Available range (AR) uncertainty is prevalent in battery electric vehicles (BEVs). AR depends on traffic conditions, weather conditions, road conditions, driving style, etc. This study aims to examine the role of AR uncertainty in BEV users’ en-route charging and charging route choice behavior. A stated preference survey is designed and conducted to explore the two consecutive choice behaviors: (1) en-route charging and (2) charging route choice. Four model specifications for en-route charging and three for charging route choice are defined. AR uncertainty is, for the first time, considered in explaining en-route charging choice behavior, in addition to the conventional choice attributes such as travel time, charging duration, initial AR, average AR, etc. Socio-demographic and vehicle-related factors are also considered in the behavioral modeling. Furthermore, random parameters logit with error components model is utilized to capture the full panel effects. Results indicate that AR uncertainty significantly affects users’ en-route charging choice behavior, thus being indispensable in behavioral modeling. In addition, average AR can modify the association between AR uncertainty and choice decision. Demand for en-route charging will be overestimated when AR uncertainty is not considered. Furthermore, there is considerable inter-respondentand intra-respondent heterogeneity in the en-route charging choice. The effect of AR uncertainty is stronger for female and high-income BEV users. The findings will contribute substantially to better en-route charging demand estimation and optimal deployment of public charging stations, particularly for medium- to long-distance trips.

可用范围 (AR) 不确定性在纯电动汽车 (BEV) 中普遍存在。AR 取决于交通状况、天气状况、道路状况、驾驶方式等。本研究旨在检验 AR 不确定性在 BEV 用户的途中充电和充电路线选择行为中的作用。设计并进行了一项陈述偏好调查，以探索两种连续的选择行为：(1) 途中充电和 (2) 充电路线选择。定义了四种用于途中充电的模型规范和三种用于充电路径选择的模型规范。除了旅行时间、充电持续时间、初始 AR、平均 AR 等传统选择属性外，AR 不确定性首次在解释途中充电选择行为时被考虑。 社会人口和车辆相关因素在行为建模中也考虑了。此外，使用带有误差分量模型的随机参数 logit 来捕获完整的面板效果。结果表明，AR 不确定性显着影响用户的途中充电选择行为，因此在行为建模中不可或缺。此外，平均 AR 可以修改 AR 不确定性和选择决策之间的关联。如果不考虑 AR 不确定性，航路充电需求将被高估。此外，在途中收费选择中存在相当大的受访者间和受访者内部异质性。AR 不确定性对女性和高收入 BEV 用户的影响更大。这些发现将大大有助于更好地估计途中充电需求和优化公共充电站的部署，特别是对于中长途旅行。具有误差分量模型的随机参数 logit 用于捕获完整的面板效果。结果表明，AR 不确定性显着影响用户的途中充电选择行为，因此在行为建模中不可或缺。此外，平均 AR 可以修改 AR 不确定性和选择决策之间的关联。如果不考虑 AR 不确定性，航路充电需求将被高估。此外，在途中收费选择中存在相当大的受访者间和受访者内部异质性。AR 不确定性对女性和高收入 BEV 用户的影响更大。这些发现将大大有助于更好地估计途中充电需求和优化公共充电站的部署，特别是对于中长途旅行。具有误差分量模型的随机参数 logit 用于捕获完整的面板效果。结果表明，AR 不确定性显着影响用户的途中充电选择行为，因此在行为建模中不可或缺。此外，平均 AR 可以修改 AR 不确定性和选择决策之间的关联。如果不考虑 AR 不确定性，航路充电需求将被高估。此外，在途中收费选择中存在相当大的受访者间和受访者内部异质性。AR 不确定性对女性和高收入 BEV 用户的影响更大。这些发现将大大有助于更好地估计途中充电需求和优化公共充电站的部署，特别是对于中长途旅行。