Analysis of Electric Vehicle Charging Behavior Based on Gaussian Mixture Model Clustering

Peng Peng; Zhaosheng Zhang; Jinli Li; Wei Gao; Yi Xie

doi:10.1007/978-981-96-0897-3_37

Analysis of Electric Vehicle Charging Behavior Based on Gaussian Mixture Model Clustering

Peng Peng
, Zhaosheng Zhang^*
, Jinli Li
, Wei Gao
, Yi Xie

^*此作品的通讯作者

机械与车辆学院

Beijing Institute of Technology
State Grid Shanxi Electric Power Company

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	The Proceedings of the 19th Annual Conference of China Electrotechnical Society - Annual Conference of China Electrotechnical Society, ACCES 2024
编辑	Qingxin Yang, Zhaohong Bie, Xu Yang
出版商	Springer Science and Business Media Deutschland GmbH
页	383-390
页数	8
ISBN（印刷版）	9789819608966
DOI	https://doi.org/10.1007/978-981-96-0897-3_37
出版状态	已出版 - 2025
活动	19th Annual Conference of China Electrotechnical Society, ACCES 2024 - Xi'an, 中国期限: 20 9月 2024 → 22 9月 2024

出版系列

姓名	Lecture Notes in Electrical Engineering
卷	1308 LNEE
ISSN（印刷版）	1876-1100
ISSN（电子版）	1876-1119

会议

会议	19th Annual Conference of China Electrotechnical Society, ACCES 2024
国家/地区	中国
市	Xi'an
时期	20/09/24 → 22/09/24

访问文件

10.1007/978-981-96-0897-3_37

其它文件与链接

链接到 Scopus 的出版物

引用此

Peng, P., Zhang, Z., Li, J., Gao, W., & Xie, Y. (2025). Analysis of Electric Vehicle Charging Behavior Based on Gaussian Mixture Model Clustering. 在 Q. Yang, Z. Bie, & X. Yang (编辑), The Proceedings of the 19th Annual Conference of China Electrotechnical Society - Annual Conference of China Electrotechnical Society, ACCES 2024 (页码 383-390). (Lecture Notes in Electrical Engineering; 卷 1308 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-0897-3_37

Peng, Peng ; Zhang, Zhaosheng ; Li, Jinli 等. / Analysis of Electric Vehicle Charging Behavior Based on Gaussian Mixture Model Clustering. The Proceedings of the 19th Annual Conference of China Electrotechnical Society - Annual Conference of China Electrotechnical Society, ACCES 2024. 编辑 / Qingxin Yang ; Zhaohong Bie ; Xu Yang. Springer Science and Business Media Deutschland GmbH, 2025. 页码 383-390 (Lecture Notes in Electrical Engineering).

@inproceedings{035b4081515940cd9968409ac94b7a74,

title = "Analysis of Electric Vehicle Charging Behavior Based on Gaussian Mixture Model Clustering",

abstract = "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.",

keywords = "Charging Behavior Pattern Analysis Model, Electric Vehicle, Gaussian Mixture Model Clustering, User Charging Behavior",

author = "Peng Peng and Zhaosheng Zhang and Jinli Li and Wei Gao and Yi Xie",

note = "Publisher Copyright: {\textcopyright} Beijing Paike Culture Commu. Co., Ltd. 2025.; 19th Annual Conference of China Electrotechnical Society, ACCES 2024 ; Conference date: 20-09-2024 Through 22-09-2024",

year = "2025",

doi = "10.1007/978-981-96-0897-3\_37",

language = "English",

isbn = "9789819608966",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "383--390",

editor = "Qingxin Yang and Zhaohong Bie and Xu Yang",

booktitle = "The Proceedings of the 19th Annual Conference of China Electrotechnical Society - Annual Conference of China Electrotechnical Society, ACCES 2024",

address = "Germany",

}

Peng, P, Zhang, Z, Li, J, Gao, W & Xie, Y 2025, Analysis of Electric Vehicle Charging Behavior Based on Gaussian Mixture Model Clustering. 在 Q Yang, Z Bie & X Yang (编辑), The Proceedings of the 19th Annual Conference of China Electrotechnical Society - Annual Conference of China Electrotechnical Society, ACCES 2024. Lecture Notes in Electrical Engineering, 卷 1308 LNEE, Springer Science and Business Media Deutschland GmbH, 页码 383-390, 19th Annual Conference of China Electrotechnical Society, ACCES 2024, Xi'an, 中国, 20/09/24. https://doi.org/10.1007/978-981-96-0897-3_37

Analysis of Electric Vehicle Charging Behavior Based on Gaussian Mixture Model Clustering. / Peng, Peng; Zhang, Zhaosheng; Li, Jinli 等.
The Proceedings of the 19th Annual Conference of China Electrotechnical Society - Annual Conference of China Electrotechnical Society, ACCES 2024. 编辑 / Qingxin Yang; Zhaohong Bie; Xu Yang. Springer Science and Business Media Deutschland GmbH, 2025. 页码 383-390 (Lecture Notes in Electrical Engineering; 卷 1308 LNEE).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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 - https://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 -

Peng P, Zhang Z, Li J, Gao W, Xie Y. Analysis of Electric Vehicle Charging Behavior Based on Gaussian Mixture Model Clustering. 在 Yang Q, Bie Z, Yang X, 编辑, The Proceedings of the 19th Annual Conference of China Electrotechnical Society - Annual Conference of China Electrotechnical Society, ACCES 2024. Springer Science and Business Media Deutschland GmbH. 2025. 页码 383-390. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-96-0897-3_37

Analysis of Electric Vehicle Charging Behavior Based on Gaussian Mixture Model Clustering

摘要

出版系列

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