Adaptive distributed explicit model predictive controller with road surface identification for HM-AS

The rapid advancement of hub-motor electric vehicle (HMEV) is propelled by its capacity to significantly improve energy efficiency, handling dynamics, and space utilization while minimizing mechanical losses and maintenance costs. A significant challenge in HMEV is mitigating the performance degradation caused by unbalanced electromagnetic force (UEMF), which result from the interaction between the hub motor and road-induced vibrations. This study introduces an Adaptive Distributed Explicit Model Predictive Control (ADEMPC) strategy for hub-motor electric vehicles equipped with air suspension (HM-AS), aiming to enhance ride comfort, handling stability, and reduce eccentricity between the stator and rotor. A full-vehicle dynamic model considering vertical-longitudinal coupling is established and validated. A road surface identification system based on a BP neural network is designed. The Whale Optimization Algorithm (WOA) is used to optimize weight coefficients on 16 conditions, which are then saved as tables for ADEMPC. An ADEMPC controller is designed based on distributed prediction model, which decompose the entire vehicle into four subsystems and consider the coupling of roll and pitch. Simulation results demonstrated that ADEMPC achieved improvements of 24.44% in body acceleration, 21.43% in eccentricity, 14% in tire dynamic load, 25% in roll, and 21.74% in pitch. It showcases its effectiveness in enhancing ride comfort and vehicle stability.