Analysis of Factors Influencing Fast Charging Behavior Based on Data of Connected Electric Vehicles

被引：0
|
作者：
Yang Y. [1 ]
Tan Z.-F. [1 ]
Jiao G.-X. [2 ]
机构：
[1] School of Economics and Management, North China Electric Power University, Beijing
[2] Beijing Key Laboratory of Cooperative Vehicle Infrastructure System and Safety Control, Beijing
来源：
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2020年 / 20卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Charging behavior prediction; Fast charging behavior; Influencing factors analysis; Logistic regression; Private electric vehicle; Urban traffic;
D O I：
10.16097/j.cnki.1009-6744.2020.05.013
中图分类号：
学科分类号：
摘要：
The research of this paper is based on the data of connected electric vehicles in Beijing. Firstly, electric vehicle trips are extracted with the type of charging behavior, and potential factors influencing the fast charging behavior are analyzed. Then, a logistic regression model is developed to identify the factors influencing the fast charging behavior, which includes available driving ranges, travel distance, and travel time. Finally, based on the significant influencing factors, a model is established to predict the fast charging behavior of private EVs. The prediction results show that the model has good prediction performance. The research results could help to optimize the charging behavior of private electric vehicles and improve the charging efficiency. Copyright © 2020 by Science Press.
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页码：86 / 92
页数：6
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