Charging behavior modeling of battery electric vehicle drivers on long-distance trips. (December 2022) - Ge, Yanbo; MacKenzie, Don
Abstract: Using the data from an interactive stated choice experiment, we analyzed battery electric vehicle (BEV) users' fast-charging choices on long-distance trips using both static and dynamic discrete choices models (SDCMs and DDCMs). The results show that battery state of charge (SOC) and the ability to reach the next station without deviating from the original plan are the primary factors influencing charging decisions. Charging cost, time, the detour time to reach a station, and the amenities are statistically significant predictors, but less important than SOC and the ability to complete the trip as planned. The comparison of the SDCMs and DDCMs shows that SDCMs have better goodness-of-fit than more complicated DDCMs. By comparing the relative size of the coefficient, we estimated the monetary value of increasing charging power, moving the charging stations closer to highway exits, and having amenities such as restrooms, restaurants, and Wi-Fi at the charging stations.