URL

http://publications.energyresearch.ucf.edu/wp-content/uploads/2018/06/IEEE_20141215_Gusrialdi_Qu_Simaan.pdf

Keywords

Electric Vehicles; Charging stations; Traffic management; Distributed algorithms; Highway infrastructure

Abstract

This is a re-print version of a publication for the 53rd IEEE Conference on Decision and Control, Los Angeles, California, USA, December 15-17, 2014. Due to their limited ranges, electric vehicles (EVs)need to be periodically charged during their long-distance travels on a highway. Compared to the fossil-fuel powered vehicles, the charging of a single EV takes much more time(up to 30 mins versus 2 mins). As the number of EVs on highways increases, adequate charging infrastructure needs to be put in place. Nonetheless waiting times for EVs to get charged at service stations could still vary significantly unless an appropriate scheduling coordination is in place and individual EVs make correct decisions about their choice of charging locations. This paper attempts to address both the system-level scheduling problem and the individual control problem, while requiring only distributed information about EVs and their charging at service stations along a highway. Specifically, we first develop a higher-level distributed scheduling algorithm to optimize the operation of the overall charging network. The scheduling algorithm uses only local information of traffic flows measured at the neighboring service stations (nodes), and it aims at adjusting the percentage of the EVs to be charged at individual stations so that all the charging resources along the highway are well (uniformly) utilized and the total waiting time is minimized. Then, a lower level cooperative control law is designed for individual EVs to decide whether or not it should charge its battery when approaching a specific service station by meeting the published scheduling level while taking into account its own battery constraint. Analytical designs are presented and their performance improvement is illustrated using simulation.

Date Published

12-15-2014

Identifiers

160

Notes

The 53rd IEEE Conference on Decision and Control, Los Angeles, California, USA, December 15-17, 2014

Subjects

Electric vehicles; Battery charging stations (Electric vehicles); Traffic flow; Algorithms; Highway engineering

Local Subjects

Electric Vehicles

Type

Text; Document

Creator (Linked Data)

Gusrialdi, Azwirman [LC]

Collection

FSEC Energy Research Center® Collection

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Rights Statement

In Copyright