A novel interval analysis method to identify and reduce pure electric vehicle structure-borne noise. (9th June 2020)

• Record Type: Journal Article
• Authors: Huang, Hai B.; Wu, Jiu H.; Huang, Xiao R.; Ding, Wei P.; Yang, Ming L.
• Is Part Of: Journal of sound and vibration. Volume 475(2020)
• Publication Date: 2020-06-09
• Subjects: Pure electric vehicle -- Vehicle suspension -- Structure-borne noise -- Noise source identification -- Interval analysis; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2020.115258
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: The interior noise of a pure electric vehicle (EV) is quieter than that of a traditional internal combustion engine vehicle (ICEV). However, if the noise masking effect of an ICEV is not employed, structure-borne noise from the suspension in an EV will become prevalent, although the interior noise weakens when the EV is driven at a moderate speed. Identifying the sources of suspension structure-borne noise is necessary to reduce the interior noise in an EV and improve the vehicle sound quality. However, the suspension system comprises many components and noise sources, making the identification and reduction of suspension structure-borne noise in an EV a challenging task. In this paper, a new noise source identification method based on interval analysis is proposed. This method can not only accurately identify the sources of noise but also provide details regarding modification methods for reducing interior noise. To implement this method, a test EV was used for measurement, and 15 tests constructed through experimental design were carried out to record the interior noise and vibrations of suspension components. The results showed that the suspension structure-borne noise of the test vehicle was distributed mainly below a frequency of 400 Hz. The feasible intervals and noise source contributions were calculated via the interval analysis method, and the front spring and rear shock absorber were identified as the major sources of suspension structure-borne noise. In addition, component parameters were optimized through the interval analysis method. In accordance with the suggested modification method, a verification test was implemented, illustrating that the EV interior noise quality was improved and validating the effectiveness of the proposed method. The presented approach may be regarded as a promising method for identifying and optimizing vehicle noise sources. Highlights: An interval analysis method is proposed for noise source identification. The design variables and objectives are treated as feasible intervals. The potential sources of structure-borne noise are analyzed and presented. A contribution analysis of potential noise sources is implemented. A modification method for optimizing structure-borne noise is presented.