Analysis of vibration and noise for the powertrain system of electric vehicles under speed-varying operating conditions

被引:0
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作者
Deng, Chenghao [1 ,2 ]
Deng, Qingpeng [2 ]
Liu, Weiguo [2 ]
Yu, Cheng [2 ]
Hu, Jianjun [1 ]
Li, Xiaofeng [3 ]
机构
[1] School of Automotive Engineering, Chongqing University, Chongqing,400040, China
[2] Chongqing Chang’an New Energy Automobile Technology Co. Ltd., Chongqing,401120, China
[3] Chongqing Deyin Technology Co. Ltd., Chongqing,400050, China
关键词
Sailing vessels - Vibration analysis - Boundary element method - Electromagnetic fields - Traction motors - Acoustic noise;
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摘要
Whine noise from the electric powertrain system of electric vehicles, including electromagnetic noise and gear-meshing noise, significantly affects vehicle comfort and has been getting growing concern. In order to identify and avoid whine problems as early as possible in the powertrain development process, this paper presents a vibration and noise simulation methodology for the electric powertrain system of vehicles under speed-varying operating conditions. The electromagnetic forces on the stator teeth of the motor and the bearing forces on the gearbox for several constant-speed operating conditions are obtained first by electromagnetic field simulation and multi-body dynamic simulation, respectively. Order forces for the speed-varying operating condition are generated by interpolation between the obtained forces, before they are applied on the mechanical model whose natural modes have been calibrated in advance by tested modes. The whine noise radiated from the powertrain is then obtained based on acoustic boundary element analysis. The simulated bearing forces indicate that the overlooking of the motor torque ripple does not result in significant loss in simulation accuracy of electromagnetic noise. The simulation results and tested data show good consistency, with the relative frequency deviation of local peaks being less than 8% and the error of the average sound pressure level (SPL) being mostly below 10 dB (A). Copyright © 2020 Chenghao Deng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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