准确理解电动汽车（EV）驾驶员行为对于长期基础设施规划、电网管理及评估下游经济影响至关重要，但目前仍缺乏个体层面的EV出行数据。本文构建了一个可扩展的分析框架，基于被动采集的高分辨率移动轨迹数据，覆盖美国四大主要都市区逾76万名驾驶员，推断EV拥有状况与充电行为。我们依据驾驶员对充电站与加油站的访问模式差异、访问频次及日常出行特征识别潜在EV驾驶员，并利用各邮政编码区域（zip code）的汇总EV注册统计数据校准推断群体规模。所获EV群体在邮政编码层级上与官方注册数据高度吻合，且其充电模式与独立的充电桩级基准数据集一致，从而为推断结果提供了外部验证。依托该推断群体，我们重构充电事件及其关联的活动模式，考察EV驾驶员如何与周边城市设施互动。相较于非EV驾驶员，EV驾驶员在充电期间对邻近咖啡馆与餐厅的访问率系统性更高，揭示出显著的经济溢出效应；此外，我们发现EV驾驶员存在行程捆绑（trip bundling）行为：在充电日，其单位时间与单位距离内访问的兴趣点（POI）数量多于非充电日。此类模式无法从传统充电会话数据中识别，因其仅记录充电事件本身，缺乏行为背景信息。本研究结果表明，移动数据有望支撑对EV驾驶员‘离桩需求’（off-plug needs）开展更丰富、更具行为基础的理解，为优化充电基础设施部署提供依据。

Accurate insights into electric vehicle (EV) driver behavior are essential for long-term infrastructure planning, grid management, and understanding downstream economic impacts, yet individual level data on EV mobility remains limited. Here, we develop a scalable framework to infer EV ownership and charging behavior from passively collected, high-resolution mobility traces covering over 760,000 drivers across four major U.S. metropolitan areas. We identify likely EV drivers based on distinctive visitation patterns to charging stations and gas stations, frequency of visits, and daily travel behavior, and calibrate cohort size using aggregate EV registration statistics. The resulting EV cohort closely matches official registration data at the zip code level and exhibits charging patterns consistent with independent, charger level benchmark datasets, providing external validation of the inferred population. Leveraging this inferred cohort, we reconstruct charging events and associated activity patterns to examine how EV drivers interact with surrounding urban amenities. Compared to non-EV drivers, EV drivers exhibit systematically higher visitation rates to nearby cafes and restaurants during charging sessions, revealing significant economic spillover effects. Furthermore, we find EV drivers exhibit trip bundling behavior, visiting more POIs over less time and distance on days where they charge versus all other days. These patterns are not observable in conventional charging session data, which lack behavioral context beyond the charging event itself. Our results demonstrate the potential of using mobility data to enable a richer, behaviorally grounded understanding of the off-plug needs of EV drivers, providing a foundation for optimizing charging infrastructure deployment and co-locating complementary urban amenities in an increasingly electrified transportation landscape.