A CDC damper damping self-adaptive control method based on multi-working condition perception

By constructing a neural network model and combining it with multi-condition perception data, adaptive adjustment of the damper damping force is achieved, solving the problem that traditional suspension damping adjustment devices cannot accurately match under multiple conditions, thus improving the vehicle's ride comfort and handling stability.

CN122126040APending Publication Date: 2026-06-02SWISS CHONGQING AUTOMOBILE R&D CENT CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SWISS CHONGQING AUTOMOBILE R&D CENT CO LTD
Filing Date
2026-04-02
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing automotive suspension damping adjustment devices are difficult to adjust to the most suitable damping coefficient quickly and accurately under various operating conditions, resulting in the vehicle's ride comfort and handling stability not always maintaining their optimal state.

Method used

An adaptive control method for damper damping based on multi-condition perception is adopted. By acquiring vehicle body data, road condition data, and vehicle motion state data, a neural network model is constructed to achieve adaptive adjustment of damper damping force. Multi-dimensional data features are extracted using convolutional layers and fully connected layers, and the target force value of the active damper is calculated in combination with road surface unevenness. The damping force is then adjusted through a solenoid valve.

Benefits of technology

It enables precise adjustment of shock absorber damping force under multiple operating conditions, improving the adaptability and accuracy of suspension damping adjustment, and ensuring vehicle comfort and stability under different driving conditions.

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Abstract

This invention discloses an adaptive control method for CDC damper damping based on multi-condition perception. The method includes acquiring vehicle body data and multi-source condition perception data of vehicle movement. A neural network model for inferring the target force value of the damper is constructed using the acquired data. Based on the input layer, hidden layer, and output layer of the neural network model, and combined with initial coefficients obtained from road surface unevenness, the target force value of the active damper is calculated after weighted fusion. Subsequently, based on the ratio of vehicle speed V1 and damper speed V2, the damper damping force is adaptively adjusted. By collecting multi-source perception data on vehicle body, road conditions, vehicle motion, and damper operating status, and introducing a neural network model to complete the correlation extraction and nonlinear inference of multi-dimensional condition features, the method can accurately output the appropriate target force value for the damper under different driving conditions, significantly improving the adaptability and accuracy of CDC damper damping adjustment.
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