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A Neural Network-Based Four Phases Interleaved Boost Converter for Fuel Cell System Applications

Author

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  • El Manaa Barhoumi

    (Department of Electrical and Computer Engineering, College of Engineering, Dhofar University, Salalah 211, Oman
    Laboratoire Analyse, Conception et Commande des Systèmes (LR11ES20), Ecole Nationale d’Ingénieurs de Tunis, Université de Tunis El Manar, Tunis 1002, Tunisia)

  • Ikram Ben Belgacem

    (Laboratoire de Génie Mécanique, Ecole Nationale d’Ingénieurs de Monastir, Université de Monastir, Monastir 5019, Tunisia)

  • Abla Khiareddine

    (Research Unit on Study of Industrial Systems and Renewable Energy (ESIER), Université de Monastir, National Engineering School of Monastir, Université de Monastir, Monastir 5019, Tunisia)

  • Manaf Zghaibeh

    (Department of Electrical and Computer Engineering, College of Engineering, Dhofar University, Salalah 211, Oman)

  • Iskander Tlili

    (Energy and Thermal Systems Laboratory, National Engineering School of Monastir, Street Ibn El Jazzar, Monastir 5019, Tunisia)

Abstract

This paper presents a simple strategy for controlling an interleaved boost converter that is used to reduce the current fluctuations in proton exchange membrane fuel cells, with high impact on the fuel cell lifetime. To keep the output voltage at the desired reference value under the strong fluctuations of the fuel flow rate, fuel supply pressure, and temperature, a neural network controller is developed and implemented using Matlab-Simulink (R2012b, MathWorks limited, London, UK). The advantage of this controller resides in its simplicity, where limited number of tests are carried out using Matlab-Simulink to construct it. To investigate the robustness of the proposed converter and the neural network controller, strong variations of the fuel flow rate, fuel supply pressure, temperature and air supply pressure are applied to both the fuel cell and the neural network controller of the converter. The simulation results show the effectiveness and the robustness of the both the proposed controller and converter to control the load voltage and minimize the current and voltage ripples. As a result of that, fuel cell current oscillations are considerably reduced on the one hand, while on the other hand, the load voltage is stabilized during transient variations of the fuel cell inputs.

Suggested Citation

  • El Manaa Barhoumi & Ikram Ben Belgacem & Abla Khiareddine & Manaf Zghaibeh & Iskander Tlili, 2018. "A Neural Network-Based Four Phases Interleaved Boost Converter for Fuel Cell System Applications," Energies, MDPI, vol. 11(12), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3423-:d:188554
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    References listed on IDEAS

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    1. Khiareddine, Abla & Ben Salah, Chokri & Rekioua, Djamila & Mimouni, Mohamed Faouzi, 2018. "Sizing methodology for hybrid photovoltaic /wind/ hydrogen/battery integrated to energy management strategy for pumping system," Energy, Elsevier, vol. 153(C), pages 743-762.
    2. Bizon, Nicu, 2018. "Effective mitigation of the load pulses by controlling the battery/SMES hybrid energy storage system," Applied Energy, Elsevier, vol. 229(C), pages 459-473.
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    Cited by:

    1. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    2. Fadhil Khadoum Alhousni & Firas Basim Ismail Alnaimi & Paul C. Okonkwo & Ikram Ben Belgacem & Hassan Mohamed & El Manaa Barhoumi, 2023. "Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability," Sustainability, MDPI, vol. 15(11), pages 1-13, May.
    3. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.
    4. Tao Yang & Yong Liao, 2019. "Discrete Sliding Mode Control Strategy for Start-Up and Steady-State of Boost Converter," Energies, MDPI, vol. 12(15), pages 1-13, August.
    5. Rongchao Niu & Hongyu Zhang & Jian Song, 2023. "Model Predictive Control of DC–DC Boost Converter Based on Generalized Proportional Integral Observer," Energies, MDPI, vol. 16(3), pages 1-16, January.

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