Search

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]

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]