As mobility evolves, Electric Vehicles (EVs) are becoming increasingly prevalent, offering new challenges and opportunities for understanding user behavior and optimizing charging strategies. This thesis presents the enhancement and evaluation of a Battery Electric Vehicle (BEV) simulator, along with an analysis of the factors behind unsatisfied trips for specific EV models under predefined charging policies. The study uses real-world driving data on ICE cars provided by UnipolTech. A previous version of the simulator developed by the research team was enhanced in terms of efficiency, scalability, realism, and flexibility, and models the behaviors of diverse charging profiles, specifying constraints based on time of the day, day of the week, charging durations, state-of-charge (SoC) thresholds, and charger preferences (AC or DC); integrating 50 EV models with different battery capacities, energy consumption rates and charging capabilities.