Topology Optimization of Rear Cover in EV Motor and Noise Attenuation

2022-01-7005

02/14/2022

Features
Event
Vehicle Electrification and Powertrain Diversification Technology Forum Part II
Authors Abstract
Content
It is becoming an increasingly important issue to improve NVH performance of electric drive motor for electric vehicle as the market grows rapidly. The correlation between stiffness of rear cover of motor, rotor eccentricity and noise of an electric drive is discussed in this paper which was few mentioned before. Poor stiffness of bearing chamber of rear cover may cause rotor eccentricity, which would lead to additional orders of electromagnetic noise. Stiffness optimization model of rear cover of motor was established, and the Optistruct of Hyper works software was used to improve stiffness as well as mode frequency by designing circular and radial ribs to surround bearing chamber of rear cover under guidance of topology. As compared to basis model with same mass, the 1st and 2nd strict mode frequencies of optimized rear cover separately increased by 11% and 12.5% with numerical simulations. While average dynamic stiffness in x and z direction respectively increased by 14.3% and 11.2%, and static stiffness of bearing chamber of optimized rear cover in +x and -z directions increased by 41.98% and 38.9%. Besides, compared results of NVH experiments in vehicle with two prototypes showed that optimized rear cover could reduce vibration on itself and assembly, and significantly reduce the 3rd, 4th, and 5th motor noise in front compartment, as well as in passenger cabin. As showed in the order slices of noise color map in front compartment, the amplitude of 3rd, 4th, and 5th orders of noise reduced about 5dB~10dB.
Meta TagsDetails
DOI
https://doi.org/10.4271/2022-01-7005
Pages
13
Citation
Zhang, Y., Lu, X., Ma, T., and Lin, Z., "Topology Optimization of Rear Cover in EV Motor and Noise Attenuation," SAE Technical Paper 2022-01-7005, 2022, https://doi.org/10.4271/2022-01-7005.
Additional Details
Publisher
Published
Feb 14, 2022
Product Code
2022-01-7005
Content Type
Technical Paper
Language
English