EKF based Road Friction Coefficient Estimation and Experimental Verification 2019-01-0176
Many active chassis control systems in the past few decades have been widely implemented on production vehicles to improve vehicle stability and safety performance. In the implementation, the performance of current vehicle active control systems heavily relies on accurate road friction information. However, road friction coefficient is difficult to be measured directly. In this paper, Using the available onboard sensors, a model-based Extended Kalman filter (EKF) algorithm is proposed in this paper to estimate road friction coefficient. 3DOF single track model is introduced to estimate vehicle motion states. Four-wheel nonlinear vehicle model along with Dugoff tire model is present for the estimation of road friction coefficient. In the development of estimation algorithm, vehicle motion states such as sideslip angle, yaw rate and vehicle speed are first estimated. Then, road friction coefficient estimator is designed using nonlinear vehicle model together with the pre-estimated vehicle motion states. To verify the effectiveness of proposed EKF estimation algorithm on different road surfaces, simulations were conducted under three different road conditions: high-friction road, low-friction road, and joint road in this section. The results from estimation are also compared with the experimental values from scaled model vehicle for each case.
Bin Li, Tao sun, Arlene Fang PhD, Guobiao Song
APTIV PLC, Univ. of Shanghai for Science & Technolo, Ford Motor Company