Estimation of Road Friction Coefficient and Vehicle States by 3-DOF Dynamic Model and HSRI Model Based on Information Fusion

Vehicle states and the road friction coefficient in active safety control systems have become increasingly prominent. However, a low-cost, high-precision system in real-time has yet to be achieved. The use of complex models has led to poor real-time estimation, while variations in the road friction coefficient have often been neglected. This paper adopts information fusion technology by using DEKF theory for rapid simulation and estimation of these parameters. Using a vehicle dynamic model based on three degrees of freedom (3-DOF) and the Highway Safety Research Institute tire model, DEKF recursive estimation models are established and verified. In the DEKF, two recursive state and parameter estimation models exist in parallel. The models are dependent upon each other and have real-time interaction correction in order to forecast information, which quickly yields true value estimation in simulation. Experimental brake test results show that the DEKF estimator not only accurately estimates the vehicle state parameters, but also estimates the road friction coefficient in real-time. This can reduce the cost of the vehicle sensor, and can estimate the status parameter, which is difficult to measure. The validity and feasibility of this algorithm have been verified by an HIL driving simulator, offering the possibility of future application in real cars.