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), can support the electric grid in the future energy systems. The aim of this study is to understand the characteristics of flexibility for smart charging including V2G from analyzing real-world driving, parking and charging patterns of logged EVs.

The following three key factors for flexibility for smart charging are analyzed: (i) energy demand for driving, (ii) preference of state of charge (SOC) for EV owners and (iii) possibility of shifting charging time in this study. The analyzed data is collected from GPS logging of 395 randomly selected private EVs in Sweden for a year long and answers to a survey sent to the participating EV owners to understand the EV owner’s preferences and attributes.

The analyses show that the average daily charged energy is ranging from 4 to 10kWh for 90% of all EVs. Furthermore, 40% of the logged EVs are always charged if the SOC is below 30% when arriving at home. About 70% of EVs charge until the SOC reaches 100%, while 20% and 10% of EVs stop charging when the SOC reaches 80% and 90%, respectively.

The possibility of EVs for smart charging in the future is represented by probability of parking at different locations if assuming that the EVs then are connected to the grid in any location as long as the EVs are parked. The average probability of parking at home is the lowest around noon (50%) in weekdays. For commuters, the probability of parking at workplaces is approximately 35% during daytime in weekdays.

For the current EV charging patterns, only 12% of the charging events at home with private chargers need higher than 3kWh/h of charging power if the EV is connected to the charger throughout the parking event. Since the parking duration is shorter at workplaces, 27% of the charging events need at least 3kWh/h. At public AC chargers and DC chargers, 46% and less than 1% of charging events could be satisfied with lower than 3kWh/h, respectively. Since small share of charging events need 3kWh/h or lower charging power, there is large flexibility for smart charging at home and workplaces.

The data show the flexibility is utilized by some of the logged EVs. EV owners who have hourly electricity contract charge EVs depending on the spot price when spot price is higher than daily average. The analysis also reveals that the probability of charging does not depend on the SOC when spot price is 20Eur/kWh or higher than daily average.

The EVs are also clustered based on the key factors for flexibility for smart charging to observe generalizability of the EV usage patterns since the generalizability to a few clusters can be important in many studies such as energy systems modeling. The clustering enables energy systems modeling to avoid input parameters of a large amount of EV usage patterns as well as to avoid simplification such as regarding all EVs as one aggregated large battery.