IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i5p1807-d1595962.html
   My bibliography  Save this article

EV Charging Behavior Analysis and Load Prediction via Order Data of Charging Stations

Author

Listed:
  • Shiqian Wang

    (State Grid Henan Electric Power Company Economic and Technology Research Institute, Zhengzhou 450052, China)

  • Bo Liu

    (State Grid Henan Electric Power Company, Zhengzhou 450052, China)

  • Qiuyan Li

    (State Grid Henan Electric Power Company Economic and Technology Research Institute, Zhengzhou 450052, China)

  • Ding Han

    (State Grid Henan Electric Power Company Economic and Technology Research Institute, Zhengzhou 450052, China)

  • Jianshu Zhou

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Yue Xiang

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

Abstract

To understand the charging behavior of electric vehicle (EV) users and the sustainable use of the flexibility resources of EV, EV charging behavior analysis and load prediction via order data of charging stations was proposed. The user probability distribution model is established from the characteristic dimensions of EV charging initial time, initial state of charge, power level, and charging time. Under the conditions of specific districts, seasons, multiple EV types, and specific weather, the Monte Carlo simulation method is used to predict the EV load distribution at the physical level. The correlation between users’ willingness to charge and the electricity price is analyzed, and the logistic function is used to establish the charging load prediction model on the economic level. Taking a city in Henan Province, China, as an example, the calculation results show that the EV charging load distribution varies with the district, season, weather, and EV type, and the 24 h time-of-use (TOU) electricity price and EV quantity distribution are analyzed. The proposed method can better reflect EV charging behavior and accurately predict EV charging load.

Suggested Citation

  • Shiqian Wang & Bo Liu & Qiuyan Li & Ding Han & Jianshu Zhou & Yue Xiang, 2025. "EV Charging Behavior Analysis and Load Prediction via Order Data of Charging Stations," Sustainability, MDPI, vol. 17(5), pages 1-16, February.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:1807-:d:1595962
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/5/1807/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/5/1807/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Zhao, Yiwen & Zhan, Weipeng, 2023. "Stacking regression technology with event profile for electric vehicle fast charging behavior prediction," Applied Energy, Elsevier, vol. 336(C).
    2. Kumar, Navin & Sood, Sandeep Kumar & Saini, Munish, 2024. "Internet of Vehicles (IoV) Based Framework for electricity Demand Forecasting in V2G," Energy, Elsevier, vol. 297(C).
    3. Yin, Wanjun & Ji, Jianbo & Wen, Tao & Zhang, Chao, 2023. "Study on orderly charging strategy of EV with load forecasting," Energy, Elsevier, vol. 278(C).
    4. Xiang, Yue & Liu, Junyong & Li, Ran & Li, Furong & Gu, Chenghong & Tang, Shuoya, 2016. "Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates," Applied Energy, Elsevier, vol. 178(C), pages 647-659.
    5. Xiang, Yue & Jiang, Zhuozhen & Gu, Chenghong & Teng, Fei & Wei, Xiangyu & Wang, Yang, 2019. "Electric vehicle charging in smart grid: A spatial-temporal simulation method," Energy, Elsevier, vol. 189(C).
    6. Zhang, Kaizhe & Xu, Yinliang & Sun, Hongbin, 2024. "Bilevel optimal coordination of active distribution network and charging stations considering EV drivers' willingness," Applied Energy, Elsevier, vol. 360(C).
    7. Yin, Wanjun & Ji, Jianbo, 2024. "Research on EV charging load forecasting and orderly charging scheduling based on model fusion," Energy, Elsevier, vol. 290(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
    2. Muyang Liu & Yinjun Xiong & Quan Li & Mohammed Ahsan Adib Murad & Weilin Zhong, 2025. "Higher-Order Markov Chain-Based Probabilistic Power Flow Calculation Method Considering Spatio-Temporal Correlations," Energies, MDPI, vol. 18(5), pages 1-15, February.
    3. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    4. Viktor Slednev & Patrick Jochem & Wolf Fichtner, 2022. "Impacts of electric vehicles on the European high and extra high voltage power grid," Journal of Industrial Ecology, Yale University, vol. 26(3), pages 824-837, June.
    5. Zhou, Guangyou & Zhu, Zhiwei & Luo, Sumei, 2022. "Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm," Energy, Elsevier, vol. 247(C).
    6. Müller, Mathias & Blume, Yannic & Reinhard, Janis, 2022. "Impact of behind-the-meter optimised bidirectional electric vehicles on the distribution grid load," Energy, Elsevier, vol. 255(C).
    7. Li, Bin & Dong, Xujun & Wen, Jianghui, 2022. "Cooperative-driving control for mixed fleets at wireless charging sections for lane changing behaviour," Energy, Elsevier, vol. 243(C).
    8. Sanchari Deb & Kari Tammi & Karuna Kalita & Pinakeswar Mahanta, 2018. "Review of recent trends in charging infrastructure planning for electric vehicles," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 7(6), November.
    9. Poyrazoglu, Gokturk & Coban, Elvin, 2021. "A stochastic value estimation tool for electric vehicle charging points," Energy, Elsevier, vol. 227(C).
    10. Wang, Yue & Shi, Jianmai & Wang, Rui & Liu, Zhong & Wang, Ling, 2018. "Siting and sizing of fast charging stations in highway network with budget constraint," Applied Energy, Elsevier, vol. 228(C), pages 1255-1271.
    11. Zhao, Zhonghao & Lee, Carman K.M. & Huo, Jiage, 2023. "EV charging station deployment on coupled transportation and power distribution networks via reinforcement learning," Energy, Elsevier, vol. 267(C).
    12. Tian, Chenlu & Liu, Yechun & Zhang, Guiqing & Yang, Yalong & Yan, Yi & Li, Chengdong, 2024. "Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles," Energy, Elsevier, vol. 300(C).
    13. Xiang, Yue & Jiang, Zhuozhen & Gu, Chenghong & Teng, Fei & Wei, Xiangyu & Wang, Yang, 2019. "Electric vehicle charging in smart grid: A spatial-temporal simulation method," Energy, Elsevier, vol. 189(C).
    14. Ferro, G. & Minciardi, R. & Robba, M., 2020. "A user equilibrium model for electric vehicles: Joint traffic and energy demand assignment," Energy, Elsevier, vol. 198(C).
    15. Jianxin Qin & Jing Qiu & Yating Chen & Tao Wu & Longgang Xiang, 2022. "Charging Stations Selection Using a Graph Convolutional Network from Geographic Grid," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    16. Yue Wang & Zhong Liu & Jianmai Shi & Guohua Wu & Rui Wang, 2018. "Joint Optimal Policy for Subsidy on Electric Vehicles and Infrastructure Construction in Highway Network," Energies, MDPI, vol. 11(9), pages 1-21, September.
    17. Cui, Li & Wang, Qingyuan & Qu, Hongquan & Wang, Mingshen & Wu, Yile & Ge, Le, 2023. "Dynamic pricing for fast charging stations with deep reinforcement learning," Applied Energy, Elsevier, vol. 346(C).
    18. Zhou, Ze & Liu, Zhitao & Su, Hongye & Zhang, Liyan, 2022. "Integrated pricing strategy for coordinating load levels in coupled power and transportation networks," Applied Energy, Elsevier, vol. 307(C).
    19. Davidov, Sreten & Pantoš, Miloš, 2017. "Impact of stochastic driving range on the optimal charging infrastructure expansion planning," Energy, Elsevier, vol. 141(C), pages 603-612.
    20. Jefferson Morán & Esteban Inga, 2022. "Characterization of Load Centers for Electric Vehicles Based on Simulation of Urban Vehicular Traffic Using Geo-Referenced Environments," Sustainability, MDPI, vol. 14(6), pages 1-20, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:1807-:d:1595962. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.