气候变化领域集成服务门户(CCPortal): Data mining of plug-in electric vehicles charging behavior using supply-side data

CCPortal

DOI	10.1016/j.enpol.2021.112710
	Data mining of plug-in electric vehicles charging behavior using supply-side data
	Siddique C.; Afifah F.; Guo Z.; Zhou Y.
发表日期	2022
ISSN	0301-4215
卷号	161
英文摘要	This paper aims to better understand the charging patterns of plug-in electric vehicles (PEVs) and identify factors that may significantly impact PEVs’ charging behavior. We collected 189,864 supply-side charging session data over 13 months from 821 charging stations in Illinois from ChargePoint. Through descriptive and regression analyses, we characterize the distributions of key charging behavior indicators, including charging location, dwell time, and battery start state of charge (SOC), and quantify the impacts of closely related factors on these charging behaviors. We find that: (1) PEVs are more likely to charge in the morning at multifamily commercial locations with a lower start SOC compared with single family residential locations; (2) Weekday and morning sessions are more likely to utilize workplace charging and have shorter dwell time compared with weekend and afternoon sessions; (3) Single family residential area and locations with Levels 1/2 chargers have a higher start SOC and longer dwell time compared with other locations and DC fast chargers (DCFCs). These findings provide policy insights to identify potential time and locations to incentivize PEVs for grid services, as well as identify critical location categories for further charging infrastructure investment to better reduce range anxiety and promote PEV adoption. © 2021 Elsevier Ltd
英文关键词	Charging behaviors; Electric vehicle; Regression analysis; Supply-side data
语种	英语
scopus关键词	Charging (batteries); Data mining; Housing; Investments; Location; Plug-in electric vehicles; Charging behavior; Charging patterns; Charging station; Dwell time; Illinois; Plug-in electric vehicle charging; Residential locations; States of charges; Supply sides; Supply-side data; Regression analysis; data mining; electric vehicle; regression analysis; residential location; workplace; Illinois; United States
来源期刊	Energy Policy
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/256394
作者单位	Energy Systems Division, Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL 60439, United States; Department of Civil, Environmental and Construction Engineering Resilient, Intelligent and Sustainable Energy Systems Cluster University of Central FloridaFL 32766, United States
推荐引用方式 GB/T 7714	Siddique C.,Afifah F.,Guo Z.,et al. Data mining of plug-in electric vehicles charging behavior using supply-side data[J],2022,161.
APA	Siddique C.,Afifah F.,Guo Z.,&Zhou Y..(2022).Data mining of plug-in electric vehicles charging behavior using supply-side data.Energy Policy,161.
MLA	Siddique C.,et al."Data mining of plug-in electric vehicles charging behavior using supply-side data".Energy Policy 161(2022).