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A perturbation strategy for fuel consumption minimization in polymer electrolyte membrane fuel cells: Analysis, Design and FPGA implementation

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  • Ramos-Paja, Carlos Andrés
  • Spagnuolo, Giovanni
  • Petrone, Giovanni
  • Mamarelis, Emilio

Abstract

In this paper a perturbation approach to the minimization of hydrogen consumption in polymer electrolyte membrane fuel cell systems is introduced. The control technique is mainly based on the minimization of the current produced by the fuel cell by means of the Perturb and Observe method. The controller works in conjunction with the dc/dc converter which is usually connected at the fuel cell system terminals and processing the power it produces: the electrical power requested by the load is provided at the minimum current, so that the minimum hydrogen consumption is ensured. The technique is explained by means of some results obtained by using a fuel cell dynamic model which was experimentally validated on a Ballard Nexa 1.2kW commercially available system. One of the main features of the fuel cell system model used in this paper is its ability in virtually sensing the value of the oxygen excess ratio: such a possibility is employed in order to implement the proposed model based control approach. The manuscript shows the advantages of the proposed control technique by means of simulation results and gives the details of the implementation of the algorithm into a digital device, thus an FPGA. The implementation is also used in order to confirm the effectiveness of the control technique: simulations and tests using the hardware-in-the-loop concept, both in steady state conditions as well as during load transients, are provided. The time behavior of the oxygen excess ratio, which is an important variable in any fuel cell application, obtained by means of the proposed technique is also shown.

Suggested Citation

  • Ramos-Paja, Carlos Andrés & Spagnuolo, Giovanni & Petrone, Giovanni & Mamarelis, Emilio, 2014. "A perturbation strategy for fuel consumption minimization in polymer electrolyte membrane fuel cells: Analysis, Design and FPGA implementation," Applied Energy, Elsevier, vol. 119(C), pages 21-32.
    • Handle: RePEc:eee:appene:v:119:y:2014:i:c:p:21-32
    DOI: 10.1016/j.apenergy.2013.12.037
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    References listed on IDEAS

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    1. Matraji, Imad & Laghrouche, Salah & Jemei, Samir & Wack, Maxime, 2013. "Robust control of the PEM fuel cell air-feed system via sub-optimal second order sliding mode," Applied Energy, Elsevier, vol. 104(C), pages 945-957.
    2. Bizon, N., 2010. "On tracking robustness in adaptive extremum seeking control of the fuel cell power plants," Applied Energy, Elsevier, vol. 87(10), pages 3115-3130, October.
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    Cited by:

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    2. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    3. Besseris, George J., 2014. "Using qualimetric engineering and extremal analysis to optimize a proton exchange membrane fuel cell stack," Applied Energy, Elsevier, vol. 128(C), pages 15-26.
    4. Zhang, Tie-qing & Malik, Fawad Rahim & Jung, Seunghun & Kim, Young-Bae, 2022. "Hydrogen production and temperature control for DME autothermal reforming process," Energy, Elsevier, vol. 239(PA).
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    6. Ipsakis, Dimitris & Ouzounidou, Martha & Papadopoulou, Simira & Seferlis, Panos & Voutetakis, Spyros, 2017. "Dynamic modeling and control analysis of a methanol autothermal reforming and PEM fuel cell power system," Applied Energy, Elsevier, vol. 208(C), pages 703-718.
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    9. Nicu Bizon & Mihai Oproescu, 2018. "Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/FC-Based Hybrid Power Systems Based on Load-Following Control," Energies, MDPI, vol. 11(12), pages 1-32, December.
    10. Nicu Bizon & Phatiphat Thounthong & Damien Guilbert, 2020. "Efficient Operation of the Hybrid Power System Using an Optimal Fueling Strategy and Control of the Fuel Cell Power Based on the Required Power Tracking Algorithm," Sustainability, MDPI, vol. 12(22), pages 1-26, November.
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