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Modeling and Analysis of Electric Vehicle User Behavior Based on Full Data Chain Driven

Author

Listed:
  • Ruisheng Wang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Qiang Xing

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Zhong Chen

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Ziqi Zhang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Bo Liu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

The rapid development of electric vehicles (EVs) has posed challenges to power grids and transportation networks. Accurately capturing the usage patterns of EV users is a prerequisite for EVs’ interaction with electrified transportation networks. Thus, this paper proposes a full data chain (FDC) driven model to mine EVs’ comprehensive characteristics. By collecting the data of 150 private electric vehicles (PREVs), 100 commercial electric vehicles (CEVs), and 50 official electric vehicles (OEVs) in Chongqing, China, the driving characteristics are firstly mined by the adoption of origin-destination (OD) distribution and driving portrait. Moreover, the charging characteristics are extracted based on the state recognition for data chains. Then, vehicle usage characteristics of different types of users are comprehensively described based on the density-based spatial clustering of applications with noise (DBSCAN). Finally, the results of EV user characteristics are analyzed, and the effectiveness of the proposed model is verified by regional charging load analysis and urban road traffic flow comparison. The findings provide a data source and user behavior model for the planning, operation, and control of power grids and transportation networks.

Suggested Citation

  • Ruisheng Wang & Qiang Xing & Zhong Chen & Ziqi Zhang & Bo Liu, 2022. "Modeling and Analysis of Electric Vehicle User Behavior Based on Full Data Chain Driven," Sustainability, MDPI, vol. 14(14), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8600-:d:862352
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    References listed on IDEAS

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    Cited by:

    1. Claudia Violeta Pop & Daniel Fodorean & Dan-Cristian Popa, 2022. "Structural Analysis of an In-Wheel Motor with Integrated Magnetic Gear Designed for Automotive Applications," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    2. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.

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