A review of electric vehicle charging session open data

2021 ◽  
Author(s):  
Yvenn Amara-Ouali ◽  
Pascal Massart ◽  
Jean-Michel Poggi ◽  
Yannig Goude ◽  
Hui Yan
Keyword(s):  
Electric Vehicle ◽  
Open Data ◽  
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.

scholarly journals A Review of Electric Vehicle Load Open Data and Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2233
Author(s):  
Yvenn Amara-Ouali ◽  
Yannig Goude ◽  
Pascal Massart ◽  
Jean-Michel Poggi ◽  
Hui Yan
Keyword(s):  
Electric Vehicle ◽  
Open Data ◽  
Reproducible Research ◽  
Statistical Characterization ◽  
Charging Station ◽  
Load Models ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
Electric Vehicle Adoption ◽  
Open Datasets

The field of electric vehicle charging load modelling has been growing rapidly in the last decade. In light of the Paris Agreement, it is crucial to keep encouraging better modelling techniques for successful electric vehicle adoption. Additionally, numerous papers highlight the lack of charging station data available in order to build models that are consistent with reality. In this context, the purpose of this article is threefold. First, to provide the reader with an overview of the open datasets available and ready to be used in order to foster reproducible research in the field. Second, to review electric vehicle charging load models with their strengths and weaknesses. Third, to provide suggestions on matching the models reviewed to six datasets found in this research that have not previously been explored in the literature. The open data search covered more than 860 repositories and yielded around 60 datasets that are relevant for modelling electric vehicle charging load. These datasets include information on charging point locations, historical and real-time charging sessions, traffic counts, travel surveys and registered vehicles. The models reviewed range from statistical characterization to stochastic processes and machine learning and the context of their application is assessed.

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