This paper defines a method for generating individual electric vehicle charging patterns and it intends to quantify the realistic loading impact on distribution grid feeders. The inputs are based on historical driving characteristics of private conventional vehicles from Denmark and home plug-in behavior of EVs from Japan. The first input is used to define properties such as the daily driven distance and the expected departure and arrival time, which determines the possible home charging window. The second input is used to quantify the probability of performing a domestic charge every day. Because most of the EVs does not need to charge every day, even when considering a 100% EV penetration scenario, the amount of simultaneous charging with domestic single-phase charging power (3.7 kW) determines a coincidence factor lower than 45%. When considering three-phase charging (11 kW), the combined power of the EV population increases only to 50% because of shorter charging sessions. Although the power increase, due to 11 kW charging, is likely to trigger grid components overloading, it is highlighted how uncontrolled distribution of single-phase charging could be responsible for local voltage unbalances.

Accelerated battery degradation, resulting in a reduced capacity, is the main concern when discussing vehicle-to-grid (V2G) services. This paper gives a unique empirical insight into the long-term effects on batteries performing an energy demanding V2G service. To this end, an extensive method for measuring the battery capacity of series produced electric vehicles (EVs) via the DC charge port was developed. The reproducibility of the method is demonstrated with repeated tests on new and aged vehicles and the results are compared with the EVs internal estimates, read via the on-board diagnostics port. The method is also validated with a state-of-the-art battery degradation model. The capacity test method is used to evaluate the battery degradation of a fleet of commercially owned EVs which, as a part of the world's first commercial operation, have been used for V2G services for five years. The EVs have, together with ±10 kW V2G chargers, been delivering primary frequency regulation for 15 hours per day with a daily energy throughput of 50.6 kWh. The usable battery capacity, initially found to be 23 kWh, after two years is reduced on average to 20.7 kWh and after five years is reduced to 18.9 kWh.

This study models the coincidence factor (CF) of electric vehicle (EV) charging given driving and plug-in behaviors by combining data sources from travel surveys and recorded EV charging data. From these, we generate travel and plug-in behaviors by using a Monte Carlo approach to derive CFs. By varying the EV battery size, the rated charging power, and the plug-in behavior, their influence on the CF is examined. The key results show that the CF decreases to less than 25% when considering more than 50 EVs with a charging level of 11 kW, with the CF strongly depending on the number of EVs considered. By contrast, the driving behavior and the battery size have a minor influence on the CF. Furthermore, when mixing the parameters, such as EV battery size and rated charging power, then, especially, the active power drawn by the feeder does not change linearly. Ultimately, the study aims to add to the state-of-the-art by solely and systematically focusing on the CF and its sensitivity to a number of key factors. For planning and design, distribution system operators may use this study as a part of their planning for the integration of electric vehicles in the electrical grid.