多源数据驱动下公共充电站智能选址优化研究

doi:10.3976/j.issn.1002-4026.2025061

山东科学

• 交通运输 •

多源数据驱动下公共充电站智能选址优化研究

何佳^1*，胡艳磊¹，王涛²

1. 北京工业大学交通工程北京市重点实验室，北京 100124；2. 合肥工业大学汽车与交通工程学院，合肥 230009

收稿日期:2025-05-19 接受日期:2025-06-13 上线日期:2026-01-07
通信作者: 何佳 E-mail:hejia@bjut.edu.cn
作者简介:何佳（1990—），男，博士，副教授，研究方向为电动汽车充电设施选址、民航交通。E-mail: hejia@bjut.edu.cn
基金资助:
国家自然科学基金项目(72371004、72231001)

Evaluation and location optimization of public charging stations using multisource spatiotemporal data

HE Jia1*, HU Yanlei1, WANG Tao2

1.Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China; 2. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China

Received:2025-05-19 Accepted:2025-06-13 Online:2026-01-07
Contact: HE Jia E-mail:hejia@bjut.edu.cn

摘要/Abstract

摘要： 随着新能源汽车保有量的持续增长，城市公共充电基础设施在布局密度与空间配置方面存在滞后，供需失衡问题日益突出，已成为制约绿色出行发展的关键瓶颈。为应对这一挑战，以北京市顺义区为研究区域，提出一套融合多源数据的公共充电站综合评估与选址优化方法。研究融合车辆轨迹与兴趣点（POI）等多源时空数据，构建了城市充电需求时空分布模型，以精细刻画不同功能区的动态充电负荷；进一步通过交通可达性分析与充电行为仿真，量化评估了现有站点的布局有效性并识别出服务盲区。结果表明，顺义区部分高强度需求区域的服务能力不足，存在明显覆盖缺口。为此，研究利用K均值聚类算法识别出未被满足的需求核心，提出了具备建设优先级的新增站点规划方案。为缓解区域充电供需不均、提升城市公共充电设施的布局科学性与系统适应性提供了理论依据与实践路径。

关键词: 充电站选址, 布局优化方案, 动态充电仿真, 服务半径, 充电需求分析

Abstract: With the increasing number of new energy vehicles globally, the density and spatial distribution of urban public charging infrastructure lag behind demand. Moreover, supply–demand imbalance has become an increasingly prominent issue, posing a key bottleneck in the development of green mobility. To address this challenge, this study considers Shunyi District, Beijing, as a case study to propose a comprehensive evaluation and location optimization method for public charging stations using multisource spatiotemporal data. By combining multisource spatiotemporal data such as vehicle trajectories and points of interest, we constructed a spatiotemporal distribution model of urban charging demand to accurately characterize the dynamic charging loads in different functional zones. Furthermore, through traffic accessibility analysis and charging behavior simulation, the effectiveness of the layout of the existing stations is quantitatively assessed and service blind spots are identified. The results reveal that the service capacity in some high-demand areas of Shunyi District is insufficient, with considerable coverage gaps. To overcome this issue, we used the K-means clustering algorithm to identify the cores of unmet demand and proposed a priority-based construction plan for new stations. This study provides a theoretical basis and a practical approach for mitigating regional supply–demand imbalances and enhancing the scientific layout and systemic adaptability of urban public charging facilities.

Key words: charging station location, layout optimization plan, dynamic charging simulation, service radius, charging demand analysis

中图分类号:

U469.72

何佳, 胡艳磊, 王涛. 多源数据驱动下公共充电站智能选址优化研究[J]. 山东科学, 0, (): 1-.

HE Jia, HU Yanlei , WANG Tao. Evaluation and location optimization of public charging stations using multisource spatiotemporal data[J]. Shandong Science, 0, (): 1-.

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多源数据驱动下公共充电站智能选址优化研究

Evaluation and location optimization of public charging stations using multisource spatiotemporal data

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