Abstract: To address issues in the traditional overhead line traction machine tail reel, such as low tension control accuracy, poor synchronization between the rope drum and traction mechanism, and weak dynamic disturbance resistance during the winding and unwinding process, a digital twin-based intelligent tail reel dynamic modeling method for overhead line traction machines is proposed. Firstly, combining multibody dynamics with rope transmission theory, a core multibody dynamic model of the intelligent tail reel is established. Secondly, by integrating real-time tail reel data collected from sensors, a real-time mapping mechanism between the physical tail reel and the virtual model is constructed. Finally, simulation experiments of tail reel winding and unwinding conditions are conducted based on this digital twin model. The results show that the proposed method achieves a tension fluctuation range of ≤±3%, reduces synchronization errors by 75% compared to traditional methods, and ensures that when the load suddenly changes, the system stabilizes within ≤0.5 seconds. The dynamic disturbance resistance is significantly improved, providing high-precision technical support for the automated control of overhead line traction machine tail reels.