Integration of charging behavior into infrastructure planning and management of electric vehicles: A systematic review and framework

被引:64
作者
Patil, Priyadarshan [1 ]
Kazemzadeh, Khashayar [2 ]
Bansal, Prateek [3 ,4 ]
机构
[1] Univ Texas Austin, Operat Res & Ind Engn, Austin, TX 78712 USA
[2] Chalmers Univ Technol, Space Earth & Environm, S-41296 Gothenburg, Sweden
[3] Natl Univ Singapore, Civil & Environm Engn, 1 Engn Dr 2, 07-03 E1A, Singapore 117576, Singapore
[4] Singapore ETH Ctr, Future Cities Lab Global Programme, Singapore Hub, CREATE campus,1 CREATE Way, 06-01 CREATE Tower, Singapore 138602, Singapore
基金
新加坡国家研究基金会;
关键词
Electric vehicles; Charging behavior; Charging infrastructure; Demand modeling; Charging stations; CONSUMER PREFERENCES; CHOICE; ADOPTION; DRIVERS; DEMAND; INCENTIVES; INTENTIONS; PROFILES; STATIONS; SERVICE;
D O I
10.1016/j.scs.2022.104265
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Increasing electric vehicle (EV) sales have shifted the focus of researchers from EV adoption to new operational challenges such as charging infrastructure deployment and management. These challenges require an accurate characterization of EV user charging behavior, especially with evolving battery technology. This study critically reviews approaches and data sources used to elicit EV charging behavior and patterns from a demand-side perspective and investigates how supply-side studies on charging infrastructure deployment and management incorporate charging behavior. We observe a noticeable disconnect between both strands of the literature, as supply-side studies still rely on simplistic assumptions about charging behavior and focus on a handful of aspects in isolation. More specifically, several studies either consider personal EVs or ride-hailing services with only public fast-charging infrastructure while ignoring available home/work charging infrastructure. We recommend shifting from this silo approach to a system-level dynamic planning framework where future charging demand is forecasted by combining charging behavior models with the models to forecast travel demand and EV adoption, followed by an integration of demand information into supply-side optimization. The framework can thus capture complex supply-demand interactions and inform the charging infrastructure planning policies, laying out a roadmap for emerging and mature EV markets.
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页数:18
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