Improving the electrification rate of the vehicle miles traveled in Beijing: A data-driven approach
Improving the electrification rate of the vehicle miles traveled in Beijing: A data-driven approach
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DOI：
10.1016/j.tra.2017.01.005
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发表时间：
2017-03
影响因子：
6.4
通讯作者：
Meng Li;Yinghao Jia;Shen Zuojun;Fang He
中科院分区：
文献类型：
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作者：
Meng Li;Yinghao Jia;Shen Zuojun;Fang He
电动汽车（EV）作为可预见的未来车辆技术被推广，以减少对化石燃料的依赖和与传统车辆相关的温室气体排放。本文提出了一种数据驱动的方法，以提高电动化率的车辆行驶里程（VMT）的出租车车队在北京举行。具体而言，基于北京市46，765辆出租车的实时车辆轨迹数据，我们进行了时间序列模拟，以获得公共充电站部署计划的见解，包括公共充电站的位置，每个站点的充电器数量及其类型。该仿真模型从时间窗口、充电需求和充电器可用性等方面定义了电动汽车充电时机，并进一步考虑了车辆的异质出行模式。虽然这项研究只考察了特定城市的一种类型的车队，但该方法框架很容易适用于其他城市和具有类似数据集的车队类型，分析结果有助于我们了解电动汽车的充电行为。仿真结果表明：（i）在集中式充电时间窗内设置公共充电站，是提高机动车辆电动化率的上级策略;（ii）设置500个公共充电站（每台包括30个慢充），如果电动汽车电池续航里程为80公里，并提供家庭充电服务，两个月内可在北京实现1.7亿VMT的充电;（3）在公共充电站中适当地组合慢速充电器和快速充电器有助于提高电动化率;（4）将充电站分成较小的充电站并在空间上分布它们将提高VMT的电动化率;（v）向驾驶者提供充电站充电器可用性的信息可以提高VMT的电气化率;（vi）通过采用覆盖较长时间的数据集可以显著减轻轨迹数据中嵌入的随机性的影响。
Electric vehicles (EV) are promoted as a foreseeable future vehicle technology to reduce dependence on fossil fuels and greenhouse gas emissions associated with conventional vehicles. This paper proposes a data-driven approach to improving the electrification rate of the vehicle miles traveled (VMT) by taxi fleet in Beijing. Specifically, based on the gathered real-time vehicle trajectory data of 46,765 taxis in Beijing, we conduct time-series simulations to derive insights for the public charging station deployment plan, including the locations of public charging stations, the number of chargers at each station and their types. The proposed simulation model defines the electric vehicle charging opportunity from the aspects of time window, charging demand and charger availability, and further incorporates the heterogeneous travel patterns of individual vehicles. Although this study only examines one type of fleet in a specific city, the methodological framework is readily applicable to other cities and types of fleet with similar dataset available, and the analysis results contribute to our understanding on electric vehicle’s charging behavior. Simulation results indicate that: (i) locating public charging stations to the clustered charging time windows is a superior strategy to increase the electrification rate of VMT; (ii) deploying 500 public stations (each includes 30 slow chargers) can electrify 170 million VMT in Beijing in two months, if EV’s battery range is 80km and home charging is available; (iii) appropriately combining slow and fast chargers in public charging stations contributes to the electrification rate; (iv) breaking the charging stations into smaller ones and spatially distributing them will increase the electrification rate of VMT; (v) feeding the information of availability of chargers in charging stations to drivers can increase the electrification rate of VMT; (vi) the impact of stochasticity embedded in the trajectory data can be significantly mitigated by adopting the dataset covering a longer period.