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Development of a Hydrogen Fuel Cell Prototype Vehicle Supported by Artificial Intelligence for Green Urban Transport

Author

Listed:
  • Krisztián Kun

    (Department of Innovative Vehicles and Materials, GAMF Faculty of Mechanical Engineering and Computer Science, John von Neumann University, H-6000 Kecskemét, Hungary)

  • Lóránt Szabó

    (Department of Innovative Vehicles and Materials, GAMF Faculty of Mechanical Engineering and Computer Science, John von Neumann University, H-6000 Kecskemét, Hungary)

  • Erika Varga

    (Hydrogen Technology Research Center, John von Neumann University, H-6000 Kecskemét, Hungary)

  • Dávid István Kis

    (Department of Automotive Technologies, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary)

Abstract

In the automotive sector, the zero emissions area has been dominated by battery electric vehicles. However, prospective users cite charging times, large batteries, and the deployment of charging stations as a counter-argument. Hydrogen will offer a solution to these areas, in the future. This research focuses on the development of a prototype three-wheeled vehicle that is named Neumann H2. It integrates state-of-the-art energy storage systems, demonstrating the benefits of solar-, battery-, and hydrogen-powered drives. Of crucial importance for the R&D platform is the system’s ability to record its internal states in a time-synchronous format, providing valuable data for researchers and developers. Given that the platform is equipped with the ROS2 Open-Source interface, the data are recorded in a standardized format. Energy management is supported by artificial intelligence of the “Reinforcement Learning” type, which selects the optimal energy source for operation based on different layers of high-fidelity maps. In addition to powertrain control, the vehicle also uses artificial intelligence to detect the environment. The vehicle’s environment-sensing system is essentially designed to detect, distinguish, and select environmental elements through image segmentation using camera images and then to provide feedback to the user via displays.

Suggested Citation

  • Krisztián Kun & Lóránt Szabó & Erika Varga & Dávid István Kis, 2024. "Development of a Hydrogen Fuel Cell Prototype Vehicle Supported by Artificial Intelligence for Green Urban Transport," Energies, MDPI, vol. 17(7), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1519-:d:1362010
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    References listed on IDEAS

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    1. Jamila Snoussi & Seifeddine Ben Elghali & Mohamed Benbouzid & Mohamed Faouzi Mimouni, 2018. "Auto-Adaptive Filtering-Based Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-20, August.
    2. Mariana Pimenta Alves & Waseem Gul & Carlos Alberto Cimini Junior & Sung Kyu Ha, 2022. "A Review on Industrial Perspectives and Challenges on Material, Manufacturing, Design and Development of Compressed Hydrogen Storage Tanks for the Transportation Sector," Energies, MDPI, vol. 15(14), pages 1-32, July.
    3. Shafiee, Shahriar & Topal, Erkan, 2009. "When will fossil fuel reserves be diminished?," Energy Policy, Elsevier, vol. 37(1), pages 181-189, January.
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