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

^*Corresponding author for this work

School of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	The Proceedings of the 19th Annual Conference of China Electrotechnical Society - Annual Conference of China Electrotechnical Society, ACCES 2024
Editors	Qingxin Yang, Zhaohong Bie, Xu Yang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	383-390
Number of pages	8
ISBN (Print)	9789819608966
DOIs	https://doi.org/10.1007/978-981-96-0897-3_37
Publication status	Published - 2025
Event	19th Annual Conference of China Electrotechnical Society, ACCES 2024 - Xi'an, China Duration: 20 Sept 2024 → 22 Sept 2024

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	1308 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	19th Annual Conference of China Electrotechnical Society, ACCES 2024
Country/Territory	China
City	Xi'an
Period	20/09/24 → 22/09/24

Keywords

Charging Behavior Pattern Analysis Model
Electric Vehicle
Gaussian Mixture Model Clustering
User Charging Behavior

Access to Document

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

Cite this

Peng, P., Zhang, Z., Li, J., Gao, W., & Xie, Y. (2025). Analysis of Electric Vehicle Charging Behavior Based on Gaussian Mixture Model Clustering. In Q. Yang, Z. Bie, & X. Yang (Eds.), The Proceedings of the 19th Annual Conference of China Electrotechnical Society - Annual Conference of China Electrotechnical Society, ACCES 2024 (pp. 383-390). (Lecture Notes in Electrical Engineering; Vol. 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 et al. / 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. editor / Qingxin Yang ; Zhaohong Bie ; Xu Yang. Springer Science and Business Media Deutschland GmbH, 2025. pp. 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. in Q Yang, Z Bie & X Yang (eds), The Proceedings of the 19th Annual Conference of China Electrotechnical Society - Annual Conference of China Electrotechnical Society, ACCES 2024. Lecture Notes in Electrical Engineering, vol. 1308 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 383-390, 19th Annual Conference of China Electrotechnical Society, ACCES 2024, Xi'an, China, 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 et al.
The Proceedings of the 19th Annual Conference of China Electrotechnical Society - Annual Conference of China Electrotechnical Society, ACCES 2024. ed. / Qingxin Yang; Zhaohong Bie; Xu Yang. Springer Science and Business Media Deutschland GmbH, 2025. p. 383-390 (Lecture Notes in Electrical Engineering; Vol. 1308 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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/inward/record.url?scp=85216006032&partnerID=8YFLogxK

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. In Yang Q, Bie Z, Yang X, editors, 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. p. 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

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Keywords

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