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.

对于轮毂电机电动汽车（HM-EV），驱动电机直接与车轮集成在一起。轮毂电机的不平衡磁拉力（UMP）将由路面粗糙度激励下的磁隙变形产生。因此，HM-EV系统的纵向和垂直动态性能下降。首先，为了分析和优化HM-EV系统的纵向和垂直动态性能，新的配备了空气悬浮模型，永磁无刷直流电（PM BLDC）的十自由度四分之一四分之一HM-EV系统模型提出了轮毂电机模型和刚性环轮胎模型。该模型考虑了PM BLDC轮毂电机的UMP。构建了HM-EV系统模型验证测试台。通过实验验证了模型的准确性。第二，基于四分之一的HM-EV系统模型，采用BP神经网络计算纵向和纵向UMP。BP神经网络和电磁公式计算的结果之间的相对误差小于5％，均方根误差（RMSE）小于2。使用所提出的BP神经网络计算方法，UMP计算时间缩短了70.3％。最后，介绍了可调力，并采用模型预测控制（MPC）方法抑制了HMEV系统的纵向和纵向振动。提出了两种控制方法，即模型预测控制（MPC）和约束最优控制（COC）。仿真结果表明，应用MPC可使评估指标的RMS值分别降低17.21％–44.10％，优于COC（−14.42％–17.21％）。使用MPC，纵向和垂直振动得到抑制。用MPC控制器比较了两种UMP计算方法。评价指标的相对误差在3.85％以内。因此，与被动悬架和COC主动悬架相比，HM-EV的行驶安全性和乘坐舒适性得到了改善。