基于网联数据的电动车快速充电影响因素分析

基于北京市私家电动汽车网联数据，按照充电行为类型提取车辆行程，并对行程中影响快速充电行为的潜在因素进行细致分析；基于Logistic回归模型进行显著性影响因素识别，结果表明，电动汽车续航里程、出行距离、出行时间等因素显著影响电动汽车的快速充电行为；最后，基于显著影响因素建立模型，对私家电动汽车快速充电行为进行预测，预测结果表明，预测模型具有较好的预测效果和可靠度.本文研究成果将有助于优化私家电动汽车的充电行为，提高充电效率.

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

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.