Huang, Z.; Xiao, X.; Gao, Y.; Xia, Y.; Dragičević, T.; Wheeler, P. Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications. Energies2023, 16, 6269.
Huang, Z.; Xiao, X.; Gao, Y.; Xia, Y.; Dragičević, T.; Wheeler, P. Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications. Energies 2023, 16, 6269.
Huang, Z.; Xiao, X.; Gao, Y.; Xia, Y.; Dragičević, T.; Wheeler, P. Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications. Energies2023, 16, 6269.
Huang, Z.; Xiao, X.; Gao, Y.; Xia, Y.; Dragičević, T.; Wheeler, P. Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications. Energies 2023, 16, 6269.
Abstract
In the past decades, the world is actively working towards the global goal of net-zero emission. To decrease emissions, there is a notable trend of electrification in transportation, which is a transition from traditional fuel-based systems to electrical power systems onboard different transportation platforms. For this transition, it is important to study the electrical structure, specifically the onboard microgrid, powered by various energy sources. In this paper, traditional energy management strategies for onboard microgrid systems are discussed, which usually require complicated optimization algorithms and high computation capabilities. Driven by the recent advancements in information technologies, artificial intelligence and digital twin have gained much interest within the transportation sector. These technologies can effectively utilize data to achieve intelligent decision-making, optimize resource utilization, and save energy consumption. This paper presents an overview of the usage of these emerging information technologies in energy management strategies, providing an overall summary and classification of the practical applications. In addition, after examining the potential challenges associated with artificial intelligence and digital twin, this paper also discusses future trends in this field.
Keywords
onboard microgrid; intelligent transportation; energy management; artificial intelligence; digital twin; machine learning; reinforcement learning
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
