The Estimation of Tire Lateral Stiffness for Distributed Drive Electric Vehicle Based on PMSM Sensorless Control

Accurate acquisition of the tire lateral stiffness is crucial for enhancing the dynamic control performance and maneuverability of distributed drive electric vehicles (DDEVs). To address this, we propose a method for estimating tire lateral stiffness in DDEV using permanent magnet synchronous motor (PMSM) sensorless control. First, to overcome the challenge of obtaining the speed and rotor position of the PMSMs in DDEV, we design the square root seventh-degree cubature quadrature Kalman filter with maximum correntropy (MC-SR7thCQKF) algorithm, the algorithm is based on the seventh order orthogonal spherical radius criterion and introduces QR decomposition, it ensures stability during the filtering process and provides high estimation accuracy. Next, the vehicle state parameters required for tire lateral stiffness estimation are derived using the MC-SR7thCQKF algorithm. Finally, we use the estimated vehicle state parameters as the inputs, the forgetting factor recursive least squares (FFRLS) is designed for estimating the tire lateral stiffness. Simulation and experimental results confirm that the proposed hierarchical estimation algorithms achieve high accuracy.