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The Optimization of PEM Fuel-Cell Operating Parameters with the Design of a Multiport High-Gain DC–DC Converter for Hybrid Electric Vehicle Application

Author

Listed:
  • B. Karthikeyan

    (Department of EEE, K. Ramakrishnan College of Technology, Trichy 621112, India)

  • Palanisamy Ramasamy

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai 603203, India)

  • M. Pandi Maharajan

    (Department of EEE, Nadar Saraswathi College of Engineering and Technology, Theni 625531, India)

  • N. Padmamalini

    (Department of Physics, St. Joseph’s Institute of Technology, Chennai 600119, India)

  • J. Sivakumar

    (Department of Electronics and Communication Engineering, St. Joseph’s College of Engineering, Chennai 600119, India)

  • Subhashree Choudhury

    (Department of EEE, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India)

  • George Fernandez Savari

    (OES Technologies, 4056 Blakie Road, London, ON N6L 1P7, Canada)

Abstract

The fossil fuel crisis is a major concern across the globe, and fossil fuels are being exhausted day by day. It is essential to promptly change from fossil fuels to renewable energy resources for transportation applications as they make a major contribution to fossil fuel consumption. Among the available energy resources, a fuel cell is the most affordable for transportation applications because of such advantages as moderate operating temperature, high energy density, and scalable size. It is a challenging task to optimize PEMFC operating parameters for the enhancement of performance. This paper provides a detailed study on the optimization of PEMFC operating parameters using a multilayer feed-forward neural network, a genetic algorithm, and the design of a multiport high-gain DC–DC converter for hybrid electric vehicle application, which is capable of handling both a 6 kW PEMFC and an 80 AH 12 V heavy-duty battery. To trace the maximum power from the PEMFC, the most recent SFO-based MPPT control technique is implemented in this research work. Initially, a multilayer feed-forward neural network is trained using a back-propagation algorithm with experimental data. Then, the optimization phase is separately carried out in a neural-power software environment using a genetic algorithm (GA). The simulation study was carried out using the MATLAB/R2022a platform to verify the converter performance along with the SFO-based MPPT controller. To validate the real-time test bench results, a 0.2 kW prototype model was constructed in the laboratory, and the results were verified.

Suggested Citation

  • B. Karthikeyan & Palanisamy Ramasamy & M. Pandi Maharajan & N. Padmamalini & J. Sivakumar & Subhashree Choudhury & George Fernandez Savari, 2024. "The Optimization of PEM Fuel-Cell Operating Parameters with the Design of a Multiport High-Gain DC–DC Converter for Hybrid Electric Vehicle Application," Sustainability, MDPI, vol. 16(2), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:872-:d:1322590
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    References listed on IDEAS

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    1. Xuanxia Guo & Noradin Ghadimi, 2023. "Optimal Design of the Proton-Exchange Membrane Fuel Cell Connected to the Network Utilizing an Improved Version of the Metaheuristic Algorithm," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    2. Maheshwari, A. & Nageswari, S., 2022. "Real-time state of charge estimation for electric vehicle power batteries using optimized filter," Energy, Elsevier, vol. 254(PB).
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