Vehicle control method and vehicle

By integrating attitude processing with IMU and dynamic parameters, and using confidence coefficients and weighted Kalman algorithms to generate target attitude parameters, the noise and road surface adaptation problems in lateral acceleration calculation of fully active suspension vehicles are solved, achieving more accurate vehicle attitude control.

CN122354147APending Publication Date: 2026-07-10GREAT WALL MOTOR CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
GREAT WALL MOTOR CO LTD
Filing Date
2026-06-09
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

When calculating the roll control force of a fully active suspension vehicle, the lateral acceleration measured by the IMU has large noise and the amplitude fluctuation exceeds the true value. The dynamic parameters are not accurately calculated on different road surfaces, resulting in the failure of vehicle attitude control.

Method used

The second attitude parameters are calculated by fusing the first attitude parameters and dynamic parameters measured by the IMU, and then fused using confidence coefficients and a weighted Kalman algorithm to generate target attitude parameters for vehicle control.

Benefits of technology

It improves the accuracy and adaptability of vehicle attitude parameters, avoids noise interference and road surface adaptation problems of single measurement methods, and achieves more effective vehicle attitude control.

✦ Generated by Eureka AI based on patent content.

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Abstract

This application discloses a vehicle control method and a vehicle, relating to the field of vehicle chassis technology. The method includes: acquiring dynamic parameters including auxiliary parameters and measured vehicle posture corresponding to a first posture parameter; generating a second posture parameter corresponding to the vehicle posture during operation based on the dynamic parameters; obtaining a confidence coefficient characterizing the reliability of the first posture parameter based on the first and second posture parameters; and fusing the first and second posture parameters based on the confidence coefficient and auxiliary parameters to obtain a target posture parameter. By fusing the first and second posture parameters, the accuracy of the obtained target posture parameter is improved, thereby enhancing the effectiveness of vehicle control based on the target posture parameter.
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