Estimation of driving state and tire-road friction coefficient of distributed drive electric vehicles based on sensorless control of PMSM

Abstract

To boost the precision of estimating the vehicle driving state (VDS) and tire-road friction coefficient (TRFC) in a non-Gaussian noise environment (NGNE) and reduce the dependence on wheel angular velocity (WAV) sensors, this research suggests a technique for estimating the VDS and TRFC through control without sensors of the permanent magnet synchronous motor (PMSM). Firstly, a three-degree-of-freedom (3-DOF) automobile dynamics model and the mathematical model of PMSM are formulated, leading to the derivation of maximum correntropy singular value decomposition cubature Kalman filter (MCSVDCKF). Subsequently, a sensor-less control system (SLCS) for PMSM is designed utilizing MCSVDCKF algorithm to precisely estimate the rotor velocity and location of PMSM. The rotor speed obtained from this system substitutes the WAV signal, thus validating its effectiveness. Ultimately, the practicality of our method for estimating VS and TRFC in a NGNE is verified via simulated tests. The RMSE values for sideslip angle and TRFC prediction outcomes derived from our MCSVDCKF method improved by 77.7% and 49.1%, respectively. This demonstrates greater exactness and increased sturdiness compared against existing methods such as maximum correntropy cubature Kalman filter (MCCKF), singular value decomposition cubature Kalman filter (SVDCKF), and cubature Kalman filter (CKF).