Tire road adhesion coefficient multi-model fusion estimation method considering quality mismatch

Abstract

The invention discloses a vehicle state estimation method under the condition of abnormal measurement data of a vehicle-mounted sensor, which specifically comprises the following steps of: acquiring longitudinal acceleration, transverse acceleration, yaw velocity and front wheel rotation angle signals of a vehicle , combining with a nonlinear vehicle model, estimating axial force information of the vehicle by using strong tracking unscented Kalman filtering, estimating a tire road adhesion coefficient by utilizing interactive multi-model unscented Kalman based on the axial force information of the vehicle; wherein the vehicle axial force information includes longitudinal and lateral forces of a front axle of the vehicle and longitudinal and lateral forces of a rear axle of the vehicle. Through interaction, mixing, prediction and fusion, the invention provides an estimation method which can combine the advantages of a plurality of models to realize accurate estimation of the tire road adhesion coefficient under a complex driving condition, then updates a posteriori state and a covariance matrix P eta thereof, and adopts a prior and posteriori combined estimation method the technical blank that the axial force of the vehicle cannot be accurately estimated under the current mass mismatching condition can be filled.

Description

technical field [0001] The invention relates to the field of vehicle control, in particular to a multi-model fusion estimation method for tire road surface adhesion coefficient considering mass mismatch. Background technique [0002] In order to improve the safety of automobiles, many active safety technologies have been developed to reduce traffic accidents, such as active collision avoidance systems and body stabilization systems. The effective implementation of these systems is directly affected by key information such as the tire-road adhesion coefficient. However, the tire-road adhesion coefficient cannot be directly measured by on-board sensors. Therefore, some estimation methods based on state observers are used to solve this problem. But a basic premise in the traditional estimation method is that the mass parameters in the vehicle model are known accurately and the driving manipulation is relatively simple. [0003] However, it ignores some actual working conditi...

AI Technical Summary

Problems solved by technology

[0003] However, it ignores some actual working conditions. The mass difference is obvious when the vehicle is unloaded and fully loaded, and some complex driving maneuvers are also common. In this case, the existing tire-road adhesion coefficient estimation method may not be applicable due to excessive estimation deviation. to reality

Images