TY - GEN
T1 - Analysis of Electric Vehicle Charging Behavior Based on Gaussian Mixture Model Clustering
AU - Peng, Peng
AU - Zhang, Zhaosheng
AU - Li, Jinli
AU - Gao, Wei
AU - Xie, Yi
N1 - Publisher Copyright:
© Beijing Paike Culture Commu. Co., Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Using large-scale electric vehicle charging behavior data in Beijing, this study reveals the charging behavior characteristics of electric vehicle users and their individualized needs through analysis and modeling. First, the data were effectively processed and feature extracted, and a database containing many charging records was established. Then, the Gaussian mixture model algorithm was applied for clustering analysis, and four charging behavior patterns were successfully identified: short-duration low-power charging mode, balanced charging mode, long-duration high-power charging mode, and high-efficiency fast charging mode. Finally, the personalized characteristics of individual users are found by profiling their charging behaviors.
AB - Using large-scale electric vehicle charging behavior data in Beijing, this study reveals the charging behavior characteristics of electric vehicle users and their individualized needs through analysis and modeling. First, the data were effectively processed and feature extracted, and a database containing many charging records was established. Then, the Gaussian mixture model algorithm was applied for clustering analysis, and four charging behavior patterns were successfully identified: short-duration low-power charging mode, balanced charging mode, long-duration high-power charging mode, and high-efficiency fast charging mode. Finally, the personalized characteristics of individual users are found by profiling their charging behaviors.
KW - Charging Behavior Pattern Analysis Model
KW - Electric Vehicle
KW - Gaussian Mixture Model Clustering
KW - User Charging Behavior
UR - http://www.scopus.com/pages/publications/85216006032
U2 - 10.1007/978-981-96-0897-3_37
DO - 10.1007/978-981-96-0897-3_37
M3 - Conference contribution
AN - SCOPUS:85216006032
SN - 9789819608966
T3 - Lecture Notes in Electrical Engineering
SP - 383
EP - 390
BT - The Proceedings of the 19th Annual Conference of China Electrotechnical Society - Annual Conference of China Electrotechnical Society, ACCES 2024
A2 - Yang, Qingxin
A2 - Bie, Zhaohong
A2 - Yang, Xu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th Annual Conference of China Electrotechnical Society, ACCES 2024
Y2 - 20 September 2024 through 22 September 2024
ER -