Loss of control inflight is the most common cause of fatal accidents in aviation. Aerodynamic stall models are utilized in pilot training to enhance safety and prevent accidents. This research presents an advanced longitudinal stall model for the Cessna Citation II, achieved through innovations in modeling methodologies and experimental design. By introducing dynamic stall maneuvers with step inputs, the study mitigated $\tau_1$ and $\tau_2$ parameter correlation, enabling more reliable parameter identification. A separable nonlinear least squares method significantly reduced computational time for nonlinear stall model estimation, decreasing it from hours to seconds. This approach revealed two minimally correlated flow separation states, offering deeper insights into wing flow characteristics and improving model accuracy. The lift model was refined to incorporate pitch rate and elevator deflection effects, while the drag model was enhanced with a lift-induced drag component. Additionally, a center of pressure model was derived from pitching moment data, advancing the understanding of stability during stall. A novel structure for characterizing degraded elevator control effectiveness was also developed. These advancements resulted in substantial performance improvements, with mean squared errors for lift, drag, and pitch moment coefficients reduced by 32\%, 29\%, and 27\%, respectively. The models also demonstrated greater consistency across diverse maneuvers, evidenced by reduced variability in $R^2$ values. This work contributes to more accurate stall modeling, enhancing both aerodynamic understanding and aviation safety.