With the rapid growth of private electric vehicle (EV) ownership, the resulting charging load has become an increasingly important issue requiring attention from urban planners and policymakers. Effectively coordinating the layout of EV charging networks with users’ charging behaviors has emerged as a key challenge in this field, while advancing relevant optimization studies necessitates access to charging demand data with high spatiotemporal resolution. This study developed a data-driven framework based on 460,000 cellular base station records. By systematically analyzing the signaling data of hundreds of thousands of mobile users, users’ frequently traveled trajectories within the study area were inferred. Building on the reconstructed travel behavior, an EV energy consumption model was introduced, and the Monte Carlo method was applied to simulate energy consumption dynamics and estimate the potential spatiotemporal distribution of charging demand. This study proposes a feasible approach for deriving intra-day mobility patterns and estimating potential charging demand distributions from large-scale mobile signaling data, providing a data foundation for understanding the dynamic characteristics of urban EV charging demand and optimizing the deployment of public charging infrastructure.