IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i12p3423-d188554.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/12/3423/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/12/3423/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Jaszczur, Marek & Hassan, Qusay & Palej, Patryk & Abdulateef, Jasim, 2020. "Multi-Objective optimisation of a micro-grid hybrid power system for household application," Energy, Elsevier, vol. 202(C).
    4. Benim, Ali Cemal & Pfeiffelmann, Björn & Ocłoń, Paweł & Taler, Jan, 2019. "Computational investigation of a lifted hydrogen flame with LES and FGM," Energy, Elsevier, vol. 173(C), pages 1172-1181.
    5. Ceran, Bartosz, 2019. "The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer," Energy, Elsevier, vol. 167(C), pages 853-865.
    6. Hongshan Zhao & Junyang Xu & Kunyu Xu & Jingjie Sun & Yufeng Wang, 2022. "Optimal Allocation Method of Source and Storage Capacity of PV-Hydrogen Zero Carbon Emission Microgrid Considering the Usage Cost of Energy Storage Equipment," Energies, MDPI, vol. 15(13), pages 1-18, July.
    7. Nicu Bizon & Alin Gheorghita Mazare & Laurentiu Mihai Ionescu & Phatiphat Thounthong & Erol Kurt & Mihai Oproescu & Gheorghe Serban & Ioan Lita, 2019. "Better Fuel Economy by Optimizing Airflow of the Fuel Cell Hybrid Power Systems Using Fuel Flow-Based Load-Following Control," Energies, MDPI, vol. 12(14), pages 1-17, July.
    8. Ifaei, Pouya & Tayerani Charmchi, Amir Saman & Loy-Benitez, Jorge & Yang, Rebecca Jing & Yoo, ChangKyoo, 2022. "A data-driven analytical roadmap to a sustainable 2030 in South Korea based on optimal renewable microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    9. Amole, Abraham Olatide & Owosibo, Rachael Abiola & Adewuyi, Oludamilare Bode & Oladipo, Stephen & Imarhiagbe, Nosagiagbon Owomano, 2024. "Comparative analysis of control strategies for solar photovoltaic/diesel power system for stand-alone applications," Renewable Energy, Elsevier, vol. 226(C).
    10. Fahd A. Alturki & Emad Mahrous Awwad, 2021. "Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems," Energies, MDPI, vol. 14(2), pages 1-20, January.
    11. Zhu, Lingfeng & Wang, Yinshun & Guo, Yuetong & Liu, Wei & Hu, Chengyang, 2023. "Current decay and compensation of a closed-loop HTS magnet in non-uniform magnetic fields based on electro-magneto-thermal semi-analytical analysis," Energy, Elsevier, vol. 277(C).
    12. Chen, Scarlett & Kumar, Anikesh & Wong, Wee Chin & Chiu, Min-Sen & Wang, Xiaonan, 2019. "Hydrogen value chain and fuel cells within hybrid renewable energy systems: Advanced operation and control strategies," Applied Energy, Elsevier, vol. 233, pages 321-337.
    13. 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.
    14. Wang, Jing & Kang, Lixia & Huang, Xiankun & Liu, Yongzhong, 2021. "An analysis framework for quantitative evaluation of parametric uncertainty in a cooperated energy storage system with multiple energy carriers," Energy, Elsevier, vol. 226(C).
    15. Elkholy, M.H. & Senjyu, Tomonobu & Elymany, Mahmoud & Gamil, Mahmoud M. & Talaat, M. & Masrur, Hasan & Ueda, Soichiro & Lotfy, Mohammed Elsayed, 2024. "Optimal resilient operation and sustainable power management within an autonomous residential microgrid using African vultures optimization algorithm," Renewable Energy, Elsevier, vol. 224(C).
    16. Ghaffari, Abolfazl & Askarzadeh, Alireza, 2020. "Design optimization of a hybrid system subject to reliability level and renewable energy penetration," Energy, Elsevier, vol. 193(C).
    17. Nicu Bizon & Phatiphat Thounthong, 2021. "A Simple and Safe Strategy for Improving the Fuel Economy of a Fuel Cell Vehicle," Mathematics, MDPI, vol. 9(6), pages 1-29, March.
    18. Yang, Bo & Zhu, Tianjiao & Zhang, Xiaoshun & Wang, Jingbo & Shu, Hongchun & Li, Shengnan & He, Tingyi & Yang, Lei & Yu, Tao, 2020. "Design and implementation of Battery/SMES hybrid energy storage systems used in electric vehicles: A nonlinear robust fractional-order control approach," Energy, Elsevier, vol. 191(C).
    19. Xiao, Xianyong & Zhang, Mingshun & Yang, Ruohuan & Chen, Xiaoyuan & Zheng, Zixuan, 2024. "Superconducting magnetic energy storage based modular interline dynamic voltage restorer for renewable-based MTDC network," Applied Energy, Elsevier, vol. 371(C).
    20. Akram, Umer & Nadarajah, Mithulananthan & Shah, Rakibuzzaman & Milano, Federico, 2020. "A review on rapid responsive energy storage technologies for frequency regulation in modern power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3423-:d:188554. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.