The identification of vehicle noise is the basis for studying the acoustic characteristics of vehicles. In this paper, both excitation of noise sources and response of interior noise were identified. Firstly, a transfer path analysis (TPA) model was established to identify the excitation of noise sources, which includes vehicle main noise sources, such as engine, tire, exhaust pipe and muffler. Based on the operational signals and transfer function which were tested in the vehicle semi-anechoic room, the excitation of noise sources was identified using inverse matrix method. Identify result indicated that tires have higher excitation amplitude than engine in high frequency band. Therefore, the transfer path between the tire and the cabin, such as carpet and windshield, should be taken as the focus of acoustic performance improvement. By improving the acoustic material on the transfer path, the loss of sound in the transfer path will be increase. Secondly, the energy superposition method was used to calculate the response of vehicle interior noise based on the established TPA model. In this part, we combine the transfer path analysis and condition power spectrum (CPS) to reduce the correlation among signals. So that we can improve the calculation accuracy of interior noise. The result shows that error between calculated value and test value of interior noise doesn’t exceed 5%, which means the model has a higher identification accuracy for the interior noise. These findings provide important reference value for the improvement of this vehicle’s acoustic performance.


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    Titel :

    Identification of Vehicle Noise Based on Transfer Path and Condition Power Spectrum Analysis


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Shangguan, Wen-Bin (Autor:in) / Xing, Yu-Jin (Autor:in) / Qu, Zhang (Autor:in)

    Kongress:

    WCX SAE World Congress Experience ; 2022



    Erscheinungsdatum :

    2022-03-29




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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