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

被引:0
|
作者
Huang, Zhen [1 ]
Xiao, Xuechun [1 ]
Gao, Yuan [2 ]
Xia, Yonghong [1 ]
Dragicevic, Tomislav [3 ]
Wheeler, Pat [4 ]
机构
[1] Nanchang Univ, Sch Informat Engn, Nanchang 330031, Peoples R China
[2] Univ Leicester, Sch Engn, Leicester LE1 7RH, Leicestershire, England
[3] Tech Univ Denmark, Dept Elect Engn, DK-2800 Lyngby, Denmark
[4] Univ Nottingham, Power Elect Machines & Control Grp PEMC, Nottingham NG7 2RD, England
关键词
artificial intelligence; digital twin; energy management; intelligent transportation; machine learning; onboard microgrid; reinforcement learning; HYBRID ELECTRIC VEHICLES; MODEL-PREDICTIVE CONTROL; FUEL-ECONOMY; STRATEGY; AIRCRAFT; TIME; POWERTRAIN; FRAMEWORK; ALGORITHM; SYSTEMS;
D O I
10.3390/en16176269
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
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页数:26
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