This chapter proposes two distinct modeling frameworks for power-traffic coupled networks that incorporate the bounded rationality of users to better reflect realistic travel and charging behaviors. First, a bounded rationality decision model is developed using a tolerance parameter to quantify the degree of rationality, contrasting it with the fully rational model. Based on this, a multi-period static modeling method is introduced, where traffic flow is characterized as a bounded rationality static user equilibrium without dynamic propagation. Subsequently, a dynamic modeling method is proposed, modeling traffic flow as a bounded rationality dynamic user equilibrium with full propagation characteristics. Within the dynamic framework, an improved point queue model based on difference equations is designed to accurately capture the entry, queuing, charging, and departure processes of electric vehicles at fast-charging stations, enabling precise tracking of the transformation from traffic flow to charging load. In both approaches, the distribution network is represented by a multi-period optimal power flow model, with nodal marginal prices serving as charging prices. The interaction between traffic flow and power flow is characterized via a fixed-point model, defining the equilibrium state of the coupled network. Iterative solution algorithms are developed accordingly. Case studies validate the effectiveness of both methods: the multi-period static model offers high computational efficiency for identifying critical periods and infrastructure, while the dynamic model provides finer temporal resolution and reveals microscopic coupling mechanisms, offering robust theoretical support for the flexible scheduling of fast-charging infrastructure.