关键词: 分布式驱动电动汽车；轮胎-路面附着系数估计；滚动时域；自适应观测器

Abstract: The estimation of tire-road friction coefficient (TRFC) is one of the most important parameters in the precise motion control of intelligent distributed drive electric vehicles, and its estimation accuracy and real-time performance greatly affect the vehicle control effect. Traditional methods based on vehicle dynamics often only use current information for TRFC estimation. Due to model uncertainties and insufficient driving excitation, the estimation results often suffer from lowaccuracy and slow convergence, which greatly affects the control effect of subsequent vehicle chassis control systems. To address this issue, this paper proposes a TRFC observer that introduces past information based on vehicle lateral dynamics, thereby improving estimation accuracy and convergence speed. This method is based on historical vehicle longitudinal/lateral speed, lateral acceleration, yaw rate, and front wheel angle information, and designs a nonlinear adaptive observer to simultaneously estimate historical tire lateral force and current TRFC. The accuracy and convergence speed of this method are verified through real vehicle datasets, confirming its feasibility in engineering applications. The proposed method provides an accurate parameter basis for the subsequent intelligent vehicle chassis control.

Key words: distributed drive electric vehicles; tire-road friction coefficient estimation; receding horizon; adaptive observer