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Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles

Author

Listed:
  • Mojgan Fayyazi

    (School of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3083, Australia)

  • Paramjotsingh Sardar

    (School of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3083, Australia
    Department of Aerospace Engineering, FH Aachen University of Applied Sciences, 52066 Aachen, Germany)

  • Sumit Infent Thomas

    (Department of Chemical Engineering, Amal Jyothi College of Engineering, Kanjirappally, Kottayam 686518, Kerala, India)

  • Roonak Daghigh

    (Department of Mechanical Engineering, University of Kurdistan, Sanandaj 66177-15175, Iran)

  • Ali Jamali

    (Department of Artificial Intelligence, Kyungpook National University, Daegu 37224, Republic of Korea)

  • Thomas Esch

    (Department of Aerospace Engineering, FH Aachen University of Applied Sciences, 52066 Aachen, Germany)

  • Hans Kemper

    (Department of Aerospace Engineering, FH Aachen University of Applied Sciences, 52066 Aachen, Germany)

  • Reza Langari

    (Engineering Technology and Industrial Distribution (ETID), Texas A & M University, College Station, TX 77843, USA)

  • Hamid Khayyam

    (School of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3083, Australia)

Abstract

Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.

Suggested Citation

  • Mojgan Fayyazi & Paramjotsingh Sardar & Sumit Infent Thomas & Roonak Daghigh & Ali Jamali & Thomas Esch & Hans Kemper & Reza Langari & Hamid Khayyam, 2023. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles," Sustainability, MDPI, vol. 15(6), pages 1-38, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5249-:d:1098549
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    References listed on IDEAS

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    2. Lombardi, Simone & Di Ilio, Giovanni & Tribioli, Laura & Jannelli, Elio, 2023. "Optimal design of an adaptive energy management strategy for a fuel cell tractor operating in ports," Applied Energy, Elsevier, vol. 352(C).
    3. Zhencheng Fan & Zheng Yan & Shiping Wen, 2023. "Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health," Sustainability, MDPI, vol. 15(18), pages 1-20, September.

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