交通运输系统工程与信息

交通运输系统工程与信息 ›› 2017, Vol. 17 ›› Issue (1): 106-111.

• 智能交通系统与信息技术 • 上一篇    下一篇

基于数据驱动的物流电动汽车充电行为分析

毕军*1，2，张文艳1，赵小梅1，张廷1   

  1. 1. 北京交通大学城市复杂交通系统理论与技术教育部重点实验室，北京100044； 2. 北京城市交通协同创新中心，北京100044
  • 收稿日期:2016-04-21 修回日期:2016-11-04 出版日期:2017-02-25 发布日期:2017-02-27
  • 作者简介:毕军（1973-），男，山东济宁人，教授.
  • 基金资助:

    国家自然科学基金/ National Natural Science Foundation of China（714710014）；中央高校基本科研业务费/ The Fundamental Research Funds for the Central Universities (T15JB00150)；山东省重点研发计划/Key Research and Development Program of Shandong Province（2016GGX105004）

Charging Behavior Analysis of Electric Logistic Vehicle Based on Data Driven

BI Jun, ZHANGWen-yan, ZHAO Xiao-mei, ZHANG Ting   

  1. 1. MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, Beijing Jiaotong University, Beijing 100044, China; 2. Center of Cooperative Innovation for Beijing Metropolitan Transportation, Beijing 100044, China
  • Received:2016-04-21 Revised:2016-11-04 Online:2017-02-25 Published:2017-02-27

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被引次数

3

摘要：

为研究城市配送中物流电动汽车用户的充电行为规律，本文采集70 辆物流电 动车2014 年一年的充放电数据，并采用数据挖掘相关分析方法，建立考虑物流电动汽车 的充电电量状态及充电时刻的充电行为模型.研究结果表明：用户一般在SOC 为30%~ 50%时为车辆进行充电，车辆开始充电时的剩余电量服从μ=0.48、σ=0.22 的正态分布；车 辆开始充电时刻主要集中在14:00-16:00 之间.通过数据实验证明本文所建立的充电行为 模型具有较高的精确性，同时具有较好的实用性，为车辆的充电调度和用户的出行安排 提供科学的决策支持.

关键词: 城市交通, 充电行为, 数据挖掘, 物流电动汽车, 充电需求

Abstract:

In order to explore the charging behavior roles of the electric logistic vehicle users in the city distribution, this paper adopts the analysis methods in data mining to research on the state of charge (SOC) and charging time based on the data of charge and discharge of 70 electric logistic vehicles operating during 2014. Moreover, a charging behavior model is established. The results show that: most drivers tend to charge the electric logistic vehicle when the SOC ranges from 30% to 50% ; the remaining electricity before charging follows the normal distribution with the parameters are μ =0.48, σ =0.22; the charging time are mainly ranges from 14:00 to 16:00. The experimental results show that this charging behavior model has a high accuracy and good practicability, and provide scientific making decision support about charging dispatch with the enterprises as well as the charging schedule with the users.

Key words: urban traffic, charging behavior, data mining, electric logistics vehicles, charging demand

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