Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications

Zhen Huang, Xuechun Xiao, Yuan Gao (), Yonghong Xia, Tomislav Dragičević and Pat Wheeler
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Zhen Huang: School of Information Engineering, Nanchang University, Nanchang 330031, China
Xuechun Xiao: School of Information Engineering, Nanchang University, Nanchang 330031, China
Yuan Gao: School of Engineering, University of Leicester, Leicester LE1 7RH, UK
Yonghong Xia: School of Information Engineering, Nanchang University, Nanchang 330031, China
Tomislav Dragičević: Department of Electrical Engineering, Technical University of Denmark, 2800 Lyngby, Denmark
Pat Wheeler: Power Electronics, Machines and Control (PEMC), University of Nottingham, Nottingham NG7 2RD, UK

Energies, 2023, vol. 16, issue 17, 1-26

Abstract: The global objective of achieving net-zero emissions drives a significant electrified trend by replacing fuel-mechanical systems with onboard microgrid (OBMG) systems for transportation applications. Energy management strategies (EMS) for OBMG systems require complicated optimization algorithms and high computation capabilities, while traditional control techniques may not meet these requirements. Driven by the ability to achieve intelligent decision-making by exploring data, artificial intelligence (AI) and digital twins (DT) have gained much interest within the transportation sector. Currently, research on EMS for OBMGs primarily focuses on AI technology, while overlooking the DT. This article provides a comprehensive overview of both information technology, particularly elucidating the role of DT technology. The evaluation and analysis of those emerging information technologies are explicitly summarized. Moreover, this article explores potential challenges in the implementation of AI and DT technologies and subsequently offers insights into future trends.

Keywords: artificial intelligence; digital twin; energy management; intelligent transportation; machine learning; onboard microgrid; reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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