Analysis and control of 2f noise in an ultra-high-speed permanent magnet synchronous motor

Guoping  Feng; Ruihong  Jing; Jing  Yao; Jianjun  Zhao; Dongya  Li; Wenjian  Zhou

doi:10.59400/sv3701

Analysis and control of 2f noise in an ultra-high-speed permanent magnet synchronous motor

Guoping Feng
School of Intelligent Manufacturing, Suzhou Chien-Shiung Institute of Technology, Suzhou 215000, China
Ruihong Jing
School of Intelligent Manufacturing, Suzhou Chien-Shiung Institute of Technology, Suzhou 215000, China
Jing Yao
State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process, Beijing 100089, China
Jianjun Zhao
State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process, Beijing 100089, China
Dongya Li
Applied Technology College, Soochow University, Suzhou 215000, China
Wenjian Zhou
College of Power and Energy Engineering, Harbin University of Science and Technology, Harbin 150006, China

Article ID: 3701

DOI: https://doi.org/10.59400/sv3701

Keywords: ultra-high-speed, permanent magnet synchronous motor, unbalanced magnetic pull, noise, assembly gap

Abstract

Ultra-high-speed permanent magnet synchronous motors (PMSMs) have been widely applied in aerospace, advanced manufacturing, and consumer products due to their small size, high reliability, and high-power density. As the rotational speed increases, losses, cooling requirements, excitation forces, bearing losses, and noise characteristics of the motor are significantly different from those of conventional motors. To minimize iron losses and improve efficiency, ultra-high-speed motors typically employ a low pole count configuration, exemplified by the prevalent 2-pole 3-slot (2P3S) design. In 2-pole machines, low-order electromagnetic excitation forces may cause significant electromagnetic vibration and noise. Reducing electromagnetic noise, especially the two-times mechanical rotational frequency (2f) noise in 2P3S motors, is critical. Therefore, the noise reduction strategies for a 120,000 rpm PMSM are investigated. First, the electromagnetic excitation frequencies and magnitudes of the motor are analyzed. Through changing the pole-slot configuration (such as transitioning from 2P3S to 2P6S), the unbalanced magnetic pull (UMP) in the original 2P3S design is eliminated, significantly reducing the main source of vibration. Then, the impact of the stator assembly gap on electromagnetic excitation is examined using a segmented stator structure. Effective control of this assembly gap further reduces the electromagnetic noise of the motor. Finally, experimental tests demonstrate a significant noise reduction in the 2f frequency band, with a sound pressure level reduction of about 9 dB. The reduction validates the effectiveness of this pole-slot optimization and stator assembly gap control strategies.

Published

2025-10-28

How to Cite

Feng, G., Jing, R., Yao, J., Zhao, J., Li, D., & Zhou, W. (2025). Analysis and control of 2f noise in an ultra-high-speed permanent magnet synchronous motor. Sound & Vibration, 59(5). https://doi.org/10.59400/sv3701

Download Citation

Issue

Vol. 59 No. 5 (2025)

Section

Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Gao QX, Wang XL, Zhang Y. Multi-physical-field characteristics modeling and structure optimization for kW-level ultra-high speed PM motors with integrated support system. Chinese Journal of Aeronautics. 2023; 36(4): 455–467.

[2]Wang DH, Xu S, Li Y, et al. Sensorless control impact on electromagnetic vibrations and acoustic noises in interior permanent magnet synchronous machines. IEEE Transactions on Transportation Electrification. 2024; 11(1): 416–425.

[3]Liu CC, Zhang HM, Wang SH, et al. Multiphysical design and optimization of High-Speed Permanent Magnet Synchronous Motor with sinusoidal segmented permanent magnet structure. Journal of Electrical Engineering & Technology. 2023; 19: 1459–1473.

[4]Peng C, He JX, Zhu MT, et al. Optimal synchronous vibration control for magnetically suspended centrifugal compressor. Mechanical Systems and Signal Processing. 2019; 132: 776–789.

[5]Yi FY, Su QQ, Feng CX, et al. Response analysis and stator optimization of Ultrahigh-Speed PMSM for fuel cell electric air compressor. IEEE Transactions on Transportation Electrification. 2023; 9: 5098–5110.

[6]Bianchi N, Bolognani S, Pré M. Design techniques for reducing the cogging torque in surface-mounted PM motors. IEEE Transactions on Industry Applications. 2002; 38(5): 1259–1265.

[7]Zhu ZQ, Howe D. Influence of design parameters on cogging torque in permanent magnet machines. IEEE Transactions on Energy Conversion. 2000; 15(4): 407–412.

[8]Jahns TM, Soong WL. Pulsating torque minimization techniques for permanent magnet AC motor drives—A review. IEEE Transactions on Industrial Electronics. 1996; 43(2): 321–330.

[9]Islam MS, Mir S, Sebastian T. Issues in reducing the cogging torque of mass-produced permanent-magnet brushless DC motor. In: Proceedings of the 38th IAS Annual Meeting on Conference Record of the Industry Applications Conference; 12–16 October 2003; Salt Lake City, UT, USA. pp. 1356–1363.

[10]Wang A, Jia Y, Soong WL. Comparison of five topologies for an interior permanent-magnet machine for a hybrid electric vehicle. IEEE Transactions on Magnetics. 2011; 47(10): 3606–3609.

[11]Anuja TA, Doss MAN. Reduction of cogging torque in surface mounted permanent magnet brushless DC motor by adapting rotor magnetic displacement. Energies. 2021; 14(10): 2861.

[12]Huang PC, Ma JE, Zheng GL, et al. Characteristic analysis and control study of aerodynamic noise in Self-Ventilated traction motors. Electric Machines & Control Application. 2024; 51(10): 1–7. (in Chinese).

[13]Feng GP, Zhou WJ, Li DY, et al. Analysis and Control of Rotating Frequency Noise Induced by High-speed Rotation of Fan Machines. Noise and Vibration Control. 2024; 44(6): 309–314. (in Chinese)

[14]Jamali Arand S, Ardebili M. Cogging torque reduction in axial-flux permanent magnet wind generators with yokeless and segmented armature by radially segmented and peripherally shifted magnet pieces. Renewable Energy. 2016; 99: 95–106.

[15]Ilka R, Alinejad-Beromi Y, Yaghobi H. Cogging torque reduction of permanent magnet synchronous motor using multi-objective optimization. Mathematics and Computers in Simulation, 2018; 153: 83–95.

[16]Lin F, Zuo SG, Deng WZ, et al. Reduction of vibration and acoustic noise in permanent magnet synchronous motor by optimizing magnetic forces. Journal of Sound and Vibration. 2018; 429: 193–205.

[17]Wang SM, Hong JF, Sun YG, et al. Analysis of Zeroth Mode Slot Frequency Vibration of Integer Slot Permanent Magnet Synchronous Motors. IEEE Transactions on Industrial Electronics. 2019; 67(4): 2954–2964.

[18]Yang ZG, Li W, Gou YN, et al. Research on Radial Force of Permanent Magnet Synchronous Motor Based on Maxwell. Journal of Electrical Engineering & Technology. 2020; 15: 2601–2608.

[19]Ge HR, Qiu X, Guo BC, et al. Optimized Rotor Shape for Reducing Torque Ripple and Electromagnetic Noise. IEEE Transactions on Magnetics. 2021; 58(2): 8102905.

[20]Wu ZP, Zuo SG, Huang ZY, et al. Modelling, calculation and analysis of electromagnetic force and vibroacoustic behavior of integer-slot permanent magnet synchronous motor considering current harmonics. Journal of Vibration Engineering & Technologies. 2022; 10: 1135–1152.

[21]Chen Y, Zhu ZQ, Howe D. Vibration of PM brushless machines having a fractional number of slots per pole. In: Proceedings of the 2006 IEEE International Magnetics Conference (INTERMAG); 8–12 May 2006; San Diego, CA, USA.

[22]Petkovska L, Lefley P, Cvetkovski LV. Design techniques for cogging torque reduction in a fractional-slot PMBLDC motor. COMPEL. 2020; 39(5): 1041–1055.

[23]Zhou JY, Xiao X. Combined methods for torque variation reduction of high-speed FSPM. Energy Reports. 2023; 9 (8): 199–206.

[24]Xing ZZ, Wang XH, Zhao WL. Optimization of stator slot parameters for electromagnetic vibration reduction of permanent magnet synchronous motors. IEEE Transactions on Transportation Electrification. 2022; 8(4): 4337–4347.

[25]Wang XQ, Wu M, Zhao PP. Dielectric identification method and system design of coal gangue based on frequency shift characteristics. Scientific Reports. 2025; 15: 8712.

[26]Chen YY, Xiao Y, Ji C, et al. An Online Torque Prediction Method of PMSM Using PINNs. IEEE Access. 2025; 13: 196796–196808.

[27]Feng GP, Chen Y, Huang XC, et al. Study on transmission paths in submarine stern excited longitudinally. Noise and Vibration Control. 2009; 29(6): 132–135. (in Chinese)

[28]Zhu ZQ, Ishak D, Howe D, et al. Unbalanced magnetic forces in permanent-magnet brushless machines with diametrically asymmetric phase windings. IEEE Transactions on Industry Applications. 2007; 43(6): 1544–1553.

[29]Chen YX, Zhu ZQ, Ying SC. Electric Machine Noise Analysis and Control. Zhejiang University Press; 1987. (in Chinese)

This paper introduces a universal framework for understanding the vibration responses of systems subjected to harmonic excitation. By examining a simplified cylinder-spring-damper model, the study refurbishes traditional scaling methods for the excitation frequency ratio and critical damping ratio. The findings indicate that in damped systems, the maximum amplitude of vibration does not align with the natural frequency. This observation leads to the introduction of a new scaling method for reduced frequency. This new approach aligns resonance peaks at the new reduced velocity of 1.0 across different damping ratios, providing a consistent characterization of vibration behavior. A new critical damping ratio of 0.707 is identified for an excited system as opposed to the traditional damping ratio of 1.0 for an unexcited system. Key properties such as maximum amplitude, phase lag, bandwidth, and quality factor are analyzed, demonstrating that the proposed reduced frequency and critical damping ratio effectively capture the dynamics of both damped and undamped excited systems. The findings offer significant insights for practical applications in engineering and various scientific fields.

This study makes significant contributions to the field of ultrasonic testing (UT) by offering a novel approach to the identification of artificially introduced defects within Japanese cedar wood (Cryptomeria japonica). The findings are of particular relevance for the heritage conservation and construction sectors, where non-invasive defect detection is paramount. The study establishes a robust framework for assessing the structural integrity of timber by correlating ultrasonic wave velocity reductions with defect size and distribution. Big-sized defects led to more substantial decreases in wave velocity. The study establishes a robust framework for assessing the structural integrity of historical timber by correlating ultrasonic wave velocity reductions with defect size and distribution. This framework has the potential to be applicable to diverse wood species and defect types.

The control of vehicle interior noise has become a critical metric for assessing noise, vibration, and harshness (NVH) in vehicles. During the initial phases of vehicle development, accurately predicting the impact of road noise on interior noise is essential for reducing noise levels and expediting the product development cycle. In recent years, data-driven methods based on machine learning have gained significant attention due to their robust capability in navigating complex data mapping relationships. Notably, surrogate models have demonstrated exceptional performance in this domain. Numerous researchers have integrated diverse intelligent algorithms into the study of vehicle noise, leveraging advantages such as the elimination of precise modeling requirements, extensive solution space exploration, continuous learning from data, and robust algorithmic versatility. However, in NVH engineering applications, data-driven models face inherent limitations, particularly in interpretability and stability. To address these issues, this paper introduces an improved Long Short-Term Memory (LSTM) network that combines knowledge and data. Inspired by the physical information neural network concept, this approach incorporates values calculated through empirical formulas into the neural network as constraints. Comparative assessments with traditional LSTM networks highlight the advantages of this deep learning model. By integrating empirical formulas constraints, the model not only enhances interpretability but also achieves robust generalization with fewer data samples. The proposed method is validated on a specific vehicle model, showing significant improvements in prediction accuracy and efficiency.