An electricity demand-based planning of electric vehicle charging infrastructure

2017 ◽  
Vol 22 (5) ◽  
pp. 449-454 ◽  
Author(s):  
Yongxia Dai ◽  
Min Liu
Keyword(s):  
Electric Vehicle ◽  
Electricity Demand ◽  
Charging Infrastructure ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

Universal Electric Vehicle Charging Infrastructure Analysis Tool

2020 ◽  
Author(s):  
Iosu Aizpuru ◽  
Asier Arruti ◽  
Jon Anzola ◽  
Unai Iraola ◽  
Mikel Mazuela ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Analysis Tool ◽  
Charging Infrastructure ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

scholarly journals Methodology for assessing electric vehicle charging infrastructure business models

Energy Policy ◽  
2016 ◽  
Vol 89 ◽  
pp. 284-293 ◽  
Author(s):  
Carlos Madina ◽  
Inmaculada Zamora ◽  
Eduardo Zabala
Keyword(s):  
Electric Vehicle ◽  
Business Models ◽  
Charging Infrastructure ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

scholarly journals Service Time Analysis For Electric Vehicle Charging Infrastructure

2018 ◽  
Vol 8 (2) ◽  
pp. 818
Author(s):  
Junghoon Lee ◽  
Gyung-Leen Park
Keyword(s):  
Electric Vehicle ◽  
Service Time ◽  
Open Data ◽  
Energy Integration ◽  
Charging Infrastructure ◽  
Tourist Attractions ◽  
Electric Vehicle Charging ◽  
Charging Time ◽  
Main Observation ◽  
Vehicle Charging

This paper analyzes electric vehicle charging patterns in Jeju City, taking advantage of open software such as MySQL, Hadoop, and R, as well as open data obtained from the real-time charger monitoring system currently in operation. Main observation points lie in average service time, maximum service time, and the number of transactions, while we measure the effect of both temporal and spatial factors to them. According to the analysis result, the average service time is almost constant for all parameters. The charging time of 88.7 % transactions ranges from 10 to 40 minutes, while abnormally long transactions occupy just 3.4 % for fast chargers. The day-by-day difference in the number of charging transactions is 28.6 % at maximum, while Wednesday shows the largest number of transactions. Additionally, geographic information-based analysis tells that the charging demand is concentrated in those regions having many tourist attractions and administrative offices. With this analysis, it is possible to predict when a charger will be idle and allocate it to another service such as V2G or renewable energy integration.

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