Electric vehicles can play a central role in reducing greenhouse gas (GHG) emissions if they are powered by electricity produced from renewable energy sources. On the other hand, the integration of electric vehicles requires the development of charging stations for electric vehicles and their efficient use in order to reduce the impact on the network and reduce the costs of network development related to the acceptance of electric vehicles or charging stations. To analyze the impact of the connection of filling stations to the power grid, it is necessary to know the trends and characteristics of consumption at the same level, which are often not available. Some of the key data that need to be considered in such analyzes are the time of arrival of the vehicle, the duration of charging, the time the charger is occupied, the amount of energy taken, the maximum charging power, etc. In the absence of actual data from the charging stations, in the analyzes of the impact of the connection of the charging stations to the power grid, the specified data are most often indirectly modeled on the basis of the usual behavior patterns of vehicle users based on GPS readings of their movement, historical data from different traffic systems, laboratory tests, etc. At the same time, the characteristics of electricity consumption at the filling station level largely depends on its location and purpose: public filling stations, residential filling stations, filling stations within business entities, … Given the relatively large number of filling stations in some countries and the public availability of data, much better quality data is available today, on the basis of which it is possible to better determine the basic data necessary for modeling consumption at the level of filling stations. In this paper, real data from larger electric vehicle charging stations will be analyzed and probability functions will be determined for some basic parameters that define the characteristics of an individual charging cycle.