Design of a distributed hybrid model predictive controller based on long short-term memory networks model for hub-motor electric vehicle with air suspension
Restricted accessResearch articleFirst published online 2025
Design of a distributed hybrid model predictive controller based on long short-term memory networks model for hub-motor electric vehicle with air suspension
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%.
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