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Assessment of real-world driving patterns for electric vehicles: an on-board measurements study from Sweden

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  • Kobayashi, Yuki
  • Taljegard, Maria
  • Johnsson, Filip

Abstract

This study presents an analysis of the driving and charging patterns of passenger, battery-powered electric vehicles (EVs) in Sweden. The analysis is based on 1 year of GPS logging data acquired through the on-board diagnostics port for 334 randomly selected EVs in Sweden. Included are 55 EV models with battery capacities in the range of 16–100 kWh. The results show that 70 % of the electricity is charged at the home location, of which 86 % is charged during overnight parking events. The maximum share of the investigated EV fleet charging simultaneously is 13 % on average (at 00:10 h). For 56 % of the overnight parking events, the EVs arrive home with a state of charge (SOC) of 60 % or more. For the EVs that arrive at the home location with 60 % SOC, they are charged during 64 % and 34 % of the overnight charging events at home for the small (16–50 kWh)-battery and large (54–100 kWh)-battery EVs, respectively. The most-frequent parking duration is 14 h, which is about four-times longer than the time needed for charging and, thus, offers possibilities for flexible charging in time and vehicle-to-grid services. In summary, this study shows that there is a large potential for smart/flexible charging at home, since the EVs often arrive home with a relatively high SOC and are parked at home, between two trips, for a much longer time than is needed to recharge the battery.

Suggested Citation

  • Kobayashi, Yuki & Taljegard, Maria & Johnsson, Filip, 2025. "Assessment of real-world driving patterns for electric vehicles: an on-board measurements study from Sweden," Applied Energy, Elsevier, vol. 401(PA).
    • Handle: RePEc:eee:appene:v:401:y:2025:i:pa:s0306261925013388
    DOI: 10.1016/j.apenergy.2025.126608
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    References listed on IDEAS

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    1. Langbroek, Joram H.M. & Franklin, Joel P. & Susilo, Yusak O., 2017. "When do you charge your electric vehicle? A stated adaptation approach," Energy Policy, Elsevier, vol. 108(C), pages 565-573.
    2. Wiedemann, Nina & Xin, Yanan & Medici, Vasco & Nespoli, Lorenzo & Suel, Esra & Raubal, Martin, 2024. "Vehicle-to-grid for car sharing - A simulation study for 2030," Applied Energy, Elsevier, vol. 372(C).
    3. 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.
    4. Xu, Min & Meng, Qiang & Liu, Kai & Yamamoto, Toshiyuki, 2017. "Joint charging mode and location choice model for battery electric vehicle users," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 68-86.
    5. Calearo, Lisa & Marinelli, Mattia & Ziras, Charalampos, 2021. "A review of data sources for electric vehicle integration studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    6. Alexandra Märtz & Uwe Langenmayr & Sabrina Ried & Katrin Seddig & Patrick Jochem, 2022. "Charging Behavior of Electric Vehicles: Temporal Clustering Based on Real-World Data," Energies, MDPI, vol. 15(18), pages 1-26, September.
    7. Zhang, Xudong & Zou, Yuan & Fan, Jie & Guo, Hongwei, 2019. "Usage pattern analysis of Beijing private electric vehicles based on real-world data," Energy, Elsevier, vol. 167(C), pages 1074-1085.
    8. Mandev, Ahmet & Plötz, Patrick & Sprei, Frances & Tal, Gil, 2022. "Empirical charging behavior of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    Full references (including those not matched with items on IDEAS)

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