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Charging-Expense Minimization Through Assignment Rescheduling of Movable Charging Stations in Electric Vehicle Networks.

Authors :
Liu, Linfeng
Qi, Xin
Xi, Zhiyuan
Wu, Jiagao
Xu, Jia
Source :
IEEE Transactions on Intelligent Transportation Systems; Oct2022, Vol. 23 Issue 10, p17212-17223, 12p
Publication Year :
2022

Abstract

Electric vehicles (EVs), as promising components of the sustainable and eco-friendly transportation systems, are being widely adopted to reduce the consumption of fossil fuel and pollution of environments. EVs are usually equipped with wireless modules to support the vehicle to vehicle communications, by which an electric vehicular network (EVN) is formed. In EVN, some EVs are with insufficient battery energy and may exhaust the battery energy before arriving at their destinations, and these EVs are referred to as IEVs. More seriously, IEVs probably cannot find any fixed charging facilities nearby. With the development of mobile charging technology, some movable charging stations (MCSs) are deployed into EVN, and MCSs can actively navigate to charge IEVs. In this paper, an assignment rescheduling mechanism of movable charging stations (ARMM) is proposed, where the MCS assignments are dynamically rescheduled. In ARMM, in order to reduce the charging expenses of IEVs and enhance the proportion of charged IEVs, the assigned IEVs of some MCSs could be switched to other MCSs, while the charging positions of MCSs are selected by minimizing the charging expenses of IEVs and are dynamically altered. Besides, the incentives of assigned IEVs to reduce the charging expenses of unassigned IEVs are proven. Simulation results demonstrate the preferable performance of ARMM, i.e. ARMM can reduce the charging expenses of IEVs and enhance the proportion of charged IEVs effectively. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15249050
Volume :
23
Issue :
10
Database :
Complementary Index
Journal :
IEEE Transactions on Intelligent Transportation Systems
Publication Type :
Academic Journal
Accession number :
160686542
Full Text :
https://doi.org/10.1109/TITS.2022.3154444