考虑行程数据的电动出租车充电需求预测及充电桩增量规划

Electric Taxi Charging Demand Forecasting and Charging Pile Incremental Planning Considering Trip Record Data

城市公共交通服务领域新能源车辆比例不断提高,电动出租车有望在全国快速推广。在这一背景下,以历史行程数据为依据,提出电动出租车充电需求预测方法及相应的充电桩增量规划方法。首先,分析电动出租车行为模式,基于马尔可夫模型描述车辆出行行为,基于模糊控制模型描述充电决策过程。然后,采用蒙特卡洛法模拟大量电动出租车行驶及充电行为,计算充电需求。最后,以城市各区域充电服务供求平衡为导向规划充电桩扩建规模。算例部分以华东某市为背景说明所提出方法的有效性,结果表明电动出租车日充电需求呈现显著峰谷差异、中心城区与周边城区呈现截然不同的充电需求时间分布特性、增量规划后各区域充电桩利用率平衡程度显著改善。

In China,the proportion of new energy vehicles in the field of urban public transport service keeps increasing,and electric taxis are expected to be rapidly promoted.Under this background,the electric taxi charging demand forecasting method and charging pile incremental planning method considering trip record data were proposed.Firstly,the behavior mode of electric taxi was analyzed,the vehicle travel behavior was described based on Markov model,and the charging decision-making process was described based on fuzzy control model.Then,Monte Carlo method was used to simulate the driving and charging behavior of massive electric taxis to calculate the charging demand.Finally,the expansion scale of charging pile was planned with the goal of balancing the supply and demand of charging services in various regions of the city.Taking a city in East China as an example,the effectiveness of the proposed method was verified.The results show that the daily charging demand of electric taxis presents significant peak valley differences,the central urban area and the surrounding urban areas show different charging demand time distribution characteristics,and the utilization balance of charging piles in each region is significantly improved after incremental planning.