The rise in EV penetration also presents new challenges for infrastructure management due to the intertwining of the transportation system with the electric power system through charging infrastructure. The integration of EVs into the transportation network requires a dynamic transportation network modeling approach that considers the impacts of EV charging. Existing studies often suffer from drawbacks such as static traffic models, limited network size, and unrealistic assumptions about traffic demand and EV behavior. The study proposes a holistic framework, incorporating essential EV-related components such as charging stations, EV charging routing, charging behavior, and energy consumption, into a dynamic traffic model for large-scale networks. The framework is applied to a large-scale transportation network in the Central Ohio region. The high-granularity dynamic traffic model is calibrated with real-world traffic data and vehicle registration data, resulting in more accurate traffic flow and energy consumption estimations. Based on the calibrated model, different scenarios are analyzed and results reveal spatiotemporal variations in charging station utilization and neighborhood-level electricity consumption patterns. Additionally, the study analyzes the impacts of EV penetration ratio, roadside charging ratio, fast charging station coverage ratio, on network-wide energy consumption and waiting times at charging stations. The proposed flexible framework provides a foundation for future studies to refine sub-models and explore the inter-dependency between traffic and electric power systems more comprehensively.