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

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.