A Novel Genetic Algorithm-based Emergent Electric Vehicle Charging Scheduling Scheme

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE
Pages4289-4292
ISBN (Electronic)978-1-7281-4878-6
Publication statusPublished - Oct 2019

Publication series

NameIEEE Industrial Electronics Society
ISSN (Print)1553-572X
ISSN (Electronic)2577-1647

Conference

Title45th Annual Conference of the IEEE Industrial Electronics Society (IECON)
PlacePortugal
CityLisbon
Period14 - 17 October 2019

Abstract

In recent years, electric vehicles (EVs) have been widely applied to improve environment. The EV could provide environmentally friendly transportation but have the demerit of low battery capacity. Rapid charging by charging stations (CS) is critically needed especially for those drivers in long distance trip. Thus, a routing optimization problem for EVs charging should be addressed. Furthermore, this problem becomes more practical when the EV density is high at peak. In this scenario EVs are only allowed to obtain energy that render them able to arrive at the destination. In this paper, we formulate an emergent EV charging optimization problem in EV high density area which has not been discussed in related work and a novel genetic algorithm based emergent charging scheduling (GECS) scheme is proposed. The genetic algorithm (GA) is presented to simplify the multi-objectives optimization process in this case. Furthermore, incorporation of the Earliest Deadline First (EDF) which indicates the minimum recharging deadline time as the subject and Nearest Job First (NJF) which indicates the minimum recharging path as the subject into genetic optimization process can relieve the charging emergent condition and improve optimized results. The simulation results show that the proposed scheme can provide an optimal solution to minimize the average distance and waiting time for emergent charging in EV high density region.

Research Area(s)

  • electric vehicles, charging stations, genetic algorithm, emergent charging

Citation Format(s)

A Novel Genetic Algorithm-based Emergent Electric Vehicle Charging Scheduling Scheme. / Ren, Junming; Wang, Hao; Wei, Yang; Liu, Yucheng; Tsang, Kim Fung; Lai, Loi Lei; Chung, Lee Chi.

Proceedings IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2019. p. 4289-4292 (IEEE Industrial Electronics Society).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review