The rapid adoption of electric vehicles (EVs) is transforming transportation and energy systems, creating new opportunities for data-based optimization of battery use, charging infrastructure, and energy management. However, the limited accessibility of real-world EV data due to privacy constraints, proprietary restrictions, and high collection costs poses significant challenges for developing and validating analytical and predictive models. To address this limitation, this thesis proposes a comprehensive framework for generating and evaluating synthetic electric vehicle time-series data that reproduces the temporal, statistical, and behavioral characteristics of real-world operation. The approach models two fundamental event types, trips and charging sessions, which form irregular, event-driven sequences with strong causal dependencies.