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Data mining of plug-in electric vehicles charging behavior using supply-side data

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  • Siddique, Choudhury
  • Afifah, Fatima
  • Guo, Zhaomiao
  • Zhou, Yan

Abstract

This paper aims to better understand the charging patterns of plug-in electric vehicles (PEVs) and identify factors that may significantly impact PEVs’ charging behavior. We collected 189,864 supply-side charging session data over 13 months from 821 charging stations in Illinois from ChargePoint. Through descriptive and regression analyses, we characterize the distributions of key charging behavior indicators, including charging location, dwell time, and battery start state of charge (SOC), and quantify the impacts of closely related factors on these charging behaviors. We find that: (1) PEVs are more likely to charge in the morning at multifamily commercial locations with a lower start SOC compared with single family residential locations; (2) Weekday and morning sessions are more likely to utilize workplace charging and have shorter dwell time compared with weekend and afternoon sessions; (3) Single family residential area and locations with Levels 1/2 chargers have a higher start SOC and longer dwell time compared with other locations and DC fast chargers (DCFCs). These findings provide policy insights to identify potential time and locations to incentivize PEVs for grid services, as well as identify critical location categories for further charging infrastructure investment to better reduce range anxiety and promote PEV adoption.

Suggested Citation

  • Siddique, Choudhury & Afifah, Fatima & Guo, Zhaomiao & Zhou, Yan, 2022. "Data mining of plug-in electric vehicles charging behavior using supply-side data," Energy Policy, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:enepol:v:161:y:2022:i:c:s0301421521005759
    DOI: 10.1016/j.enpol.2021.112710
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    References listed on IDEAS

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    1. Wang, Jianhui & Liu, Cong & Ton, Dan & Zhou, Yan & Kim, Jinho & Vyas, Anantray, 2011. "Impact of plug-in hybrid electric vehicles on power systems with demand response and wind power," Energy Policy, Elsevier, vol. 39(7), pages 4016-4021, July.
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    Cited by:

    1. Tikka, Ville & Haapaniemi, Jouni & Räisänen, Otto & Honkapuro, Samuli, 2022. "Convolutional neural networks in estimating the spatial distribution of electric vehicles to support electricity grid planning," Applied Energy, Elsevier, vol. 328(C).
    2. Kang, Zixuan & Ye, Zhongnan & Lam, Chor-Man & Hsu, Shu-Chien, 2023. "Sustainable electric vehicle charging coordination: Balancing CO2 emission reduction and peak power demand shaving," Applied Energy, Elsevier, vol. 349(C).
    3. Choi, Hyunhong & Lee, Jeongeun & Koo, Yoonmo, 2023. "Value of different electric vehicle charging facility types under different availability situations: A South Korean case study of electric vehicle and internal combustion engine vehicle owners," Energy Policy, Elsevier, vol. 174(C).

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