CLUSTER ANALYSIS OF VEHICLE DRIVING CONDITIONS BASED ON K-MEANS ALGORITHM AND EM ALGORITHM

Qi Yan; Yue Ma; Yi Li

doi:10.1049/icp.2021.0381

CLUSTER ANALYSIS OF VEHICLE DRIVING CONDITIONS BASED ON K-MEANS ALGORITHM AND EM ALGORITHM

Qi Yan^*
, Yue Ma
, Yi Li

^*此作品的通讯作者

机械与车辆学院

Beijing Institute of Technology

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

The simulation and evaluation of land vehicle performance requires accurate representation of driving conditions. The ability to effectively interpret this condition to estimate the power demand is important for the energy management and plays crucial role in the battery utilization. After collecting driving status of vehicle, two algorithms (K-means Clustering and EM Algorithm) are employed for clustering the working condition blocks. Finally, compare the clustering effects of the two algorithms, and draw the conclusion that EM Algorithm is better.

源语言	英语
主期刊名	IET Conference Proceedings
出版商	Institution of Engineering and Technology
页	1281-1285
页数	5
卷	2020
版本	3
ISBN（电子版）	9781839534195
DOI	https://doi.org/10.1049/icp.2021.0381
出版状态	已出版 - 2020
活动	2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020 - Virtual, Online 期限: 18 9月 2020 → 21 9月 2020

会议

会议	2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020
市	Virtual, Online
时期	18/09/20 → 21/09/20

访问文件

10.1049/icp.2021.0381

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链接到 Scopus 的出版物

引用此

@inproceedings{ace520b831b24b48a7fd5e08c646adc0,

title = "CLUSTER ANALYSIS OF VEHICLE DRIVING CONDITIONS BASED ON K-MEANS ALGORITHM AND EM ALGORITHM",

abstract = "The simulation and evaluation of land vehicle performance requires accurate representation of driving conditions. The ability to effectively interpret this condition to estimate the power demand is important for the energy management and plays crucial role in the battery utilization. After collecting driving status of vehicle, two algorithms (K-means Clustering and EM Algorithm) are employed for clustering the working condition blocks. Finally, compare the clustering effects of the two algorithms, and draw the conclusion that EM Algorithm is better.",

keywords = "CLUSTER ANALYSIS, DRIVING PATTERN, EM ALGORITHM, K-MEANS ALGORITHM",

author = "Qi Yan and Yue Ma and Yi Li",

note = "Publisher Copyright: {\textcopyright} 2020 The Institution of Engineering and Technology.; 2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020 ; Conference date: 18-09-2020 Through 21-09-2020",

year = "2020",

doi = "10.1049/icp.2021.0381",

language = "English",

volume = "2020",

pages = "1281--1285",

booktitle = "IET Conference Proceedings",

publisher = "Institution of Engineering and Technology",

address = "United Kingdom",

edition = "3",

}

Yan, Q, Ma, Y & Li, Y 2020, CLUSTER ANALYSIS OF VEHICLE DRIVING CONDITIONS BASED ON K-MEANS ALGORITHM AND EM ALGORITHM. 在 IET Conference Proceedings. 3 编辑, 卷 2020, Institution of Engineering and Technology, 页码 1281-1285, 2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020, Virtual, Online, 18/09/20. https://doi.org/10.1049/icp.2021.0381

TY - GEN

T1 - CLUSTER ANALYSIS OF VEHICLE DRIVING CONDITIONS BASED ON K-MEANS ALGORITHM AND EM ALGORITHM

AU - Yan, Qi

AU - Ma, Yue

AU - Li, Yi

PY - 2020

Y1 - 2020

N2 - The simulation and evaluation of land vehicle performance requires accurate representation of driving conditions. The ability to effectively interpret this condition to estimate the power demand is important for the energy management and plays crucial role in the battery utilization. After collecting driving status of vehicle, two algorithms (K-means Clustering and EM Algorithm) are employed for clustering the working condition blocks. Finally, compare the clustering effects of the two algorithms, and draw the conclusion that EM Algorithm is better.

AB - The simulation and evaluation of land vehicle performance requires accurate representation of driving conditions. The ability to effectively interpret this condition to estimate the power demand is important for the energy management and plays crucial role in the battery utilization. After collecting driving status of vehicle, two algorithms (K-means Clustering and EM Algorithm) are employed for clustering the working condition blocks. Finally, compare the clustering effects of the two algorithms, and draw the conclusion that EM Algorithm is better.

KW - CLUSTER ANALYSIS

KW - DRIVING PATTERN

KW - EM ALGORITHM

KW - K-MEANS ALGORITHM

UR - https://www.scopus.com/pages/publications/85174648216

U2 - 10.1049/icp.2021.0381

DO - 10.1049/icp.2021.0381

M3 - Conference contribution

AN - SCOPUS:85174648216

VL - 2020

SP - 1281

EP - 1285

BT - IET Conference Proceedings

PB - Institution of Engineering and Technology

T2 - 2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020

Y2 - 18 September 2020 through 21 September 2020

ER -

CLUSTER ANALYSIS OF VEHICLE DRIVING CONDITIONS BASED ON K-MEANS ALGORITHM AND EM ALGORITHM

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