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By measuring the open-circuit radial stray magnetic flux density (RSMFD) of three spatial points, a novel diagnostic method for the eccentricity of interior permanent magnetic motors (IPMSMs) in electric vehicles is proposed. Outperforming traditional methods, the proposed method can noninvasively diagnose not only the severities of static eccentricity (SE), dynamic eccentricity (DE), and mixed eccentricity (ME), but also the eccentricity location. In this article, diagnosis results are described through three eccentricity parameters: SE ratio, DE ratio, and SE angle. First, an analytical model of the open-circuit RSMFD of the IPMSM is proposed, with its accuracy validated by experiments. The pattern of the impact of the eccentricity parameters on the open-circuit RSMFD is analyzed to form the basis of the proposed method. Then, an eccentricity prototype motor, which can continuously emulate the eccentricity according to any listed eccentricity parameters, is developed. A test rig is built to collect the open-circuit RSMFD and validate the analytical calculation results. Finally, an eccentricity diagnostic model based on the open-circuit RSMFD is established. The diagnosis results proved the noninvasiveness, effectiveness, and viability of the proposed method. [ABSTRACT FROM AUTHOR]

Based on neural network model, a method for quantitative sound quality (SQ) evaluation of the interior noise of a pure electric vehicle (PEV) is presented in this paper. The method can be divided into four steps. First, the interior noises under different speeds of a PEV are collected through the interior noise test of the PEV. Subsequently, one physical acoustic parameter (A-weighted sound pressure level) and six psychoacoustic parameters (loudness, fluctuation strength, tonality, roughness, articulation index, and sharpness) are applied to describe the noise samples for objective evaluation of SQ. In the third step, five semantic evaluation indexes, namely, “annoying or pleasing,” “harsh or sweet,” “weak or powerful,” “promiscuous and pure,” and “unobservable or perceptible,” are proposed based on semantic differential method, which are used for subjective evaluation of SQ by jury tests. Finally, the neural network model for SQ evaluation of the interior noise of the PEV is established, the SQ characteristics of the interior noise of the PEV are evaluated, as well as revealing the coefficient weight of influencing factors. This model can be used for SQ prediction and evaluation of the interior noise of the PEV considering that the average error is 9%. [ABSTRACT FROM AUTHOR]

A novel sound quality (SQ) evaluation method is developed to diagnose abnormal noise of interior permanent-magnet synchronous motors (IPMSM) for electric vehicle (EV). A-weighted sound pressure level (A-W SPL) of noise in IPMSM and six psychoacoustic characteristics, which include sharpness, roughness, loudness, articulation index, tonality, and fluctuation strength, are described through the objective evaluation of SQ. The paired comparison method (PCM) and the grade evaluation method (GEM) are respectively applied in subjective evaluation on relative annoyance and absolute annoyance of IPMSM for EV, and the combined application of these two methods is proposed originally. Finally, the BP neural network model for SQ evaluation of abnormal noise in IPMSM is established, the characteristics, conditions, and influencing factors of abnormal noise are effectively diagnosed. This novel evaluation method effectively solves the problem that the annoyance level is unable to be evaluated through the PCM, and the GEM relies on rich audition experience of evaluation subject. It also improves the accuracy of diagnosis of abnormal noise in IPMSM for EV. [ABSTRACT FROM PUBLISHER]

The existing evaluation methods and indexes of noise of electric motors cannot comprehensively reflect all the physical characteristics of noise of hub permanent-magnet synchronous motors (HPMSM) and human subjective sensations. In this paper, a method of the sound quality (SQ) evaluation of HPMSM for electric vehicles is proposed. The method is divided into three steps. In the first step, a noise objective evaluation of HPMSM with seven acoustical objective indexes, including loudness, roughness, A-weighted sound pressure level (A-W SPL), tonality, sharpness, articulation index (AI), and fluctuation strength, is made. In the second step, a noise subjective evaluation of HPMSM by using the grade evaluation method is made. The subjective annoyances (SA) of noise samples of HPMSM are obtained. In the last step, the SQ evaluation model of HPMSM by using the BP neural network theory is established. The average error rate of the proposed model is only 3.97%. By means of weight analysis, a comparison between the relative importance of acoustical objective parameters is found out. The main original contribution of this paper is that the proposed method can comprehensively reflect all the physical sound characteristics of HPMSM as well as their influence on human subjective sensations. [ABSTRACT FROM PUBLISHER]

This paper presents a novel black and white box method for diagnosis and reduction of abnormal noise of the hub permanent-magnet synchronous motors (HPMSMs). The black and white box method is divided into three steps. In the first step, a black-box method is used to identify and diagnose all of the abnormal noise sources and corresponding working conditions including rotational speeds and loading moments of the HPMSM. The proposed black method is independent of the detailed internal parameters of the HPMSM. In the second step, considering the abnormal noise sources and the corresponding working conditions diagnosed by the black-box method, a white-box method is used to propose and verify the electromagnetic field model, the structure vibration model, and the acoustic computation model of the HPMSM. Then, the components of the HPMSM whose vibrations and/or acoustic emissions are the most important are predicted. In the third step, the noise reduction method, which is aimed at the diagnosed abnormal noise source under the corresponding working conditions and the diagnosed components, is proposed. The proposed method in this paper observably raises the efficiency of diagnosis and optimization for abnormal noise of the HPMSM. [ABSTRACT FROM PUBLISHER]

This paper presents a quick and effective black-box method for identification and diagnosis of abnormal noise sources of permanent magnet synchronous machines (PMSMs) for electric vehicles. The black-box method is divided into three steps. In the first step, a black-box testing theory model with the rotational speed as the input signal and the noise frequency as the output signal is proposed. The main abnormal noise sources of PMSM for electric vehicles are taken into account in this model, such as harmonic currents, switching frequency of pulsewidth modulation, eccentricity, sliding bearing, and resonance. In the second step, a black-box testing experiment of noise of PMSM is applied under acceleration conditions and the sound quality of PMSM is analyzed with objective acoustic indicators, and psychoacoustic indicators as well. In the third step, abnormal noise sources are identified and diagnosed by means of the black-box testing theory and the black-box testing experiment. Noise contribution to psychoacoustic indicators is further analyzed. The proposed method is independent of detailed internal parameters of PMSM, such as structural parameters and electromagnetic parameters, and observably raises analyzing efficiency. [ABSTRACT FROM PUBLISHER]