Abstract: Rubber solid tires are mostly used in mine heavy-duty vehicles, and their mechanical behavior its quite different from that of traditional pneumatic tires. The actual movement status of mine heavy vehicles cannot be accurately described, which severely restricts the study of mine vehicle dynamics and stability due to the lack of accurate tire model parameters. In order to establish an accurate parameter identification model for heavy traction solid tire in mine vehicles, its longitudinal force calculation formula is modified by PAC2002 magic formula model and the parameters to be identified are determined. A hybrid optimization parameter identification algorithm for heavy rubber solid tire classical model is proposed by using a combination of 3 iterative means: Gaussian Newton iteration, genetic iteration and simulated annealing. The basic experimental data of filled rubber tire fitted to a underground coal mine 25 t Heavy-Duty vehicle are acquired through a six component testing equipment, and the parameters of the tire model under two operating conditions of pure longitudinal slip and laterality longitudinal slip compound are discriminated by a hybrid optimization algorithm and a genetic algorithm, and the relative root mean square error and determination coefficient are introduced as evaluation indexes of identification accuracy. The results show that: For the parameter identification model under two working conditions, the maximum root mean square error of the objective function value is 0.08135 and 0.07965 respectively, and the determination coefficients are 0.98815 and 0.98765 respectively. Moreover, the hybrid optimization process has better global control and fast convergence ability than simple genetic iteration. the average algebra convergence to global optimum is reduced by 36% and the average time to global optimum is reduced by 31%; Finally, the vehicle traction characteristics under different load conditions are tested, the test results show that the deviation between the longitudinal traction of a single tire calculated by the identification parameters and the vehicle test results is not more than 4%, and the validity of the identification model is verified.