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The OOR (out-of-roundness) wheel is one of the main excitation sources causing vehicle vibration. However, the OOR wheel is random, which indicates that the vehicle vibration obtained with a deterministic OOR wheel cannot comprehensively express dynamic performances. To this end, a probability analysis framework is proposed in this paper. First, the probability model of the OOR wheel is derived; Second, the equation of the vehicle system dynamics is modelled; Then, the direct probability integral method is developed to obtain the PDF (probability density function) of vehicle random vibration; Finally, the statistics characterized random vibration characteristics of the vehicle are calculated. The effectiveness of the proposed framework is verified with a case study. The results show that the PDF of vehicle random vibration excited by the Gaussian distribution OOR wheel excitation exhibits a right-skew shape, which significantly affects the dynamic performance. Compared to the Monte Carlo simulation, the proposed framework has higher computational efficiency for the same accuracy to analyze vehicle random vibration.

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An Efficient Probability Analysis Framework to Obtain Vehicle Random Vibration Considering the Randomness of Out-of-Roundness Wheels

  • 会议论文

  • 发表日期：

    2025年

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  • Tengfei Wang,
  • Jinsong Zhou,
  • Wenjing Sun,
  • Guoshun Li

摘要

The OOR (out-of-roundness) wheel is one of the main excitation sources causing vehicle vibration. However, the OOR wheel is random, which indicates that the vehicle vibration obtained with a deterministic OOR wheel cannot comprehensively express dynamic performances. To this end, a probability analysis framework is proposed in this paper. First, the probability model of the OOR wheel is derived; Second, the equation of the vehicle system dynamics is modelled; Then, the direct probability integral method is developed to obtain the PDF (probability density function) of vehicle random vibration; Finally, the statistics characterized random vibration characteristics of the vehicle are calculated. The effectiveness of the proposed framework is verified with a case study. The results show that the PDF of vehicle random vibration excited by the Gaussian distribution OOR wheel excitation exhibits a right-skew shape, which significantly affects the dynamic performance. Compared to the Monte Carlo simulation, the proposed framework has higher computational efficiency for the same accuracy to analyze vehicle random vibration.