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Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet

机译：利用出租车的大数据告知出行方式在北京选址公共电动汽车充电站

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摘要

Charging infrastructure is critical to the development of electric vehicle (EV) system. While many countries have implemented great policy efforts to promote EVs, how to build charging infrastructure to maximize overall travel electrification given how people travel has not been well studied. Mismatch of demand and infrastructure can lead to under-utilized charging stations, wasting public resources. Estimating charging demand has been challenging due to lack of realistic vehicle travel data. Public charging is different from refueling from two aspects: required time and home-charging possibility. As a result, traditional approaches for refueling demand estimation (e.g. traffic flow and vehicle ownership density) do not necessarily represent public charging demand. This research uses large-scale trajectory data of 11,880 taxis in Beijing as a case study to evaluate how travel patterns mined from big-data can inform public charging infrastructure development. Although this study assumes charging stations to be dedicated to a fleet of PHEV taxis which may not fully represent the real-world situation, the methodological framework can be used to analyze private vehicle trajectory data as well to improve our understanding of charging demand for electrified private fleet. Our results show that (1) collective vehicle parking "hotspots" are good indicators for charging demand; (2) charging stations sited using travel patterns can improve electrification rate and reduce gasoline consumption; (3) with current grid mix, emissions of CO_2, PM, SO_2, and NO_x will increase with taxi electrification; and (4) power demand for public taxi charging has peak load around noon, overlapping with Beijing's summer peak power.
机译：充电基础设施对于电动汽车（EV）系统的发展至关重要。尽管许多国家已经采取了巨大的政策措施来推广电动汽车，但是鉴于人们的出行方式，如何建立充电基础设施以最大化整体出行电气化水平尚未得到充分研究。需求和基础设施不匹配会导致充电站利用率不足，浪费公共资源。由于缺乏实际的车辆行驶数据，估计充电需求一直具有挑战性。公共充电与加油从两个方面不同：所需时间和上门充电的可能性。结果，用于加油需求估计的传统方法（例如，交通流量和车辆拥有密度）不一定代表公共收费需求。本研究以北京11,880辆出租车的大规模轨迹数据为例，评估了从大数据中提取的出行方式如何为公共收费基础设施的发展提供信息。尽管本研究假设充电站专用于可能无法完全反映现实情况的PHEV出租车车队，但该方法框架可用于分析私家车的轨迹数据以及增进我们对电动私家车充电需求的了解舰队。我们的结果表明：（1）集体停车的“热点”是收费需求的良好指标； （2）采用出行方式选址的充电站可以提高电气化率，减少汽油消耗； （3）在当前的电网结构下，出租车滑行电气化会增加CO_2，PM，SO_2和NO_x的排放； （4）公共出租车充电的用电需求在中午左右达到高峰，与北京夏季的高峰用电重叠。

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  • 来源
    《Transportation Research》 |2014年第12期|39-46|共8页
  • 作者

    Hua Cai; Xiaoping Jia; Anthony S.F. Chiu; Xiaojun Hu; Ming Xu;

  • 作者单位

    School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI 48109-1041, United States,Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109-2125, United States;

    School of Environmental and Safety Engineering, Qingdao University of Science & Technology, Qingdao, China;

    Department of Industrial Engineering, De La Salle University, Manila Philippines;

    Energy Research Institute, Shanghai Jiao Tong University, Shanghai, China;

    School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI 48109-1041, United States,Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109-2125, United States, 440 Church Street, Ann Arbor, MI 48109-1041, United States;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Electric vehicle; Charging stations; Travel pattern; GPS data; Transportation emission; Grid impact;

    机译：电动汽车;充电站;出行方式;GPS数据;运输排放;网格影响;

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