Vehicle running state and tire magic formula parameter joint estimation method

Abstract

The invention relates to a tire key parameter and vehicle running state identification method and belongs to the field of information science. The method comprises the steps of establishing a multi-degree of freedom vehicle kinetic equation, substituting lateral force determined in a tire magic formula into the equation, utilizing a Runge-Kutta method to convert the vehicle kinetic equation into a Markov chain form, achieving vehicle running state and tire magic formula parameter joint estimation through particle filter, introducing an auxiliary variable to enable particle weight change to be more stable through secondary weighting operation, adopting a polynomial sampling method to perform particle re-sampling, introducing a random perturbation method in the re-sampling process to increase particle diversity while improving particle degeneracy, and achieving simultaneous estimation of tire magic formula parameters and a vehicle running state. A good estimation result can be obtained, road adhesion coefficients can be further calculated according to estimated parameters, road surface characteristics can be determined, and a basis is provided for future intelligent vehicle running control.

Description

technical field [0001] The invention belongs to the field of system state and key parameter estimation in information technology, in particular to a method for jointly estimating vehicle driving state and tire magic formula parameters. Background technique [0002] The state estimation in the process of automobile driving is an important problem in automobile dynamics. Its purpose is to determine the important state variables such as longitudinal speed, yaw rate, center of mass side slip angle and yaw angle displacement under the driving state of the car. One of the key technologies of chassis active control. The longitudinal dynamics control of the car depends on the accurate estimation of the longitudinal vehicle speed; the lateral dynamics control of the car depends on the accurate estimation of the yaw rate or the sideslip angle of the center of mass. Although these state variables to be estimated can be directly measured by sensors, they must rely on expensive special ...

AI Technical Summary

Problems solved by technology

The focus of the research is to estimate the yaw rate and vehicle speed of the vehicle through the Kalman filter and its improved algorithm, and most of them stay in the simulation research stage. Due to the limitations of the Kalman filter itself, the estimation accuracy and stability are also poor; However, there are few studies on the identification of key parameters of vehicles, especially the key parameters of tires, and the research on the joint estimation of vehicle tire parameters and vehicle driving state is especially important for future intelligent driving technology.

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