The suspension system is a significant part of a vehicle because it transmits the torque and force between the wheels and the frame, meeting the requirements of ride comfort. In this paper, a novel interconnected air suspension was introduced and a cooperative control between interconnected mode activation (IMA) and outsourced mode activation (OMA) was designed. To improve ride comfort in a bus at a minimal energy consumption, this cooperative control based on model predictive control (MPC) for computing the best force and the rule was defined to distribute the best suspension force generated by IMA and OMA. The simulation and experimental results showed that the proposed control strategy significantly improved ride comfort in different conditions. Compared with traditional control in the test vehicle, the RMS of the front-left sprung mass and unsprung vertical acceleration decreased by 24.2% and 14.5%, respectively, under a straight condition; the RMS of the sprung (unsprung) mass vertical acceleration reduced by 22.38% and 15.43%, respectively, under a bump condition.

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.

The vigorous development of hub-motor electric vehicles (HMEVs) is driven by their potential to significantly enhance energy efficiency, handling performance and space utilization, as well as reduce mechanical losses and maintenance costs. The unbalanced electromagnetic force (UEMF) is generated by the combined effect of the hub-motor and the road coupling excitation, which is a major challenge to reduce performance deterioration in HMEVs. A Distributed Hybrid Model Predictive Control (DHMPC) strategy is introduced in this paper for HMEVs equipped with air suspension (AS) to improve vehicle ride comfort and handling stability. UEMF is calculated based on analytic method and optimized by LSTM. An 11-DOF full-vehicle is established by distributive method and verified by vehicle test. Based on LSTM online training and dynamic modelling, a DHMPC controller integrates nonlinear damping force constraints with logical variables. An MLD model is used in the approach to uniformly represent the dynamic model and system constraints. The numerical simulation results indicate that DHMPC significantly improved various evaluation indicators compared to DMPC controller. The Root Mean Square values of vehicle body vertical acceleration, pitch angular acceleration and roll angular acceleration improve by 3.9%, 1.0% and 1.3%, respectively. The eccentricity of the left-rear hub-motor improve by 0.8%.