摘要
充电基础设施是影响电动汽车发展的重要因素,而分析明确电动汽车充电行为特征是优化充电设施布局的前提。基于北京科创基地充电站2017年充电数据,利用描述性分析与统计分析研究了电动汽车充电行为特征。基于充电功率,将充电桩划分为高功率(100 kW)、中等功率(40 kW)、低功率(10 kW和15 kW)三类。首先,从充电时长、用户特征、电池特征等方面全面客观分析电动汽车的充电行为特征。发现随着充电桩功率减小,充电时长明显增加,但一般不超过180 min;从用户类型来看,以网约车/出租车等集团用户为主,占比高达86.5%;从电池特征看,大部分用户在电池荷电状态(state of charge,SOC)较高时就开始充电。其次,通过构建有序逻辑回归模型识别影响充电桩选择的关键因素。建模结果发现集团用户、白天、工作日、充电高峰时段、充电起始电池SOC很低时,电动汽车用户更倾向于选择高功率充电桩。研究成果可帮助优化充电站布局。
Charging infrastructure is essential for promoting the development of electric vehicles,and identifying charging behaviors of electric vehicles is the precondition of optimizing the layout of charging infrastructure.Using the charging data of the Kechuang Base Charging Station in Beijing in 2017,charging behaviors of electric vehicles were explored by descriptive analysis and statistical analysis.Based on the charging power,charging piles were divided into three categories:high(100 kW),medium(40 kW),and low(10 kW and 15 kW).Firstly,a descriptive analysis was conducted.It is found that as the charging power decreases,the charging time significantly increases,but usually does not exceed 180 min.86.5%of customers are company users,mainly consisted of taxi/ride hailing drivers;electric vehicles are often charged when their state of charge(SOC)are still high.Then,an ordered Logistic model was built to identify the key factors influencing the charging pile choice.Company users,daytime,weekday,charging peak period,and the low starting SOC are found to be able to significantly lead users to adopt the high-power charging piles.The research findings could be used to help optimizing the charging station layout.
作者
李颖
侯学睿
刘晨辉
LI Ying;HOU Xue-rui;LIU Chen-hui(School of Information Engineering,Chang an University,Xi an 710064,China;School of Civil Engineering,Hunan University,Changsha 410082,China;Transportation Research Center,Hunan University,Changsha 410082,China)
出处
《科学技术与工程》
北大核心
2025年第8期3473-3479,共7页
Science Technology and Engineering
基金
国家自然科学基金青年科学基金(52002031)。
关键词
电动汽车
充电行为
有序逻辑回归模型
实际充电数据
充电桩功率
electric vehicle
charging behavior
ordered Logistic regression model
real charging data
charging pile power
