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

Mojgan Fayyazi, Paramjotsingh Sardar, Sumit Infent Thomas, Roonak Daghigh, Ali Jamali, Thomas Esch, Hans Kemper, Reza Langari and Hamid Khayyam ()
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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
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

Sustainability, 2023, vol. 15, issue 6, 1-38

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.

Keywords: intelligent energy management; artificial intelligence; machine learning; fuel cell vehicle; intelligent control; optimization system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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