【目的】电动汽车（electric vehicle，EV）充电负荷时空分布受路网、温度、电动汽车类型等因素影响。为提高EV充电负荷时空分布预测精度，构建了一种融合多方信息的电动汽车充电负荷时空分布预测模型。【方法】通过引入温度与车速对能耗的影响模型，量化外部环境对电动汽车续航的影响；结合充电需求引力模型，将充电站规模、电价、时间成本等因素类比为引力参数，动态修正用户充电站选择行为。同时，改进Dijkstra算法，融合实时路况信息优化充电路径规划。最后累计叠加得到充电总负荷。【结果】Matlab仿真结果表明，私家车与出租车的充电负荷分布存在显著差异。私家车充电负荷在居民区、工作区及商业区分别集中于夜间、白天工作时间段及下班时，而出租车因运营需求，充电负荷呈现早、晚高峰低谷及午间小高峰的特征；所提改进Dijkstra算法通过动态调整路段权重提高了路径规划效率，在相同目的地情况下，行驶时间降低了3.9%；所提充电需求引力模型综合考虑充电站规模、电价、用户时间成本等因素，优化了用户充电站选择行为，计算得到的充电负荷时空分布更趋合理。【结论】文章通过融合多方信息，构建了电动汽车充电负荷时空分布预测模型，揭示了不同类型EV的充电行为差异、温度敏感性及用户决策动态特性。研究成果可为电网负荷调度、充电站规划及有序充电策略制定提供理论支撑。

[Objective] Factors such as road networks， temperature， and electric vehicle （EV） type affect the spatial and temporal distribution of EV charging loads. To improve prediction accuracy， a spatiotemporal EV charging load prediction model is developed by integrating multiparty information. [Methods] By introducing the model of temperature and vehicle speed on energy consumption， the impact of the external environment on EV range is quantified. A charging demand gravity model is also used， incorporating factors such as station size， electricity price， time cost， and gravity parameters. These are used to dynamically adjust user behavior in choosing charging stations. Additionally， the Dijkstra algorithm is improved to plan charging paths more effectively by including real-time road condition data. Finally， the total charging load is accumulated and superimposed. [Results] The MATLAB simulation results showed a significant difference between the charging load distributions of private cars and cabs. The charging loads of private cars in residential， working， and commercial areas are concentrated during nighttime， daytime working hours， and off-duty hours， respectively. In contrast， the charging loads of cabs are characterized by morning and evening peaks， valleys， and small peaks at noon due to operational demand. The proposed improved Dijkstra's algorithm improves the efficiency of path planning by dynamically adjusting road section weights， reducing driving time by 3.9% for the same destination. The proposed charging demand gravity model optimizes users' charging station selection behavior by integrating factors such as charging station size， electricity price， and user time cost， resulting in a more reasonable spatial and temporal distribution of the charging load[Conclusions] This study constructed a spatial and temporal distribution prediction model for electric vehicle charging loads by integrating information from multiple sources. It reveals the differences in the charging behaviors of different types of EVs， their temperature sensitivity， and the dynamic characteristics of user decision-making. The results provide theoretical support for grid load scheduling， charging station planning， and the development of an orderly charging strategy.