Robust Optimization of High-Speed Rail Vehicle Suspension Parameters Based on Vertical Running Stability

摘要

Since noise factors have a significant influence on vertical running stability of high-speed rail vehicles, robust optimization of the suspension parameters can improve the robustness of vehicle under different running conditions, and thus ensure running quality. Vertical stiffness and damping of primary and secondary suspensions were here regarded as controllable factors, with speed, passenger capacity and railway curve radius selected as noise factors. Then Taguchi method was introduced to construct a basic robust optimization model of vehicle suspension parameters. Based on the advantages of non-linear fitting of Radial Basis Function (RBF) surrogate model, an RBF surrogate model of vehicle vertical running stability was constructed to analyze the influence of both controllable factors and noise factors. On this basis, the suspension parameter combination with best robustness was determined through internal and external orthogonal testing of the controllable factors and noise factors, as well as signal-to-noise ratio analysis. The results indicated that, after robust optimization of the suspension parameters, the mean value of the vertical running stability index under different running conditions was improved by 7.55%, and the amplitude of vertical running stability index over the whole range was reduced by 31.0%, which validated the effectiveness of the proposed method.