Evaluation and prediction of the sound quality of the electric powertrain for electric vehicles under acceleration conditions

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (22) : 126-134.

DU Jinfu*，YANG Pan，QU Nanfei

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Abstract

The high-frequency acceleration noise of electric powertrains for electric vehicles seriously affects the sound quality of the whole vehicle. In this regard, multi-operating-condition acceleration noise signals are collected through vibration and noise tests of the electric powertrain. These signals were subsequently subjectively and objectively evaluated for sound quality. After reference correlation analysis, the improved gray wolf optimizer-support vector regression (IGWO-SVR) model was developed for sound quality prediction of electric powertrains using psychoacoustic parameters as inputs and optimized support vector regression (SVR) using the improved gray wolf optimizer (IGWO). Complementary ensemble empirical mode decomposition (CEEMD) and root mean square (RMS) of the signal are introduced to extract the CEEMD-RMS features of the acceleration noise of the electric powertrain and to establish the IGWO-SVR sound quality prediction model with CEEMD-RMS as input. The test results show that the sound quality prediction model with CEEMD-RMS features as inputs predicts better than the IGWO-SVR model with psychoacoustic parameters as inputs, and the mean relative error of the test set is reduced from 8.88% to 4.18%.

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electric powertrain / acceleration condition / sound quality / complementary ensemble empirical mode decomposition (CEEMD) / predictive model

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DU Jinfu, YANG Pan, QU Nanfei. Evaluation and prediction of the sound quality of the electric powertrain for electric vehicles under acceleration conditions[J]. Journal of Vibration and Shock, 2024, 43(22): 126-134

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