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Safe and Efficient Polymer Electrolyte Membrane Fuel Cell Control Using Successive Linearization Based Model Predictive Control Validated on Real Vehicle Data

Author

Listed:
  • Martin Vrlić

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

  • Daniel Ritzberger

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

  • Stefan Jakubek

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

Abstract

In this paper, a polymer electrolyte membrane fuel cell (PEMFC) stack control study is presented. The goal is to track the transient power demand of a real fuel cell (FC) vehicle while ensuring safe and efficient operation. Due to the dynamically changing power demand, fast transients occur in the internal states of the fuel cell (e.g., pressure, humidity, reactant mass) leading to degradation effects (e.g., high/low membrane overpressure, reactants starvation) which are avoided by imposing safety constraints. Efficiency is considered in terms of internal voltage losses minimization as well as minimization of the power of the compressor used to pressurize the cathode. For solving the optimization problem of power demand tracking, adhering to safety constraints, and maximizing efficiency, model predictive control (MPC) has been chosen. Due to the nonlinearity of the FC system, a successive linearization based MPC (SLMPC) is used to control the FC throughout its operating region. Simulation results show that the power demand can be fulfilled while at the same time ensuring safe operation in terms of adhering to constraints and that the minimization of internal voltage losses and compressor power lead to an approximate 9.5% less hydrogen consumption than in the actual reference vehicle.

Suggested Citation

  • Martin Vrlić & Daniel Ritzberger & Stefan Jakubek, 2020. "Safe and Efficient Polymer Electrolyte Membrane Fuel Cell Control Using Successive Linearization Based Model Predictive Control Validated on Real Vehicle Data," Energies, MDPI, vol. 13(20), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5353-:d:427892
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    References listed on IDEAS

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    1. da Fonseca, R. & Bideaux, E. & Gerard, M. & Jeanneret, B. & Desbois-Renaudin, M. & Sari, A., 2014. "Control of PEMFC system air group using differential flatness approach: Validation by a dynamic fuel cell system model," Applied Energy, Elsevier, vol. 113(C), pages 219-229.
    2. 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.
    3. Meidanshahi, Vida & Karimi, Gholamreza, 2012. "Dynamic modeling, optimization and control of power density in a PEM fuel cell," Applied Energy, Elsevier, vol. 93(C), pages 98-105.
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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. 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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