In hub-motor electric vehicles (HM-EVs), the unbalanced electromagnetic force generated by the HM will further deteriorate the dynamic performance of the electric vehicle. In this paper, a semiactive suspension control method is proposed for HM-EVs. A quarter HM-EV model with an electromechanical coupling effect is established.The model consists of three parts: a motor model, road excitation model and vehicle model. A hybrid model predictive controller (HMPC) is designed based on the developed model, taking into account the nonlinear constraints of damping force. The focus is on improving the vertical performance of the HM-EV. Then, a Kalman filter is designed to provide the required state variables for the controller. The proposed control algorithm and constrained optimal control (COC) algorithm are simulation compared under random road excitation and bump road excitation, and the results show that the proposed control algorithm can improve ride comfort, reduce motor vibration, and improve handling stability more substantially.

For the hub motor electric vehicle (HM-EV), the drive motor is directly integrated with the wheel. The unbalanced magnetic pull (UMP) of hub motor would be generated by magnet gap deformation under road surface roughness excitation. The longitudinal and vertical dynamic performances of the HM-EV system are therefore deteriorated. Firstly, to analyze and optimize the longitudinal and vertical dynamic performance of the HM-EV system, a new ten-degree-of-freedom mathematical quarter HM-EV system model equipped with air suspension model, permanent magnet brushless direct current (PM BLDC) hub motor model and rigid ring tire model is proposed. The UMP of PM BLDC hub motor is taken into consideration in this model. A HM-EV system model validation test bench is constructed. The accuracy of the model is verified by experiment. Secondly, based on quarter HM-EV system model, the BP neural network is adopted to calculate the longitudinal and vertical UMP. The relative error between results calculated by BP neural networks and electromagnetic formula is less than 5% and root-mean-square error (RMSE) is less than 2. With proposed BP neural networks calculation method, UMP calculation time is shortened by 70.3%. Finally, the adjustable force is introduced and model predictive control (MPC) method is used to suppress the longitudinal and vertical vibration of HMEV system. Two control methods, namely model predictive control (MPC) and constrained optimal control (COC) are proposed. The simulation results show that by applying MPC, the RMS value of evaluation indexes are decreased by 17.21%–44.10% respectively, which is better than COC (−14.42%–17.21%). With MPC, longitudinal and vertical vibration are suppressed. Comparison of two UMP calculation methods with MPC controller is conducted. The relative errors of evaluation indexes are within 3.85%. Therefore, the driving safety and riding comfort of the HM-EV are improved compared to the passive suspension and COC active suspension.