Method for jointly estimating vehicle mass-road gradient under consideration of influence of braking and turning

Abstract

The invention discloses a method for jointly estimating vehicle mass-road gradient under the consideration of influence of braking and turning. The method includes steps of 1, acquiring data, to be more specific, 11, acquiring vehicle driving state data by data acquisition devices, 12, combining the vehicle driving state data and vehicle intrinsic parameters with one another and computing relevant parameters required by models; 2, building relation models of vehicle mass, road gradient and the vehicle driving state data on the basis of vehicle dynamic models; 3, building least square mass estimation models and Kalman filter gradient estimation models on the basis of the relation models of the vehicle mass, the road gradient and the vehicle driving state data; 4, jointly estimating the vehicle mass and the road gradient by means of nested loop iteration; 5, eliminating the influence of braking and turning in state maintenance modes. The method has the advantages that the influence of braking and turning is considered in real vehicle driving procedures, and accordingly the vehicle mass and the road gradient can be dynamically jointly estimated in real time.

Description

technical field [0001] The invention relates to a joint estimation method of vehicle mass and road gradient based on least squares and Kalman filtering, which considers the influence of braking and turning during real vehicle driving, and realizes real-time dynamic joint estimation of vehicle mass and road gradient . Background technique [0002] Real-time estimation of vehicle dynamics model parameters is the basis of vehicle control, and vehicle mass and road gradient are important parameters in vehicle dynamics model. Accurate and real-time estimation of vehicle mass and road gradient can effectively improve vehicle dynamics and economy. On-line adjustment of the shift control strategy according to the vehicle quality can not only make the vehicle run more smoothly during the automatic shift process, but also obtain a more economical shift control strategy. Based on information such as road gradient, speed and acceleration, the vehicle power coefficient can be calculated...

AI Technical Summary

Problems solved by technology

Although there are many methods in this regard, a common difficulty lies in the decoupling of the vehicle's own parameters (weight, etc.) and external resistance (slope) changes. In addition, the time-varying nature of the road also increases the complexity of the estimation process

The braking force and lateral dynamics brought by braking and turning during actual driving will affect the longitudinal dynamics model of the car, and the existing methods do not consider the influence of braking and turning on parameter estimation during actual driving, which will greatly reduce the Estimation Accuracy of Car Mass and Road Slope

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