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

Zhen Huang, Xuechun Xiao, Yuan Gao, Yonghong Xia, Tomislav Dragičević, Pat Wheeler

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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.
Original languageEnglish
Article number6269
JournalEnergies
Volume16
Issue number17
Number of pages26
ISSN1996-1073
DOIs
Publication statusPublished - 2023

Keywords

  • Artificial intelligence
  • Digital twin
  • Energy management
  • Intelligent transportation
  • Machine learning
  • Onboard microgrid
  • Reinforcement learning

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