Vibration suppression of hub motor electric vehicle considering unbalanced magnetic pull

控制理论(社会学) 电动汽车 均方根 振动 模型预测控制 人工神经网络 均方误差 磁铁 工程类 计算机科学 声学 数学 物理 控制(管理) 功率(物理) 机械工程 电气工程 机器学习 统计 人工智能 量子力学
作者
Zhongxing Li,Chenlai Liu,Xinyan Song,Chengchong Wang
出处
期刊:Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering [SAGE]
卷期号:235 (12): 3185-3198 被引量:9
标识
DOI:10.1177/09544070211004507
摘要

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.

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