Receding Horizon Nonlinear Adaptive Observer for Tire-road Friction Coefficient Estimation based on Intelligent Distributed Drive Electric Vehicles

The tire-road friction coefficient (TRFC) is one of the most important parameters for intelligent distributed drive electric vehicles. The real-time performance of its estimation greatly affects motion control and trajectory planning. Traditional methods based on vehicle dynamics often only use current information for TRFC estimation. Due to measurement noises and insufficient driving excitation, convergent speed of estimation results cannot be set arbitrary fast. To this end, this paper proposes a TRFC estimator based on vehicle longitudinal dynamics which introduces previous information to improve the estimation convergent speed. Based on the previous wheel speed, slip rate as well as driving/braking torque information, this method designs a nonlinear adaptive observer that simultaneously estimates the historical tire longitudinal force and the current road friction coefficient in a receding horizon way. Through various simulations based on Carsim and Simulink, the results of the proposed estimation method is better than those from traditional method which only utilize current information in terms of estimation convergence speed.