Building-related electric vehicle charging behaviors and energy consumption patterns: An urban-scale analysis

被引：0

作者：

Dai, Yi ^{[1
]}

Liu, Xiaochen ^{[1
]}

Li, Hao ^{[1
]}

Liu, Xiaohua ^{[1
]}

Zhang, Tao ^{[1
]}

Su, Zhihan ^{[1
]}

Zhao, Sheng ^{[2
]}

Zhou, Yicong ^{[2
]}

机构：

[1] Tsinghua Univ, Sch Architecture, Dept Bldg Sci, Beijing 100084, Peoples R China

[2] Wanbang Digital Energy Co Ltd, Big data Ctr, Cloud BD, Changzhou 213166, Peoples R China

来源：

TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT | 2025年 / 141卷

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Electric vehicle (EV); Charging infrastructure; Charging behavior; Charging power profile; Utilization rate; Big data analysis;

D O I：

10.1016/j.trd.2025.104663

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Understanding electric vehicle (EV) charging behavior and energy consumption patterns is fundamental to support the transportation electrification trend. We analyzed a dataset of 2,385,173 charging sessions from 2,719 chargers across 158 stations in Beijing, China. First, the charging sessions of alternating-current slow chargers (ACSCs) are mainly affected by parking behavior related to adjacent buildings; conversely, the charging sessions of direct-current fast chargers (DCFCs) cater to urgent charging demands. Second, adjacent building type and public accessibility significantly impact a station's charging power profile. Public station profiles can be quantified by combining private and independent station profiles. Third, the impact of charger rated power on utilization rate varies by station type. The charger utilization rate increases with its rated power at independent and commercial stations, but decreases at workplace and residential stations. This study reveals the inherent behavioral and energy-use relationship between EVs and buildings, providing guidance for charging infrastructure planning.

引用

页数：18