The tire-road friction coefficient (TRFC) is an essential factor affecting driving stability and dynamic control of the automobile. However, when the car is starting or the road conditions change suddenly, the unscented Kalman (UKF) based TRFC observer using a nonlinear model converges slowly; under conditions of large lateral acceleration, the accuracy of the strong tracking Kalman (STKF) based TRFC observer using a linear model significantly decreases Therefore, a TRFC observation method based on the fusion of UKF and STKF is proposed. The control-oriented two-degree of freedom linear and three-degree-of-freedom nonlinear vehicle models, the Dugoff tire model are established. A STKF-TRFC observer is designed based on a linear vehicle model and a time-varying fading factor, a UKF-TRFC observer is established based on a nonlinear vehicle model and tire model. The critical point between linear and nonlinear tire lateral dynamics is explored, and the fading memory weighted least squares method is used to fuse the two observers in the critical region. The hardware-in-the-loop simulation results show that the proposed fusion observation method exhibits strong robustness and accuracy against changes in vehicle speed and road conditions. Its root mean square error drops by up to 59.2% and 56.9%, respectively, and its mean absolute error drops by up to 94.9% and 78.1% when compared to the single STRK-TRFC or UKF-TRFC observer.
