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Estimation of the Quick Charging Station for Electric Vehicles based on Location and Population Density Data

descriptionPublicationkeyboard_double_arrow_right Article , Journal 30 Jun 2018 Australia Publisher:The Intelligent Networks and Systems SocietyJournal:International Journal of Intelligent Engineering and Systems, volume 11, pages 233-241 (eissn: 2185-3118,

Authors: Kongjeen, Yuttana; Junlakan, Wannawit; Bhumkittipich, Krischonme; Mithulananthan, Nadarajah;

doi: 10.22266/ijies2018.0630.25

Estimation of the Quick Charging Station for Electric Vehicles based on Location and Population Density Data

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Abstract

This paper has presented the estimation methodology of the quick charging station for electric vehicles (EVs) based on both area and population density data. The proportion of EV owners per number of population in location data; is also used to compute the number of the quick charging stations. The population density data and proportion of EVs owners per number of population in area data are varied from 1 to 6 % and 0.01 to 0.8 %, respectively. The simulation results showed that the number of EVs stations increased and the randomly selected Feeder No.1 was installed at EVs stations; of which range from No.1 to No.4. The total real power loss increased up to 18%. Therefore, this study could be verified that the quick charging stations should be considered both optimal in sizing and location of EVs charging stations.

Country

Australia

Related Organizations

University of Queensland
Australia
Rajamangala University of Technology
Thailand
University of Queensland
Australia
Rajamangala University of Technology
Thailand

Keywords

Electric vehicles, Quick charging stations, 333, 2200 Engineering, 1700 Computer Science, Energy management system

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	7
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average