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基于出行链的电动汽车充电行为影响因素分析

于海洋 张路 任毅龙

于海洋, 张路, 任毅龙等 . 基于出行链的电动汽车充电行为影响因素分析[J]. 北京航空航天大学学报, 2019, 45(9): 1732-1740. doi: 10.13700/j.bh.1001-5965.2018.0566
引用本文: 于海洋, 张路, 任毅龙等 . 基于出行链的电动汽车充电行为影响因素分析[J]. 北京航空航天大学学报, 2019, 45(9): 1732-1740. doi: 10.13700/j.bh.1001-5965.2018.0566
YU Haiyang, ZHANG Lu, REN Yilonget al. Influential factors analysis of electric vehicle charging behavior based on trip chain[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1732-1740. doi: 10.13700/j.bh.1001-5965.2018.0566(in Chinese)
Citation: YU Haiyang, ZHANG Lu, REN Yilonget al. Influential factors analysis of electric vehicle charging behavior based on trip chain[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1732-1740. doi: 10.13700/j.bh.1001-5965.2018.0566(in Chinese)

基于出行链的电动汽车充电行为影响因素分析

doi: 10.13700/j.bh.1001-5965.2018.0566
基金项目: 

国家重点研发计划 2018TFB1600702

详细信息
    作者简介:

    于海洋  男, 博士, 副教授, 硕士生导师。主要研究方向:智能车路协同系统、交通大数据、交通控制与仿真

    张路  女, 硕士研究生。主要研究方向:电动汽车的充电行为

    任毅龙  男, 博士, 讲师, 硕士生导师。主要研究方向:智能车路协同系统、交通控制与仿真

    通讯作者:

    任毅龙, E-mail: yilongren@buaa.edu.cn

  • 中图分类号: U121;U469.72+2

Influential factors analysis of electric vehicle charging behavior based on trip chain

Funds: 

National Key R & D Program of China 2018TFB1600702

More Information
  • 摘要:

    随着电动汽车的快速发展,大规模电动汽车充电将给电力系统规划和运行带来不可忽视的影响,研究电动汽车的充电行为及其影响因素,并实时预测潜在的充电行为越发迫切。基于北京市私家电动汽车的历史车联网数据,引入出行链的概念,综合电动汽车充电过程和放电过程的数据,从实际出发考虑影响电动汽车充电行为的多种潜在因素,并通过logistic回归模型分析确定了显著影响充电行为的因素。分别基于单一和多个显著影响因素建立电动汽车充电行为的预测模型,预测结果表明,基于多个显著影响因素的模型准确率更高,且对晴天的预测效果更好。研究成果将有助于优化电动汽车的充电行为,进而提高电动汽车的充电效率。

     

  • 图 1  起始电荷状态的影响

    Figure 1.  Influence of initial state of charge

    图 2  旅程开始时间的影响

    Figure 2.  Influence of journey start time

    图 3  出行链距离的影响

    Figure 3.  Influence of trip chain distance

    图 4  出行链时长的影响

    Figure 4.  Influence of trip chain duration

    图 5  旅程速度的影响

    Figure 5.  Influence of journey speed

    图 6  上一次出行链状态的影响

    Figure 6.  Influence of last trip chain state

    图 7  充电行为预测模型的ROC曲线

    Figure 7.  ROC curves of charging behavior prediction model

    图 8  不同天气情况下充电行为预测模型的ROC曲线

    Figure 8.  ROC curves of charging behavior prediction model under different weather conditions

    表  1  基于出行链的充电行为分布

    Table  1.   Distribution of charging behavior based on trip chains

    充电次数0123
    出行链分布2 37841016747
    下载: 导出CSV

    表  2  2015年8月部分北京天气情况

    Table  2.   Some weather conditions in Beijing in August 2015

    日期最高气温/℃最低气温/℃天气
    2015-08-013123阴转小到中雨
    2015-08-023023多云转阴
    2015-08-033023阵雨转多云
    2015-08-043322晴转多云
    2015-08-053222多云转雷阵雨
    2015-08-063122多云
    下载: 导出CSV

    表  3  出行链充电行为的潜在影响因素

    Table  3.   Potential influential factors affecting charging behavior of travel chain

    因素变量描述取值
    天气目标出行链所在日的天气情况分晴雨天,晴天为0,雨天为1
    旅程开始时间目标出行链开始行驶的时间24 h制,只取出发对应的小时数
    出行链时长目标出行链中车辆行驶的总时长只计放电过程时间,不计充电时间
    出行链距离目标出行链中车辆行驶的总距离旅程中车辆行驶的距离,单位km
    旅程速度目标出行链中车辆的平均行驶速度出行链距离/时长,不计充电时间
    起始电荷状态目标出行链开始时电池的电荷状态开始时剩余容量/电池容量,取值0~100
    上一次出行链状态目标出行链之前一次出行链充电行为取值0和1,1为有充电,0为无充电
    下载: 导出CSV

    表  4  基于潜在影响因素的模型分析结果

    Table  4.   Model analysis results based on potential influential factors

    因素变量BSEWalsdfPExp(B)
    旅程开始时间0.0320.0173.4851< 0.011.033
    出行链时长0076.8431< 0.011.000
    出行链距离0.0120.0047.8171< 0.011.004
    旅程速度0.0430.01116.5091>0.051.044
    起始电荷状态-0.0530.004161.1591< 0.010.949

    上一次出行链
    状态
    1.2120.20435.3091< 0.013.359
    天气0.3840.3101.5301>0.051.468
    常量-2.6510.42838.4381< 0.010.071
    下载: 导出CSV

    表  5  基于显著影响因素的模型分析结果

    Table  5.   Model analysis results based on significant influential factors

    因素变量BSEWalsdfPExp(B)
    旅程开始时间0.0300.0182.9191< 0.011.030
    出行链时长0069.0121< 0.011.000
    出行链距离0.0220.00438.2001< 0.051.023
    起始电荷状态-0.0520.004163.2191< 0.010.957

    上一次出行
    链状态
    1.1200.20131.0711< 0.013.065
    常量-1.7040.35722.8221< 0.010.182
    下载: 导出CSV

    表  6  充电行为预测模型验证结果

    Table  6.   Model verification results for charging behavior prediction

    有无充电
    出行链
    实际
    个数
    预测
    个数
    预测准确
    个数
    准确
    率/%
    预测
    误差/%
    充电出行链1261209276.6723.33
    无充电出行链28128725388.1511.85
    整体出行链40740734584.7715.23
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-09-28
  • 录用日期:  2019-04-21
  • 刊出日期:  2019-09-20

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