Site selection optimization of charging station based on rapid clustering of electric vehicle driving data

王妍; 吴传申; 高山

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Site selection optimization of charging station based on rapid clustering of electric vehicle driving data

更新时间：2026-03-05

- Site selection optimization of charging station based on rapid clustering of electric vehicle driving data
- Vol. 23, Issue 3, (2021)
- 作者机构：
  
  1. 东南大学电气工程学院,南京,210096
  2. 江苏省智能电网技术与装备重点实验室,南京,210096
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2021
- 稿件说明：
移动端阅览
王妍, 吴传申, 高山. Site selection optimization of charging station based on rapid clustering of electric vehicle driving data[J]. 2021, 23(3).
DOI：

王妍, 吴传申, 高山. Site selection optimization of charging station based on rapid clustering of electric vehicle driving data[J]. 2021, 23(3). DOI：

摘要

随着电动汽车的规模化发展，急需对充电站进行合理的选址规划以满足实际需求。对电动汽车交通轨迹数据进行建模分析，采用谱聚类方法实现电动汽车行驶数据的快速聚类。针对快速聚类结果，以充电站选址后的建设及经济运维成本最小为目标，实现充电站的最优选址规划。算例结果表明，算法规划建立的充电站能够在满足用户使用方便性的基础上，使得年度经济成本最小化。

Abstract

With the scaled development of electric vehicle

it is urgent to make reasonable site selection planning for charging stations to meet the actual needs. Electric vehicle traffic trajectory data are modeled and analyzed

and spectral clustering method is adopted to realize rapid clustering of electric vehicle driving data. According to the results of rapid clustering

the optimal location planning of charging stations is realized with the goal of minimizing the cost of construction economic operation and maintenance after the location of charging stations. The result of the calculation example shows that the charging station established can satisfy the user’s convenience and minimize the annual economic cost.

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