  Electric vehicle (EV) charging stations have become prominent in electricity
grids in the past years. Analysis of EV charging sessions is useful for
flexibility analysis, load balancing, offering incentives to customers, etc.
Yet, the limited availability of such EV sessions data hinders further
development in these fields. Addressing this need for publicly available and
realistic data, we develop a synthetic data generator (SDG) for EV charging
sessions. Our SDG assumes the EV inter-arrival time to follow an exponential
distribution. Departure times are modeled by defining a conditional probability
density function (pdf) for connection times. This pdf for connection time and
required energy is fitted by Gaussian mixture models. Since we train our SDG
using a large real-world dataset, its output is realistic.
