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Analysis of Charging Behavior of Electric Vehicles Based on the Ordered Logistic Regression Model
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Ying LI1, Xue-rui HOU2, Chen-hui LIU2, 3, *
Science Technology and Engineering | 2025, 25(8) : 3473 - 3479
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Science Technology and Engineering | 2025, 25(8): 3473-3479
• Traffics and Transportations •
Analysis of Charging Behavior of Electric Vehicles Based on the Ordered Logistic Regression Model
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Ying LI1, Xue-rui HOU2, Chen-hui LIU2, 3, *
Affiliations
  • 1 School of Information Engineering Chang'an University Xi'an 710064 China
  • 2 School of Civil Engineering Hunan University Changsha 410082 China
  • 3 Transportation Research Center Hunan University Changsha 410082 China
Published: 2025-03-18 doi: 10.12404/j.issn.1671-1815.2403315
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Charging infrastructure is essential for promoting the development of electric vehicles, and identifying charging behaviors of electric vehicles is the precondition of optimizing the layout of charging infrastructure. Using the charging data of the Kechuang Base Charging Station in Beijing in 2017, charging behaviors of electric vehicles were explored by descriptive analysis and statistical analysis. Based on the charging power, charging piles were divided into three categories: high(100kW), medium(40kW), and low (${10}\mathrm{\;{kW}}$and${15}\mathrm{\;{kW}}$). Firstly, a descriptive analysis was conducted. It is found that as the charging power decreases, the charging time significantly increases, but usually does not exceed 180 min. 86.5% of customers are company users, mainly consisted of taxi/ ride hailing drivers; electric vehicles are often charged when their state of charge (SOC) are still high. Then, an ordered Logistic model was built to identify the key factors influencing the charging pile choice. Company users, daytime, weekday, charging peak period, and the low starting SOC are found to be able to significantly lead users to adopt the high-power charging piles. The research findings could be used to help optimizing the charging station layout.

electric vehicle  /  charging behavior  /  ordered Logistic regression model  /  real charging data  /  charging pile power
Ying LI, Xue-rui HOU, Chen-hui LIU. Analysis of Charging Behavior of Electric Vehicles Based on the Ordered Logistic Regression Model[J]. Science Technology and Engineering, 2025 , 25 (8) : 3473 -3479 . DOI: 10.12404/j.issn.1671-1815.2403315
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Article Info
doi: 10.12404/j.issn.1671-1815.2403315
  • Receive Date：2024-05-06
  • Online Date：2025-07-29
  • Published：2025-03-18
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  • Received：2024-05-06
  • Revised：2024-12-26
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Affiliations
    1 School of Information Engineering Chang'an University Xi'an 710064 China
    2 School of Civil Engineering Hunan University Changsha 410082 China
    3 Transportation Research Center Hunan University Changsha 410082 China
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