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Model-Predictive-Control-Based Reference Governor for Fuel Cells in Automotive Application Compared with Performance from a Real Vehicle

Author

Listed:
  • Martin Vrlić

    (Institut für Mechanik und Mechatronik, Technische Universität Wien, Getreidemarkt 9, 1060 Vienna, Austria)

  • Daniel Ritzberger

    (AVL List GmbH, Hans-List-Platz 1, 8020 Graz, Austria)

  • Stefan Jakubek

    (Institut für Mechanik und Mechatronik, Technische Universität Wien, Getreidemarkt 9, 1060 Vienna, Austria)

Abstract

In this paper, a real-time capable reference governor superordinate model predictive controller (RG-MPC) is developed for fuel cell (FC) control suitable for automotive application. The RG-MPC provides reference trajectories for the subordinate proportional-integral (PI) controllers, which act directly on the FC system. Antiwindup and decoupling schemes, which are common problems in multivariable PI control, are unnecessary, given that the RG-MPC can inherently consider constraints and multivariable systems. The PI dynamics are incorporated into the prediction model used for control, ensuring the feasibility of the provided references for the PI controllers. The successive linearization technique is used in the RG-MPC to cope with the model’s nonlinear nature in real-time. The concept has been illustrated in a simulation scenario featuring efficient and safe power control of an FC stack in automotive application using real driving data obtained from an in-house-built FC vehicle. This work is the first step towards upgrading an existing, PI-based control scheme without the necessity of completely rebuilding the interface.

Suggested Citation

  • Martin Vrlić & Daniel Ritzberger & Stefan Jakubek, 2021. "Model-Predictive-Control-Based Reference Governor for Fuel Cells in Automotive Application Compared with Performance from a Real Vehicle," Energies, MDPI, vol. 14(8), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2206-:d:536795
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    References listed on IDEAS

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    1. Daniel Ritzberger & Christoph Hametner & Stefan Jakubek, 2020. "A Real-Time Dynamic Fuel Cell System Simulation for Model-Based Diagnostics and Control: Validation on Real Driving Data," Energies, MDPI, vol. 13(12), pages 1-20, June.
    2. Iqbal, M.T., 2003. "Modeling and control of a wind fuel cell hybrid energy system," Renewable Energy, Elsevier, vol. 28(2), pages 223-237.
    3. Daud, W.R.W. & Rosli, R.E. & Majlan, E.H. & Hamid, S.A.A. & Mohamed, R. & Husaini, T., 2017. "PEM fuel cell system control: A review," Renewable Energy, Elsevier, vol. 113(C), pages 620-638.
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    Cited by:

    1. Chiara Bersani & Marco Fossa & Antonella Priarone & Roberto Sacile & Enrico Zero, 2021. "Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse," Energies, MDPI, vol. 14(11), pages 1-21, June.
    2. Zhang Peng Du & Andraž Kravos & Christoph Steindl & Tomaž Katrašnik & Stefan Jakubek & Christoph Hametner, 2021. "Physically Motivated Water Modeling in Control-Oriented Polymer Electrolyte Membrane Fuel Cell Stack Models," Energies, MDPI, vol. 14(22), pages 1-20, November.

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