical & Computer Engineering, e-TESC Laboratory, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
    Department of Electrical & Computer Engineering, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada)

  • Alvaro Macias

    (Department of Electrical & Computer Engineering, e-TESC Laboratory, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
    Department of Electrical & Computer Engineering, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada)

  • Loïc Boulon

    (Department of Electrical & Computer Engineering, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada)

  • João Pedro F. Trovão

    (Department of Electrical & Computer Engineering, e-TESC Laboratory, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada)

Abstract

An energy management strategy (EMS) efficiently splits the power among different sources in a hybrid fuel cell vehicle (HFCV). Most of the existing EMSs are based on static maps while a proton exchange membrane fuel cell (PEMFC) has time-varying characteristics, which can cause mismanagement in the operation of a HFCV. This paper proposes a framework for the online parameters identification of a PMEFC model while the vehicle is under operation. This identification process can be conveniently integrated into an EMS loop, regardless of the EMS type. To do so, Kalman filter (KF) is utilized to extract the parameters of a PEMFC model online. Unlike the other similar papers, special attention is given to the initialization of KF in this work. In this regard, an optimization algorithm, shuffled frog-leaping algorithm (SFLA), is employed for the initialization of the KF. The SFLA is first used offline to find the right initial values for the PEMFC model parameters using the available polarization curve. Subsequently, it tunes the covariance matrices of the KF by utilizing the initial values obtained from the first step. Finally, the tuned KF is employed online to update the parameters. The ultimate results show good accuracy and convergence improvement in the PEMFC characteristics estimation.

Suggested Citation

  • Mohsen Kandidayeni & Alvaro Macias & Loïc Boulon & João Pedro F. Trovão, 2020. "Online Modeling of a Fuel Cell System for an Energy Management Strategy Design," Energies, MDPI, vol. 13(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3713-:d:386625
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    References listed on IDEAS

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    Cited by:

    1. Hyun Sung Kang & Myong-Hwan Kim & Yoon Hyuk Shin, 2020. "Thermodynamic Modeling and Performance Analysis of a Combined Power Generation System Based on HT-PEMFC and ORC," Energies, MDPI, vol. 13(23), pages 1-18, November.
    2. Guangjin Pan & Yunpeng Bai & Huihui Song & Yanbin Qu & Yang Wang & Xiaofei Wang, 2023. "Hydrogen Fuel Cell Power System—Development Perspectives for Hybrid Topologies," Energies, MDPI, vol. 16(6), pages 1-16, March.
    3. Mohamed Derbeli & Cristian Napole & Oscar Barambones, 2021. "Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System," Mathematics, MDPI, vol. 9(17), pages 1-18, August.
    4. Chiara Dall’Armi & Davide Pivetta & Rodolfo Taccani, 2021. "Health-Conscious Optimization of Long-Term Operation for Hybrid PEMFC Ship Propulsion Systems," Energies, MDPI, vol. 14(13), pages 1-20, June.
    5. Tom Fletcher & Kambiz Ebrahimi, 2020. "The Effect of Fuel Cell and Battery Size on Efficiency and Cell Lifetime for an L7e Fuel Cell Hybrid Vehicle," Energies, MDPI, vol. 13(22), pages 1-18, November.

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