Vehicle side slip angle adaptive fusion and compensation method considering multi-source input information

Abstract

The invention provides a vehicle side slip angle self-adaptive fusion and compensation method considering multi-source input information, which comprises the following steps of: determining an input discourse domain of a fuzzy logic system by using a steering wheel angle and a yaw rate, determining a weight coefficient by the fuzzy logic system according to a decision rule for different steering wheel angle intervals and yaw rate intervals, self-adaptively adjusting the vehicle side slip angle estimated according to the smooth variable structure filter and the weight of the vehicle side slip angle calculated by using an integral damping method so as to ensure reliable fusion of side slip angle information; and when the vehicle is in a severe working condition, triggering a side slip angle compensation strategy, achieving accurate correction of the tire cornering stiffness through iterative calculation according to the fused relation among the side slip angle, the tire side slip angle, the tire lateral force and the wheel turning angle, wherein the corrected tire cornering stiffness is used for calculation and updating of the tire longitudinal force at the next moment. The accurate and reliable output of the vehicle side slip angle under all working conditions can be guaranteed.

Description

technical field [0001] The invention belongs to the technical field of vehicle system key information perception, and in particular relates to an adaptive fusion and compensation method of vehicle center of mass sideslip angle considering multi-source input information. Background technique [0002] The side slip angle of the center of mass is a key parameter in the field of vehicle dynamics control. There are mainly two methods for measuring this parameter: direct measurement method and soft sensor method. Among them, the direct measurement method requires the use of expensive optical sensors, which have high requirements for the installation of the sensors and the measurement accuracy is easily disturbed by the external environment, so it is difficult to carry out large-scale application on mass-produced vehicles. In comparison, the center-of-mass side slip angle soft-sensing method based on ordinary vehicle sensor signals has significant advantages such as low cost and st...

AI Technical Summary

Problems solved by technology

However, for the non-linear region of tire characteristics, the correction strategy still uses the linear regional tire cornering stiffness

There is a large gap between this and the actual tire cornering stiffness, so the actual correction effect is general

In addition, the neural network method is also used to obtain the cornering stiffness at any time, but this method requires a large amount of training data and the data between different vehicles cannot be used interoperably, so it is difficult to implement

The above method can improve the acquisition effect of tire cornering stiffness to a certain extent, but there are problems in accuracy and versatility

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