The rapid growth of electric vehicles has increased the need for intelligent charging strategies capable of balancing user demand, charging station capacity, grid stability, and service provider objectives. Pricing plays a central role in this context, since charging tariffs influence user behavior and affect the allocation of electric vehicles across stations and time slots. Bi-level optimization provides a suitable modeling framework for this problem because it captures the hierarchical interaction between a charging service provider, who determines tariffs, and electric vehicle users, who respond by selecting charging options according to cost and convenience. This research builds on an existing bi-level model for electric vehicle charging tariff determination, originally designed as a synthetic data generation framework for future artificial intelligence applications.