A Model Predictive Control Strategy with Minimum Model Error Kalman Filter Observer for HMEV-AS

In hub-motor electric vehicles (HMEVs), performance is adversely affected by the mechanical-electromagnetic coupling effect arising from deformations of the air gap in the Permanent Magnet Brushless Direct Current Motor (PM BLDC), which are exacerbated by varying road conditions. In this paper, a Model Predictive Control (MPC) strategy for HMEVs equipped with air suspension (AS) is introduced to enhance ride comfort. Firstly, an 18-degree of freedom (DOF) full-vehicle model incorporating unbalanced electromagnetic forces (UEMFs) induced by motor eccentricities is developed and experimentally validated. Additionally, a Minimum Model Error Extended Kalman Filter (MME-EKF) observer is designed to estimate unmeasurable state variables and account for errors resulting from sprung mass variations. To further improve vehicle performance, the MPC optimization objective is formulated by considering the suspension damping force and dynamic displacement constraints, solving for the optimal suspension force within a rolling time domain. Simulation results demonstrate that the proposed MPC approach significantly improves ride comfort, effectively mitigates coupling effects in hub driving motors, and ensures that suspension dynamic stroke adheres to safety criteria. Comparative analyses indicate that the MPC controller outperforms conventional PID control, achieving substantial reductions of approximately 41.59% in sprung mass vertical acceleration, 14.29% in motor eccentricity, 1.78% in tire dynamic load, 17.65% in roll angular acceleration, and 16.67% in pitch angular acceleration.