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Fast Model Predictive Control of PEM Fuel Cell System Using the L 1 Norm

Author

Listed:
  • Robert Nebeluk

    (Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland)

  • Maciej Ławryńczuk

    (Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland)

Abstract

This work describes the development of a fast Model Predictive Control (MPC) algorithm for a Proton Exchange Membrane (PEM) fuel cell. The MPC cost-function used considers the sum of absolute values of predicted control errors (the L 1 norm). Unlike previous approaches to nonlinear MPC-L 1 , in which quite complicated neural approximators have been used, two analytical approximators of the absolute value function are utilised. An advanced trajectory linearisation is performed on-line. As a result, an easy-to-solve quadratic optimisation task is derived. All implementation details of the discussed algorithm are detailed for two considered approximators. Furthermore, the algorithm is thoroughly compared with the classical MPC-L 2 method in which the sum of squared predicted control errors is minimised. A multi-criteria control quality assessment is performed as the MPC-L 1 and MPC-L 2 algorithms are compared using four control quality indicators. It is shown that the presented MPC-L 1 scheme gives better results for the PEM.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5157-:d:864038
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    References listed on IDEAS

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    1. 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.
    2. Sara Luciani & Andrea Tonoli, 2022. "Control Strategy Assessment for Improving PEM Fuel Cell System Efficiency in Fuel Cell Hybrid Vehicles," Energies, MDPI, vol. 15(6), pages 1-17, March.
    3. Jing Chen & Chenghui Zhang & Ke Li & Yuedong Zhan & Bo Sun, 2020. "Hybrid Adaptive Control for PEMFC Gas Pressure," Energies, MDPI, vol. 13(20), pages 1-13, October.
    4. 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.
    5. Jonas Schröter & Daniel Frank & Valentin Radke & Christiane Bauer & Josef Kallo & Caroline Willich, 2022. "Influence of Low Inlet Pressure and Temperature on the Compressor Map Limits of Electrical Turbo Chargers for Airborne Fuel Cell Applications," Energies, MDPI, vol. 15(8), pages 1-13, April.
    6. Yin, Cong & Song, Yating & Liu, Meiru & Gao, Yan & Li, Kai & Qiao, Zemin & Tang, Hao, 2022. "Investigation of proton exchange membrane fuel cell stack with inversely phased wavy flow field design," Applied Energy, Elsevier, vol. 305(C).
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    Cited by:

    1. Reza Ghasemi & Mehdi Sedighi & Mostafa Ghasemi & Bita Sadat Ghazanfarpoor, 2023. "Design of a Fuzzy Adaptive Voltage Controller for a Nonlinear Polymer Electrolyte Membrane Fuel Cell with an Unknown Dynamical System," Sustainability, MDPI, vol. 15(18), pages 1-15, September.

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    More about this item

    Keywords

    proton exchange membrane fuel cell; model predictive control; optimisation; L1 cost function;
    All these keywords.

    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance

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