Influential factors analysis of electric vehicle charging behavior based on trip chain
-
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。
Abstract:With the rapid development of electric vehicles, large-scale electric vehicle charging behavior will bring tremendous influence on the planning and operation of electric power systems. It is more and more urgent to study the charging behavior of electric vehicles and its influential factors, and predict the potential charging behavior in real time. Based on the historical data of private electric vehicles in Beijing, this paper introduces the concept of trip chain to comprehensively analyze the data of electric vehicle charging process and discharge process. This research considers the various potential influential factors on electric vehicles' charging behavior in the actual situation and determines the factors that significantly affect charging behavior through logistic regression analysis. Finally, the charging behavior forecasting model for electric vehicle is established based on the single and multiple significant influential factors. The results show that the model based on multiple significant influential factors has higher accuracy and better prediction effect in sunny days. This research will help optimize the charging behavior of electric vehicles, thus improving the charging efficiency of electric vehicles.
-
Key words:
- electric vehicle /
- charging behavior /
- trip chain /
- influential factors /
- forecasting model
-
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 0 1 2 3 出行链分布 2 378 410 167 47 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 2015-08-01 31 23 阴转小到中雨 2015-08-02 30 23 多云转阴 2015-08-03 30 23 阵雨转多云 2015-08-04 33 22 晴转多云 2015-08-05 32 22 多云转雷阵雨 2015-08-06 31 22 多云 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 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 B SE Wals df P Exp(B) 旅程开始时间 0.032 0.017 3.485 1 < 0.01 1.033 出行链时长 0 0 76.843 1 < 0.01 1.000 出行链距离 0.012 0.004 7.817 1 < 0.01 1.004 旅程速度 0.043 0.011 16.509 1 >0.05 1.044 起始电荷状态 -0.053 0.004 161.159 1 < 0.01 0.949
上一次出行链
状态1.212 0.204 35.309 1 < 0.01 3.359 天气 0.384 0.310 1.530 1 >0.05 1.468 常量 -2.651 0.428 38.438 1 < 0.01 0.071 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 B SE Wals df P Exp(B) 旅程开始时间 0.030 0.018 2.919 1 < 0.01 1.030 出行链时长 0 0 69.012 1 < 0.01 1.000 出行链距离 0.022 0.004 38.200 1 < 0.05 1.023 起始电荷状态 -0.052 0.004 163.219 1 < 0.01 0.957
上一次出行
链状态1.120 0.201 31.071 1 < 0.01 3.065 常量 -1.704 0.357 22.822 1 < 0.01 0.182 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 126 120 92 76.67 23.33 无充电出行链 281 287 253 88.15 11.85 整体出行链 407 407 345 84.77 15.23 -
[1] BOULANGER A G, CHU A C, MAXX S, et al.Vehicle electrification:Status and issues[J].Proceedings of the IEEE, 2011, 99(6):1116-1138. doi: 10.1109/JPROC.2011.2112750 [2] 张文亮, 武斌, 李武峰, 等.我国纯电动汽车的发展方向及能源供给模式的探讨[J].电网技术, 2009, 33(4):1-5. doi: 10.3969/j.issn.1674-0629.2009.04.001ZHANG W L, WU B, LI W F, et al.Discussion on development trend of battery electric vehicles in China and its energy supply mode[J].Power System Technology, 2009, 33(4):1-5(in Chinese). doi: 10.3969/j.issn.1674-0629.2009.04.001 [3] 高赐威, 张亮.电动汽车充电对电网影响的综述[J].电网技术, 2011, 35(2):127-131. http://d.old.wanfangdata.com.cn/Conference/7705189GAO C W, ZHANG L.A survey of influence of electric vehicle charging on power grid[J].Power System Technology, 2011, 35(2):127-131(in Chinese). http://d.old.wanfangdata.com.cn/Conference/7705189 [4] 胡泽春, 宋永华, 徐智威.电动汽车接入电网的影响与利用[J].中国电机工程学报, 2012, 32(4):1-10. http://d.old.wanfangdata.com.cn/Periodical/zgdjgcxb201204002HU Z C, SONG Y H, XU Z W.Impacts and utilization of electric vehicles integration into power systems[J].Proceedings of the CSEE, 2012, 32(4):1-10(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/zgdjgcxb201204002 [5] 张艳娟, 苏小林, 闫晓霞, 等.基于电动汽车时空特性的充电负荷预测[J].电力建设, 2015, 36(7):75-81. doi: 10.3969/j.issn.1000-7229.2015.07.010ZHANG Y J, SU X L, YAN X X, et al.A method of charging load forecast based on electric vehicle time-space characteristics[J].Electric Power Construction, 2015, 36(7):75-81(in Chinese). doi: 10.3969/j.issn.1000-7229.2015.07.010 [6] 陈丽丹, 聂涌泉, 钟庆.基于出行链的电动汽车充电负荷预测模型[J].电工技术学报, 2015, 30(4):216-225. doi: 10.3969/j.issn.1000-6753.2015.04.027CHEN L D, NIE Y Q, ZHONG Q.A model for electricvehicle charging load forecasting based on trip chains[J].Transactions of China Electrotechnical Society, 2015, 30(4):216-225(in Chinese). doi: 10.3969/j.issn.1000-6753.2015.04.027 [7] 王海玲, 张美霞, 杨秀.基于气温影响的电动汽车充电需求预测[J].电测与仪表, 2017, 54(23):123-128. doi: 10.3969/j.issn.1001-1390.2017.23.020WANG H L, ZHANG M X, YANG X.Electric vehicle charging demand forecasting based on influence of weather and temperature[J].Electrical Measurement and Instrumentation, 2017, 54(23):123-128(in Chinese). doi: 10.3969/j.issn.1001-1390.2017.23.020 [8] 田立亭, 史双龙, 贾卓.电动汽车充电功率需求的统计学建模方法[J].电网技术, 2010, 34(11):126-130. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dwjs201011023TIAN L T, SHI S L, JIA Z.A statistical model for charging power demand of electric vehicles[J].Power System Technology, 2010, 34(11):126-130(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dwjs201011023 [9] 罗卓伟, 胡泽春, 宋永华, 等.电动汽车充电负荷计算方法[J].电力系统自动化, 2011, 35(14):36-42. http://d.old.wanfangdata.com.cn/Periodical/dlxtzdh201114008LUO Z W, HU Z C, SONG Y H, et al.Study on plug-in electric vehicles charging load calculating[J].Automation of Electric Power Systems, 2011, 35(14):36-42(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/dlxtzdh201114008 [10] 杨冰, 王丽芳, 廖承林.大规模电动汽车充电需求及影响因素[J].电工技术学报, 2013, 28(2):22-27. doi: 10.3969/j.issn.1000-6753.2013.02.003YANG B, WANG L F, LIAO C L.Research on powercharging demand of large-scale electric vehicles and its impacting factors[J].Transactions of China Electrotechnical Society, 2013, 28(2):22-27(in Chinese). doi: 10.3969/j.issn.1000-6753.2013.02.003 [11] MOON H B, PARK S Y, JEONG C, et al.Forecasting electricity demand of electric vehicles by analyzing consumers' charging patterns[J].Transportation Research Part D:Transport and Environment, 2018, 62:64-79. doi: 10.1016/j.trd.2018.02.009 [12] ASHTARI A, BIBEAU E, SHAHIDINEJAD S, et al.PEV charging profile prediction and analysis based on vehicle usage data[J].IEEE Transactions on Smart Grid, 2012, 3(1):341-350. doi: 10.1109/TSG.2011.2162009 [13] STEEN D, CARLSON O, BERTLING L.Assessment of electric vehicle charging scenarios based on demographical data[J].IEEE Transactions on Smart Grid, 2012, 3(3):1457-1468. doi: 10.1109/TSG.2012.2195687 [14] ALIZADEH M, SCAGLIONE A, DAVIES J, et al.A scalable stochastic model for the electricity demand of electric and plug-in hybrid vehicles[J].IEEE Transactions on Smart Grid, 2014, 5(2):848-860. doi: 10.1109/TSG.2013.2275988 [15] WANG J, LIU K, YAMAMOTO T.Improving electricity consumption estimation for electric vehicles based on sparse GPS observations[J].Energies, 2017, 10(1):129. doi: 10.3390/en10010129 [16] SCHÄUBLE J, KASCHUB T, ENSSLEN A, et al.Generating electric vehicle load profiles from empirical data of three EV fleets in Southwest Germany[J].Journal of Cleaner Production, 2017, 150:253-266. doi: 10.1016/j.jclepro.2017.02.150 [17] YANG Y, YAO E, YANG Z, et al.Modeling the charging and route choice behavior of BEV drivers[J].Transportation Research Part C:Emerging Technologies, 2016, 65:190-204. doi: 10.1016/j.trc.2015.09.008 [18] FERNANDEZ L P, ROMÁN T G S, COSSENT R, et al.Assessment of the impact of plug-in electric vehicles on distribution networks[J].IEEE Transactions on Power Systems, 2011, 26(1):206-213. doi: 10.1109/TPWRS.2010.2049133 [19] HÜBNER M, ZHAO L, MIRBACH T, et al.Impact of large-scale electric vehicle application on the power supply[C]//2009 IEEE Electrical Power & Energy Conference (EPEC).Piscataway, NJ: IEEE Press, 2009: 1-6. [20] IKEGAMI T, OGIMOTO K, YANO H, et al.Balancing power supply-demand by controlled charging of numerous electric vehicles[C]//2012 IEEE International Electric Vehicle Conference.Piscataway, NJ: IEEE Press, 2012: 1-8. [21] BOWMAN J L, BEN-AKIVA M E.Activity-based disaggregate travel demand model system with activity schedules[J].Transportation Research Part A:Policy and Practice, 2001, 35(1):1-28. doi: 10.1016/S0965-8564(99)00043-9 [22] 温剑锋, 陶顺, 肖湘宁, 等.基于出行链随机模拟的电动汽车充电需求分析[J].电网技术, 2015, 39(6):1477-1484. http://d.old.wanfangdata.com.cn/Periodical/dwjs201506002WEN J F, TAO S, XIAO X N, et al.Analysis on charging demand of EV based on stochastic simulation of trip chain[J].Power System Technology, 2015, 39(6):1477-1484(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/dwjs201506002