Estimation of vehicle states and road friction based on DEKF

Abstract

On vehicle active safety control systems, various types of state parameters of vehicle and road are needed to be estimated. This paper adopts information fusion technology, using of dual extended Kalman filter (DEKF) theory for rapid simulation and estimation of these parameters. Using different vehicles model and tire model, DEKF recursive estimation models are established and verified. Experimental results show that DEKF is designed by vehicle dynamic model based on three degrees of freedom(3-DOF), using the Highway Safety Research Institute tire model, not only accurately estimates the vehicle state parameters, but also estimates the road tire friction coefficient in real-time. In the DEKF, two recursive state and parameter estimation model exist in parallel, while they are dependent on each other, and have real-time interaction correction to forecast information, which quickly converges towards estimated true value for simulation. The accurate estimation of DEKF theory in the vehicle state and road information, make it possible that some of the parameters can be estimated, which is proved to be difficult to obtain, and it also provides the necessary conditions for the vehicle active control system. In the meantime, the validity and feasibility of this algorithm have been verified by CarSim and HIL driving simulator, and offers the possibility of application in real car in future.