Vertical speed estimation method of all-wheel drive vehicle

Abstract

The invention provides a vertical speed estimation method of an all-wheel drive vehicle. The method comprises the following steps of firstly, collecting data based on an acceleration sensor and a gyroscope, using a Kalman filtering algorithm to realize multiple sensor data fusion, and acquiring estimation of a current driving road grade of a vehicle; then, based on a kinematic vehicle speed estimation method, estimating a vertical driving speed of the vehicle according to a grade estimation result and acceleration sensor data; and finally, eliminating an integral error caused by a noise in acceleration data, and correcting a vehicle speed estimation result according to each wheel speed feedback. In the invention, through combining with a road grade identification result, the vertical speedestimation is obtained by using a kinematic estimation method, and the vehicle speed estimation result is corrected according to each wheel speed feedback so that the vehicle speed estimation of thefull-drive vehicle can be realized. The method has versatility, high real-time performance and high calculating precision. Accurate vehicle state information can be provided for a vehicle electronic control system.

Description

technical field [0001] The invention belongs to the field of automobile research, and in particular relates to a method for estimating the longitudinal speed of an all-wheel drive vehicle. Background technique [0002] In recent years, the longitudinal vehicle speed signal is one of the important references for the multi-mode energy management control of the hybrid power system, and it is also the basis for the realization of the driving anti-skid system, brake anti-lock braking system and electronic stability system. In the control process of the all-wheel drive system, the estimation of the longitudinal vehicle speed plays a very important role. [0003] At present, the longitudinal speed estimation algorithms for all-wheel drive vehicles can be mainly divided into two categories: kinematic estimation methods and dynamic methods. Estimation methods based on kinematics mainly include: wheel speed method and direct integration method, among which, wheel speed method can be ...

AI Technical Summary

Problems solved by technology

It can be seen that the core of the dynamics-based estimation method lies in the observation of tire longitudinal force, road slope, and vehicle mass. Although this method has relatively low requirements for sensors, it has high sensitivity to model accuracy and model parameters, and is relatively robust. Low, and has higher requirements for estimation methods

Such as Chinese patent publication number CN106394561A, publication date 2017-02-15; Chinese patent publication number CN101655504, publication date 2010-02-24; estimation; these methods require complex tire models and vehicle models, and require more nonlinear calculations, which have more limitations in practical applications

[0004] Some patents, such as Chinese Patent Publication No. CN107016157A, published on 2017-08-04, the invention estimates the road surface peak adhesion coefficient and longitudinal vehicle speed estimate online based on the interconnection of the longitudinal force estimator and the road surface peak adhesion coefficient estimator, and completes the road surface adaptive longitudinal direction Vehicle speed estimation; however, this invention does not take into account the impact of the dynamic characteristics of the vehicle on the data collected by the sensor during actual operation, such as the acceleration process of the vehicle starting, the rapid deceleration process, the gear shifting process, etc.

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