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Fully charged: An empirical study into the factors that influence connection times at EV-charging stations

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  • Wolbertus, Rick
  • Kroesen, Maarten
  • van den Hoed, Robert
  • Chorus, Caspar

Abstract

This study is the first to systematically and quantitatively explore the factors that determine, the length of charging sessions at public charging stations for electric vehicles in urban areas, with, particular emphasis placed on the combined parking- and charging-related determinants of connection, times. We use a unique and large data set – containing information concerning 2.6 million charging, sessions of 64,000 (i.e., 60% of) Dutch EV-users – in which both private users and taxi and car sharing, vehicles are included; thus representing a large variation in charging duration behaviour. Using, multinomial logistic regression techniques, we identify key factors explaining heterogeneity in charging, duration behaviour across charging stations. We show how these explanatory variables can be used to, predict EV-charging behaviour in urban areas and we derive preliminary implications for policy-makers, and planners who aim to optimize types and size of charging infrastructure.

Suggested Citation

  • Wolbertus, Rick & Kroesen, Maarten & van den Hoed, Robert & Chorus, Caspar, 2018. "Fully charged: An empirical study into the factors that influence connection times at EV-charging stations," Energy Policy, Elsevier, vol. 123(C), pages 1-7.
    • Handle: RePEc:eee:enepol:v:123:y:2018:i:c:p:1-7
    DOI: 10.1016/j.enpol.2018.08.030
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    Cited by:

    1. Wolff, Stefanie & Madlener, Reinhard, 2019. "Charged up? Preferences for Electric Vehicle Charging and Implications for Charging Infrastructure Planning," FCN Working Papers 3/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    2. Luis Oliveira & Arun Ulahannan & Matthew Knight & Stewart Birrell, 2020. "Wireless Charging of Electric Taxis: Understanding the Facilitators and Barriers to Its Introduction," Sustainability, MDPI, vol. 12(21), pages 1-21, October.
    3. Milan Straka & Rui Carvalho & Gijs van der Poel & v{L}ubov{s} Buzna, 2020. "Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data," Papers 2006.01672, arXiv.org, revised Jun 2020.
    4. Wolff, Stefanie & Madlener, Reinhard, 2020. "Willing to Pay? Spatial Heterogeneity of e-Vehicle Charging Preferences in Germany," FCN Working Papers 9/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    5. Einolander, Johannes & Lahdelma, Risto, 2022. "Explicit demand response potential in electric vehicle charging networks: Event-based simulation based on the multivariate copula procedure," Energy, Elsevier, vol. 256(C).
    6. Tu, Wei & Santi, Paolo & Zhao, Tianhong & He, Xiaoyi & Li, Qingquan & Dong, Lei & Wallington, Timothy J. & Ratti, Carlo, 2019. "Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing," Applied Energy, Elsevier, vol. 250(C), pages 147-160.
    7. Simolin, Toni & Rauma, Kalle & Viri, Riku & Mäkinen, Johanna & Rautiainen, Antti & Järventausta, Pertti, 2021. "Charging powers of the electric vehicle fleet: Evolution and implications at commercial charging sites," Applied Energy, Elsevier, vol. 303(C).
    8. Wang, Ning & Tian, Hangqi & Wu, Huahua & Liu, Qiaoqian & Luan, Jie & Li, Yuan, 2023. "Cost-oriented optimization of the location and capacity of charging stations for the electric Robotaxi fleet," Energy, Elsevier, vol. 263(PC).
    9. 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).
    10. Alexandre Lucas & Ricardo Barranco & Nazir Refa, 2019. "EV Idle Time Estimation on Charging Infrastructure, Comparing Supervised Machine Learning Regressions," Energies, MDPI, vol. 12(2), pages 1-17, January.
    11. Christopher Hecht & Jan Figgener & Xiaohui Li & Lei Zhang & Dirk Uwe Sauer, 2023. "Standard Load Profiles for Electric Vehicle Charging Stations in Germany Based on Representative, Empirical Data," Energies, MDPI, vol. 16(6), pages 1-21, March.
    12. 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.
    13. Lagomarsino, Maria & van der Kam, Mart & Parra, David & Hahnel, Ulf J.J., 2022. "Do I need to charge right now? Tailored choice architecture design can increase preferences for electric vehicle smart charging," Energy Policy, Elsevier, vol. 162(C).
    14. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    15. Budnitz, Hannah & Meelen, Toon & Schwanen, Tim, 2022. "Residential Neighbourhood Charging of Electric Vehicles: an exploration of user preferences," SocArXiv fsv7n, Center for Open Science.
    16. Philipp A. Friese & Wibke Michalk & Markus Fischer & Cornelius Hardt & Klaus Bogenberger, 2021. "Charging Point Usage in Germany—Automated Retrieval, Analysis, and Usage Types Explained," Sustainability, MDPI, vol. 13(23), pages 1-26, November.
    17. Steffen Limmer, 2019. "Dynamic Pricing for Electric Vehicle Charging—A Literature Review," Energies, MDPI, vol. 12(18), pages 1-24, September.
    18. Huang, Bing & Meijssen, Aart Gerard & Annema, Jan Anne & Lukszo, Zofia, 2021. "Are electric vehicle drivers willing to participate in vehicle-to-grid contracts? A context-dependent stated choice experiment," Energy Policy, Elsevier, vol. 156(C).
    19. Einolander, Johannes & Lahdelma, Risto, 2022. "Multivariate copula procedure for electric vehicle charging event simulation," Energy, Elsevier, vol. 238(PA).
    20. Wang, Shengyou & Zhuge, Chengxiang & Shao, Chunfu & Wang, Pinxi & Yang, Xiong & Wang, Shiqi, 2023. "Short-term electric vehicle charging demand prediction: A deep learning approach," Applied Energy, Elsevier, vol. 340(C).
    21. Helmus, Jurjen R. & Lees, Michael H. & van den Hoed, Robert, 2022. "A validated agent-based model for stress testing charging infrastructure utilization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 237-262.
    22. Wolbertus, Rick & van den Hoed, Robert & Kroesen, Maarten & Chorus, Caspar, 2021. "Charging infrastructure roll-out strategies for large scale introduction of electric vehicles in urban areas: An agent-based simulation study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 262-285.

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