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High-Performance Tracking for Proton Exchange Membrane Fuel Cell System PEMFC Using Model Predictive Control

Author

Listed:
  • Mohamed Derbeli

    (System Engineering and Automation Department, Faculty of Engineering of Vitoria-Gasteiz, Basque Country University (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Asma Charaabi

    (LR11ES20 Laboratory of Analysis, Conception and Control of Systems, National Engineering School of Tunis, University of Tunis El Manar, Tunis 1002, Tunisia)

  • Oscar Barambones

    (System Engineering and Automation Department, Faculty of Engineering of Vitoria-Gasteiz, Basque Country University (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Cristian Napole

    (System Engineering and Automation Department, Faculty of Engineering of Vitoria-Gasteiz, Basque Country University (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

Abstract

Proton exchange membrane (PEM) fuel cell has recently attracted broad attention from many researchers due to its cleanliness, high efficiency and soundless operation. The obtention of high-performance output characteristics is required to overcome the market restrictions of the PEMFC technologies. Therefore, the main aim of this work is to maintain the system operating point at an adequate and efficient power stage with high-performance tracking. To this end, a model predictive control (MPC) based on a global minimum cost function for a two-step horizon was designed and implemented in a boost converter integrated with a fuel cell system. An experimental comparative study has been investigated between the MPC and a PI controller to reveal the merits of the proposed technique. Comparative results have indicated that a reduction of 15.65 % and 86.9 % , respectively, in the overshoot and response time could be achieved using the suggested control structure.

Suggested Citation

  • Mohamed Derbeli & Asma Charaabi & Oscar Barambones & Cristian Napole, 2021. "High-Performance Tracking for Proton Exchange Membrane Fuel Cell System PEMFC Using Model Predictive Control," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1158-:d:558979
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    References listed on IDEAS

    as
    1. Mohammed Yousri Silaa & Mohamed Derbeli & Oscar Barambones & Ali Cheknane, 2020. "Design and Implementation of High Order Sliding Mode Control for PEMFC Power System," Energies, MDPI, vol. 13(17), pages 1-15, August.
    2. Doudou N. Luta & Atanda K. Raji, 2019. "Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack," Energies, MDPI, vol. 12(5), pages 1-15, March.
    3. Mohamed Derbeli & Oscar Barambones & Jose Antonio Ramos-Hernanz & Lassaad Sbita, 2019. "Real-Time Implementation of a Super Twisting Algorithm for PEM Fuel Cell Power System," Energies, MDPI, vol. 12(9), pages 1-20, April.
    4. José de Jesús Rubio & Adrian Gustavo Bravo, 2014. "Optimal Control of a PEM Fuel Cell for the Inputs Minimization," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-7, March.
    5. Zhihao Zhang & Zhe Wu & David Rincon & Panagiotis D. Christofides, 2019. "Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning," Mathematics, MDPI, vol. 7(10), pages 1-25, September.
    6. Mohammed Yousri Silaa & Mohamed Derbeli & Oscar Barambones & Cristian Napole & Ali Cheknane & José María Gonzalez De Durana, 2021. "An Efficient and Robust Current Control for Polymer Electrolyte Membrane Fuel Cell Power System," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    7. Slimane Hadji & Jean-Paul Gaubert & Fateh Krim, 2018. "Real-Time Genetic Algorithms-Based MPPT: Study and Comparison (Theoretical an Experimental) with Conventional Methods," Energies, MDPI, vol. 11(2), pages 1-17, February.
    8. Mostafa Ahmed & Mohamed Abdelrahem & Ralph Kennel, 2020. "Highly Efficient and Robust Grid Connected Photovoltaic System Based Model Predictive Control with Kalman Filtering Capability," Sustainability, MDPI, vol. 12(11), pages 1-22, June.
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    Cited by:

    1. Robert Nebeluk & Maciej Ławryńczuk, 2022. "Fast Model Predictive Control of PEM Fuel Cell System Using the L 1 Norm," Energies, MDPI, vol. 15(14), pages 1-17, July.
    2. Li, Da & Zhang, Zhaosheng & Zhou, Litao & Liu, Peng & Wang, Zhenpo & Deng, Junjun, 2022. "Multi-time-step and multi-parameter prediction for real-world proton exchange membrane fuel cell vehicles (PEMFCVs) toward fault prognosis and energy consumption prediction," Applied Energy, Elsevier, vol. 325(C).
    3. Tri-Cuong Do & Hoai-An Trinh & Kyoung-Kwan Ahn, 2023. "Hierarchical Control Strategy with Battery Dynamic Consideration for a Dual Fuel Cell/Battery Tramway," Mathematics, MDPI, vol. 11(10), pages 1-19, May.
    4. Muhammad Majid Gulzar, 2023. "Maximum Power Point Tracking of a Grid Connected PV Based Fuel Cell System Using Optimal Control Technique," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
    5. Javaid, Usman & Mehmood, Adeel & Iqbal, Jamshed & Uppal, Ali Arshad, 2023. "Neural network and URED observer based fast terminal integral sliding mode control for energy efficient polymer electrolyte membrane fuel cell used in vehicular technologies," Energy, Elsevier, vol. 269(C).

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