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

Sustainability, vol. 15, pp. 5249

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.

Authors 9

1. Mojgan Fayyazi

   RMIT University

   Affiliation as printed

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

2. Paramjotsingh Sardar Aachen Department of Aerospace Engineering

   RMIT University · FH Aachen

   Affiliation as printed

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

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

3. Sumit Infent Thomas

   Amal Jyothi College of Engineering

   Affiliation as printed

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

4. Roonak Daghigh

   University of Kurdistan

   Affiliation as printed

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

5. Ali Jamali

   Kyungpook National University

   Affiliation as printed

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

6. Thomas Esch Aachen Department of Aerospace Engineering

   FH Aachen

   Affiliation as printed

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

7. Hans Kemper Aachen Department of Aerospace Engineering

   FH Aachen

   Affiliation as printed

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

8. Reza Langari

   Texas A&M University

   Affiliation as printed

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

9. Hamid Khayyam corresponding

   RMIT University

   Affiliation as printed

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