实际数据驱动的纯电动汽车途中充电潜在偏好行为分析

Empirical Data Driven Approach for Modeling the Latent En-Route Charging Preference Behavior of Battery Electric Vehicle Users

摘要:

为分析纯电动汽车途中充电行为特征，提出基于位置识别算法的途中充电段甄别方法，以初始剩余电池电量、上次充电后行驶里程、充电时长和充电速度为核心指标，利用K-means聚类方法进行途中充电潜在偏好行为分析与分类，解析城市典型途中充电偏好行为特征。基于300辆150 km续航里程的纯电动汽车实际运行数据，分析表明，纯电动汽车用户的途中充电潜在偏好行为可聚类为低焦虑快充、高焦虑快充、随时加电和目的地充电4种模式，纯电动汽车用户偏好快速的途中充电模式。研究成果为充电桩的科学合理布局提供技术支撑。

Abstract:

In order to characterize the en-route charging behavior of battery electric vehicle （BEV） users， the location identification algorithm for identifying the en-route charging event was proposed and the K-means clustering method， including： the initial remaining power， the mileage after the last charging， the charging time and the charging speed， was used to classify the latent en-route charging behavior of BEV users and extract the typical en-route charging preference behavior characteristics. Based on the empirical longitudinal data of 300 BEVs with a battery range of about 150 km， the en-route charging behavior of BEV users are clustered into： the low anxiety and fast charging mode， the high anxiety and fast charging mode， the anytime charging mode and the destination charging mode. BEV users prefer the fast charging mode. The research results provide technical support for the scientific and rational deployment of charging stations.