Estimation for Tire–Road Adhesion Coefficient Based on PMSM Sensorless Control for Distributed Drive Electric Vehicle

Accurate estimation of the tire-road adhesion coefficient (TRAC) is important for the vehicle active safety control system to make corresponding control decisions under different driving conditions and road surface conditions. This article, therefore, proposes a TRAC estimation method for distributed drive electric vehicle (DDEV) based on permanent magnet synchronous motor (PMSM) sensorless control. First, for improving the speed control accuracy of PMSM, this article combines sliding mode control (SMC) with active disturbance rejection control (ADRC), and the speed control method for PMSM based on new reaching SMC-ADRC (NRSMC-ADRC) is designed. Second, the seventh-degree cubature Kalman filter (KF) (7thCKF) algorithm is derived based on the seventh-order spherical-radius criterion, and the QR decomposition and memory attenuation (MA) filter are introduced to design the square root seventh-degree cubature Kalman filter with MA (MA-SR7thCKF) algorithm, which ensures the semipositive character of the filtering and solves the problem of degradation of the filtering accuracy due to the excessive use of historical data. The MA-SR7thCKF algorithm is used to estimate the speed of PMSM and vehicle state parameters to reduce the use of sensors and the complexity of the dynamic system. Finally, the estimated vehicle state parameters are used as inputs to the Dugoff model for the calculation of the normalized tire force, and the TRAC is estimated using the MA-SR7thCKF algorithm. The effectiveness of the proposed method is verified under simulation and experiment, the results show that the proposed method has high estimation accuracy.