ABSTRACT
To address the challenges of lengthy development cycles and high testing costs in matching tire and vehicle mechanical characteristics, a fast and efficient virtual sampling method for tire mechanical properties is proposed. First, a detailed finite element model is established according to the material distribution diagram and material properties of the tire. Second, under the premise of ensuring simulation accuracy, structural simplifications and friction simplifications are applied to the detailed finite element model. A finite element friction subroutine is incorporated to accurately express the dynamic friction characteristics between the tire tread and the road surface. Then, the pure cornering and pure driving/braking mechanical characteristics of the tire are obtained through finite element simulations. With a high-precision combined-condition tire mechanical property prediction method, fast and accurate predictions of combined-condition forces and torques are made. Finally, the MF tire model is identified based on the data obtained from the finite element model and the prediction method. The results show that the finite element model can accurately obtain the lateral force, longitudinal force, and aligning moment of the tire in pure cornering and pure driving/braking, with an average accuracy of 93.4%, 88.4%, and 80.7%, respectively. Based on the pure condition data obtained from the tire finite element model, the mechanical properties under combined conditions are predicted, with average prediction accuracies of 92.82% for longitudinal force and 91.38% for lateral force. The predicted aligning torque exhibits a trend consistent with experimental results. The MF model is identified using the data from the tire finite element model and prediction method, achieving good accuracy for both forces and torques. The fast and efficient virtual sampling method for tire mechanical properties not only effectively shortens development cycles and reduces testing costs but also, by combining finite element models with predictive methods, enables the advancement of tire mechanical property development to the design stage, further enhancing the efficiency of virtual sampling for tire models.
