Vehicle kinetic parameter estimation method based on machine learning

Through the machine learning-based vehicle dynamic parameter estimation method, the sensitivity of vehicle dynamic parameter analysis and prediction is solved, and the accuracy and reliability of simulation tests are achieved.

CN119647286AActive Publication Date: 2025-03-18ANHUI JIANGHUAI AUTOMOBILE GRP CORP LTD
9 Cites 0 Cited by

Patent Information

Application Number
CN202411872852.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-18
Publication Date
2025-03-18
Estimated Expiration
2044-12-18

AI Technical Summary

Technical Problem

In the simulation test of intelligent connected vehicles, the accuracy of vehicle dynamic parameters is difficult to ensure, resulting in the large impact of the same control strategy on different cars, making it difficult to carry out subsequent testing work.

Method used

Using machine learning-based vehicle dynamic parameter estimation method, the sensitivity of vehicle dynamic parameter is analyzed through orthogonal test method and simulation test, unknown parameters are determined, and sensitive parameters are predicted using a fully connected neural network, and finally checked in the simulation software.

Benefits of technology

It effectively solves the sensitivity of vehicle dynamic parameters, provides reliable estimation of unknown parameters, and ensures the accuracy and reliability of simulation tests.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a vehicle dynamics parameter estimation method based on machine learning, which mainly obtains important parameters of vehicle dynamics involved in simulation test software through analysis, provides a prediction method for unknown parameters in the important parameters, and provides a solution for the condition that some parameters are difficult to obtain in engineering. Specifically, a grouping orthogonal test method is adopted, the test of a simulation software platform is taken as the basis, the importance of vehicle dynamics parameters is judged, a full-connection neural network is adopted to predict important unknown parameters of vehicle dynamics, and a real vehicle test result is compared with a simulation test to verify the prediction accuracy. The method focuses on analyzing and predicting unknown parameters in the vehicle dynamic parameters according to the characteristics that the types of the vehicle dynamic parameters are various and the influence of each parameter on the vehicle dynamic response is different, effectively solves the problem of sensitivity of the vehicle dynamic parameters with the automatic driving function, and particularly reliably estimates the unknown vehicle dynamic sensitive parameters.
Need to check novelty before this filing date? Find Prior Art