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作 者:付玉雪 罗霞[1,2] 邱元森 张奕源 FU Yuxue;LUO Xia;QIU Yuansen;ZHANG Yiyuan(School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 611756,China;National Engineering Laboratory for Integrated Transportation Big Data Application Technology,Chengdu 611756,China)
机构地区:[1]西南交通大学交通运输与物流学院,成都611756 [2]综合交通大数据应用国家工程实验室,成都611756
出 处:《综合运输》2023年第10期29-36,48,共9页China Transportation Review
摘 要:精确刻画高速公路场景下电动汽车充电需求分布是优化城际充电设施布局的关键,本文从深入分析高速公路出行背景下电动汽车用户特征异质性出发,包括用户出行习惯、规律和充电决策机制,通过研究电池电量影响因素得到电动汽车能耗计算公式,提出一种充电需求时空分布预测方法,通过蒙特卡洛模拟进行仿真,以“成渝双城经济圈”高速公路通道为算例。结果表明:充电需求具有明显峰谷差、符合用户日常出行习惯,充电需求空分布不均匀且呈现“多峰”形式;不同温度及电动汽车渗透率对充电需求时空分布存在显著影响,论证了预测方法的有效性,能为城际充电设施时空布局建设提供技术支撑,为充电需求预测调控提供一定启示。The key to optimize the layout of intercity charging facilities is to accurately characterize the charging demand distribution of electric vehicles in the highway scenario.This paper proposes a method to predict the spatial-temporal distribution of charging demand and solved it by using Monte Carlo simulation method,starting from an in-depth analysis of the heterogeneity of electric vehicle user characteristics in the highway travel context,including user travel habits,patterns and charging decision mechanisms.The formula for calculating the energy consumption of electric vehicles is obtained by studying the factors influencing the battery power.This paper takes"Chengdu-Chongqing Economic Circle"expressway passage as an example.The results show that the charging demand has obvious peak-valley difference,which accords with the daily travel habits of users,and the empty distribution of charging demand is uneven and presents a"multi-peak"form.Different temperatures and permeability of electric vehicles have significant effects on the spatialtemporal distribution of charging demand.These conclusions demonstrate the effectiveness of the prediction method,provide technical support for the spatial-temporal layout of intercity charging facilities,and provide some enlightenment for the prediction and regulation of charging demand.
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