Overview
Date:
Starts 4 June 2026, 10:00Ends 4 June 2026, 12:30Location:
Lecture hall KA (Campus Johanneberg)Opponent:
Dr. Lars-Henrik Björnsson, Researcher at Research Institutes of Sweden (RISE), SwedenThesis
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Electrification of passenger vehicles is an important measure to decarbonize the transport sector. An efficient introduction of electric vehicles (EVs) requires an understanding of how the charging of EVs impacts the electricity system and if, and to what extent, smart charging strategies, including vehicle to grid (V2G) services, can support the electric grid in the future energy systems. The aim of this thesis is to characterize the flexibility of smart charging including V2G by analyzing the real‑world driving, parking, and charging patterns obtained from logged EVs. The analysis is based on data collected from 394 privately owned EVs and survey responses from their owners. The results reveal substantial flexibility potential for smart charging from several perspectives. However, the findings also highlight important limitations that must be carefully considered when estimating flexibility or implementing flexible charging into energy system models.
Using the lower state of charge (SOC) threshold for charging decisions and the SOC when charging ends, the flexible battery capacity range is calculated to be 59% on average. The aggregated SOC for all logged EVs is within 60%–80% throughout the entire logging period. The results show that charging is needed in fewer than half of the days in a week for more than 73% of weeks, regardless of the attributes of the EV owners, including commuter category and battery capacity. Furthermore, EVs are charged more frequently than the minimum number of charging events required per week. Thus, there is potential for charging in a way that is flexible in time depending on, for example, grid congestion or spot prices. This is particularly the case for non‑commuters with large‑battery EVs. The amount of time that EVs are plugged in for smart charging differs by more than a factor of two if one assumes that EVs are plugged in whenever they are parked at home and that EVs are plugged in only when they charge during the parking event. Thus, careful consideration of which of these assumptions is appropriate is essential when estimating the availability of EVs for smart charging, as the choice can significantly affect the outcomes. Installation of chargers at workplaces can increase the number of grid-connected EVs at places other than the home location, although very few EVs need to charge at workplace to fulfill their driving demand. Incentives to promote plug‑in behavior at the home location can, therefore, be prove to be cost-effective at increasing the number of grid-connected EVs. This flexibility at home is exploited by EV owners with hourly electricity contracts through selecting charging times when the spot price is lower than the daily average. In this thesis, the logged EVs are clustered into three, five, and eleven clusters, resulting in groups with distinct characteristics. When the number of clusters is increased from three to five, a cluster with a low probability of parking at home during the night and a cluster maintaining a high SOC are added to the three clusters. When the number of clusters is extended to eleven, some clusters exhibit combinations of characteristics that are not present in the case with five clusters, including clusters with extreme values. Clusters with characteristics that diverge from typical commuter or non-commuter patterns are obtained.
Using the lower state of charge (SOC) threshold for charging decisions and the SOC when charging ends, the flexible battery capacity range is calculated to be 59% on average. The aggregated SOC for all logged EVs is within 60%–80% throughout the entire logging period. The results show that charging is needed in fewer than half of the days in a week for more than 73% of weeks, regardless of the attributes of the EV owners, including commuter category and battery capacity. Furthermore, EVs are charged more frequently than the minimum number of charging events required per week. Thus, there is potential for charging in a way that is flexible in time depending on, for example, grid congestion or spot prices. This is particularly the case for non‑commuters with large‑battery EVs. The amount of time that EVs are plugged in for smart charging differs by more than a factor of two if one assumes that EVs are plugged in whenever they are parked at home and that EVs are plugged in only when they charge during the parking event. Thus, careful consideration of which of these assumptions is appropriate is essential when estimating the availability of EVs for smart charging, as the choice can significantly affect the outcomes. Installation of chargers at workplaces can increase the number of grid-connected EVs at places other than the home location, although very few EVs need to charge at workplace to fulfill their driving demand. Incentives to promote plug‑in behavior at the home location can, therefore, be prove to be cost-effective at increasing the number of grid-connected EVs. This flexibility at home is exploited by EV owners with hourly electricity contracts through selecting charging times when the spot price is lower than the daily average. In this thesis, the logged EVs are clustered into three, five, and eleven clusters, resulting in groups with distinct characteristics. When the number of clusters is increased from three to five, a cluster with a low probability of parking at home during the night and a cluster maintaining a high SOC are added to the three clusters. When the number of clusters is extended to eleven, some clusters exhibit combinations of characteristics that are not present in the case with five clusters, including clusters with extreme values. Clusters with characteristics that diverge from typical commuter or non-commuter patterns are obtained.
Yuki Kobayashi
- Doctoral Student, Energy Technology, Environmental and Energy Sciences