Sound Quality Evaluation of Noise of Hub Permanent-Magnet Synchronous Motors for Electric Vehicles

Ma, Chunfeng; Li, Qing; Liu, Qinghe; Wu, Dafang; Gao, Jiajun; Tang, Haixiong; Sun, Yanhua

doi:10.1109/tie.2016.2569067

Cited by 35 publications

(24 citation statements)

References 12 publications

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“…The external rotor PMBLDCM studied in this paper is surface mounted, the permanent magnet field in polar coordinates is calculated by the equivalent remanence method and complex relative air-gap permeance, the radial magnetic flux density of permanent magnet considering the slotting effect B rm r, α, t can be expressed as [3,24,25]…”

Section: Analytical Calculation Of No-load Radial Magnetic Field Withmentioning

confidence: 99%

Effects of static eccentricity on the no‐load back electromotive force of external rotor permanent magnet brushless DC motor used as in‐wheel motor

Zheng

et al. 2019

IET Electric Power Applications

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The no-load radial magnetic field and no-load back electromotive force (EMF) of external rotor permanent magnet brushless DC motor (PMBLDCM) are calculated by applying the correction coefficient of magnetic conductance here, taking into account the stator slotting and static eccentricity effects. An external rotor PMBLDCM with 51-slot/46-pole, used as in-wheel motor, is taken as an example, the analytical calculation results of the no-load back EMF are validated by the finite-element method and experiment. The influences of static eccentricity ratio on the no-load radial magnetic field and no-load back EMF are investigated based on the analytical model. The investigation shows that static eccentricity does not change the harmonic contents of no-load radial magnetic field, so it does not change the harmonic contents of three-phase no-load back EMFs. However, static eccentricity changes the space order of no-load radial magnetic field, resulting in the different total harmonic distortions of three-phase no-load back EMFs; in other words, the asymmetric distortions of three-phase no-load back EMFs are generated. The asymmetric distortions of three-phase no-load back EMFs are intensified with the increase in static eccentricity ratio.

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Section: Analytical Calculation Of No-load Radial Magnetic Field Withmentioning

confidence: 99%

Effects of static eccentricity on the no‐load back electromotive force of external rotor permanent magnet brushless DC motor used as in‐wheel motor

Zheng

et al. 2019

IET Electric Power Applications

Self Cite

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“…Sharpness reflects the frequency characteristics of the sound. 17 The higher the proportion of high-frequency sound, the higher the sound sharpness will be. Sharpness also reveals the harsh degree of the sound signal.…”

Section: Sharpness Of Wind Buffeting Noisementioning

confidence: 99%

Sound quality evaluation of automobile side-window buffeting noise based on large-eddy simulation

Yin

Zong

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

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Large-eddy simulation (LES) and Detached-eddy simulation (DES) were applied to a simple cavity model to calculate the wind buffeting noise respectively. The results were verified by wind tunnel experiments. The results show that LES is more suitable for wind buffeting noise calculation. LES method was employed to calculate automobile side-window buffeting noise. The correctness of results was validated by a road test. In this paper, the acoustically calculated sound pressure level (SPL) spectral curve is used as the initial signal of the acoustic post-processing. Four psychoacoustic objective parameters namely: loudness, sharpness, roughness and fluctuation were obtained by using Matlab R2016a to compile the calculation process. Sound quality evaluation (SQE) of the vehicle is performed via most frequently used SPL and four calculated vehicle comfort index. It can be concluded that with the increase of driving velocity, SPL and loudness show an increasing trend, while roughness, sharpness and fluctuation present a decreasing trend. It can be also summarised that with the increase of window opening degree, SPL and loudness show an increasing trend, sharpness presents a decreasing trend, and roughness and fluctuation display the trend of ups and downs. The main original contribution of this paper is the accurate calculation of wind buffeting noise and the summary of the changing rule of SPL, loudness, roughness, sharpness and fluctuation with the variation of velocity and window opening degree.

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“…Meanwhile, it is possible to design an SQ estimation model by utilizing an ANN to simulate the subjective assessment model in an approximation process of the subjective perception. Many researchers have applied ANNs to SQ evaluation, and they have achieved reliable results [15][16][17][18][19][20]. Steinbach and Altinsoy [15] used ANNs to predict the annoyance evaluations of the pass-by noise of EVs and found that most of the parameters have no linear influence on the annoyance perception.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [19] proposed a method for predicting the SQ of vehicle interior noise using a BP-ANN that is based on particle swarm optimization (PSO). In addition, Ma et al [20] established an SQ evaluation model of hub-driven motors based on a BP-ANN, and they also proposed the limitations of the BP-ANN model. In order to improve the prediction accuracy, the structural parameters of BP-ANNs need to be optimized, but this process consumes a lot of time.…”

Section: Introductionmentioning

confidence: 99%

Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks

2020

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The sound quality (SQ) and sound perception assessments of electric vehicles (EVs) clearly differ from those of conventional internal combustion engine vehicles (ICEVs). Therefore, it is essential to describe and evaluate the SQ of EVs. To evaluate the SQ in EVs, it is necessary to organize evaluators for conducting subjective jury tests, which are time-consuming and labor-intensive. In addition, the evaluation results are subject to the evaluators themselves and other external interferences. With the advancement of machine learning and artificial neural networks (ANNs), this problem can be well solved. This paper outlines a model for SQ estimation in EVs based on a genetic algorithm-optimized back propagation artificial neural network (GA-BP ANN). Moreover, the correlation between the physical-psychoacoustical parameters and the subjective SQ estimations obtained from the jury tests was investigated in this study. It was found that the GA-BP ANN SQ model has many advantages in comparison with the multiple linear regression (MLR) model in terms of precision and generalization. In addition, this method is ready to be applied for rapidly evaluating the SQ in EVs without jury tests, and it can also be of high significance in dealing with the acoustical designs and improvements of EVs in the future.

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Sound Quality Evaluation of Noise of Hub Permanent-Magnet Synchronous Motors for Electric Vehicles

Cited by 35 publications

References 12 publications

Effects of static eccentricity on the no‐load back electromotive force of external rotor permanent magnet brushless DC motor used as in‐wheel motor

Effects of static eccentricity on the no‐load back electromotive force of external rotor permanent magnet brushless DC motor used as in‐wheel motor

Sound quality evaluation of automobile side-window buffeting noise based on large-eddy simulation

Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks

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