Wheel impact load prediction method, device, equipment and readable storage medium

CN122242255APending Publication Date: 2026-06-19XIANGYANG DAAN AUTOMOBILE TEST CENT

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Applications(China)
Current Assignee / Owner: XIANGYANG DAAN AUTOMOBILE TEST CENT
Filing Date: 2026-03-27
Publication Date: 2026-06-19

AI Technical Summary

Technical Problem

Existing technologies require costly and risky physical testing to obtain wheel impact load data, while pure virtual simulation methods are difficult or fail to simulate at medium to high vehicle speeds and cannot accurately predict loads at medium to high vehicle speeds.

Method used

By acquiring training data at low and medium vehicle speeds, a transfer rate function is established, and the triaxial force at the wheel center at high vehicle speeds is predicted using the transfer relationship between signals. This avoids dependence on complex tire models. Low-frequency and high-frequency transfer rate models are constructed using frequency band separation and regularization methods, and prediction is performed by combining convolution operations.

Benefits of technology

It enables rapid and reliable prediction of wheel loads under challenging working conditions in the early stages of development, reducing the risks and costs of simulation and testing, and accurately predicting the three-dimensional forces and moments at the wheel center.

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Abstract

A method, apparatus, device, and readable storage medium for predicting wheel impact loads are disclosed. The method includes: acquiring training data at low to medium vehicle speeds, establishing a data-driven transfer rate function, and then using this function and easily obtainable excitation point signals at the target vehicle speed to predict the triaxial force at the wheel center. This method eliminates the reliance on complex and divergent tire physical models, relying solely on the transfer relationships between signals for prediction. Since the training data originates from low to medium vehicle speed conditions, while the prediction target is high vehicle speed conditions, it cleverly solves the problems of difficult simulation or high experimental risks at medium to high vehicle speeds in existing technologies, enabling rapid and reliable prediction of wheel loads under challenging conditions in the early stages of development without the need for a complete prototype vehicle. Furthermore, it should be noted that force and torque have fixed conversion rules; obtaining the triaxial torque at the wheel center based on the predicted triaxial force value is self-evident.

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