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This paper focuses on parameter optimization for the actually manufactured test vehicle. This method achieves high-precision, rapid computation of vehicle dynamic performance while fully preserving the strongly coupled nonlinear dynamic properties of the system. Firstly, by employing twin modeling technology, the model accurately reflects the physical dynamic characteristics of the actual vehicle, enabling us to determine how much improvement the optimized vehicle dynamic response will exhibit compared to the current state. Next, a mathematical model for multi-parameter, multi-objective collaborative optimization is constructed using big data search, and key parameters significantly influencing vehicle dynamics are identified through Sobol sensitivity analysis for dynamic optimization. Finally, an improved multi-start parallel simulated annealing algorithm is proposed to enhance the computational efficiency and reliability of the optimization results. The results demonstrate significant improvement in the dynamic performance of the experimental vehicle, validating the effectiveness of the proposed method. This approach overcomes the limitations of traditional linearization treatments, providing a new perspective for dynamic optimization of complex coupled systems and demonstrating significant engineering application value in the field of rail transportation.