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Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles

Author

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  • Ye Yang

    (School of Economic and Management, North China Electric Power University, Beijing 102206, China)

  • Zhongfu Tan

    (School of Economic and Management, North China Electric Power University, Beijing 102206, China)

  • Yilong Ren

    (Hefei Innovation Research Institute, Beihang University, Beijing 100191, China)

Abstract

Due to the limited power cell performance of battery electric vehicles (BEVs), BEV drivers endure a short cruising range and a long charging time. Additionally, uneven charging facilities and unreasonable charging arrangements result in partial queuing and partial idling of charging stations. To solve these problems, it is critical to understand BEV charging behavior and its influential factors. Considering the urgency of BEV charging, BEV drivers tend to choose fast charging when BEV is in driving state. This study investigates fast charging behavior by utilizing private BEV connected data collected from Beijing. First, 130 private BEVs with travel rules were screened out. Using seven months of BEV data, a total of 15,752 trajectories were identified, among which 2161 have fast charging behavior. According to the relationship between fast charging behavior and some influential factors, including battery modeling, driving behavior, weather and environment, and even user habit, were empirically investigated. Moreover, the battery state of charge at the start time, time-origin, travel time duration, driving distance, driving speed, wind power, temperature, and last-fast-status are determined as significant influencing factors. Lastly, a prediction model based on the significant factors is proposed to estimate whether there is fast charging in a day trajectory. The proposed model achieves the best accuracy over compared models, i.e., univariate linear regression (ULR) with several factors and multivariate linear regression (MLR) model. The study is expected to help better understand fast charging behavior and further contribute to the future improvement of fast charging efficiency.

Suggested Citation

  • Ye Yang & Zhongfu Tan & Yilong Ren, 2020. "Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles," Sustainability, MDPI, vol. 12(8), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3439-:d:349309
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    References listed on IDEAS

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    Cited by:

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    3. Haber, Marc & Azaïs, Philippe & Genies, Sylvie & Raccurt, Olivier, 2023. "Stress factor identification and Risk Probabilistic Number (RPN) analysis of Li-ion batteries based on worldwide electric vehicle usage," Applied Energy, Elsevier, vol. 343(C).
    4. Isabel C. Gil-García & Mª Socorro García-Cascales & Habib Dagher & Angel Molina-García, 2021. "Electric Vehicle and Renewable Energy Sources: Motor Fusion in the Energy Transition from a Multi-Indicator Perspective," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    5. Hridoy Roy & Bimol Nath Roy & Md. Hasanuzzaman & Md. Shahinoor Islam & Ayman S. Abdel-Khalik & Mostaf S. Hamad & Shehab Ahmed, 2022. "Global Advancements and Current Challenges of Electric Vehicle Batteries and Their Prospects: A Comprehensive Review," Sustainability, MDPI, vol. 14(24), pages 1-30, December.
    6. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.
    7. Jie Xing & Peng Wu, 2021. "State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    8. Luyun Wang & Bo Zhou, 2023. "Optimal Planning of Electric Vehicle Fast-Charging Stations Considering Uncertain Charging Demands via Dantzig–Wolfe Decomposition," Sustainability, MDPI, vol. 15(8), pages 1-23, April.

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