Modelling and intelligent prediction of sound quality for high-voltage fans on fuel cell vehicles

Abstract

Since fuel cell vehicles have much higher heat dissipation requirements, 350-V high-voltage fans are adopted instead of traditional 12-V cooling fans, generating more aerodynamic noise. The installed fans are required to possess not only low sound pressure level but also good psychoacoustic performance. This paper is aimed at solving the complex correlation between subjective sound quality evaluation results and objective psychoacoustics parameters and establishing a sound quality prediction model for high-voltage fans. The noise signals of two high-voltage fans operating on a fuel cell vehicle under different running conditions are collected by an artificial head and preprocessed to acquire seven objective parameters. Then the subjective evaluation experiment on the annoyance of the noise samples is carried out based on pair-wise comparison method. A sample group of 23 adults is selected and a graphical user interface is programmed for test guiding. The subjective annoyance scores of the noise signals are obtained after data processing and effectiveness verification. By analyzing the tested results, the correlations between the subjective score and each of the single psychoacoustic parameters are summarized. Two sound quality prediction models are established by multiple linear regression and backpropagation neural network respectively, and the training results of the two methods are verified and compared, proving the reliability of neural network training results. With the established models, the sound quality of the high-voltage fans can be estimated effectively without complex subjective tests, contributing to improving the acoustic performance of fan products.

Keywords

Sound quality high-voltage fan psychoacoustics prediction subjective evaluation experiment backpropagation neural network

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References

Wang

Lee

Kim

, et al. Sound-quality prediction for nonstationary vehicle interior noise based on wavelet pre-processing neural network model. J Sound Vib 2007; 299(4–5): 933–947.

Wang

Shen

Xing

YF.

A sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network. Mech Syst Signal Process 2014; 45(1): 255–266.

Schneider

Wilhelm

Alt

. Development of vehicle sound quality-targets and methods. SAE Trans 1995; 104: 2193–2206.

Genell

Västfjäll

Experience and information content affect interior vehicle sound quality assessments. Int J Veh Noise Vib 2013; 9(1–2): 47–60.

Zwicker

. Procedure for calculating loudness of temporally variable sounds. J Acoust Soc Am 1977; 62(3): 675–682.

Fastl

. Hearing sensations and noise quality evaluation. J Acoust Soc Am 1990; 87(S1): S134–S134.

Zwicker

Fastl

. Psychoacoustics: facts and models, vol. 22. New York, NY: Springer Science & Business Media, 2013.

Aures

. Sensory euphony as a function of psychoacoustic sensations. Acustica 1985; 58: 282–290.

American National Standards Institute. ANSI S3. 4-2007: American National Standard: procedure for the computation of loudness of steady sounds. New York, NY: Acoustical Society of America, 2007.

10.

Park

Kim

, et al. A comparative study on subjective feeling of engine acceleration sound by automobile types. Int J Ind Ergon 2019; 74: 102843.

11.

Hastings

. Sound quality of diesel engines. PhD Thesis, Purdue University, West Lafayette, IN, 2004.

12.

Jang

Jeong

, et al. A study on the sound quality evaluation model of the air cleaner. In: ASME 2007 international mechanical engineering congress and exposition, American Society of Mechanical Engineers Digital Collection, Seattle, Washington, DC, 11–17 November 2007.

13.

Jeong

Kim

Jeong

, et al. Development of a sound quality index for the wash cycle process of front-loading washing machines considering the impacts of individual noise sources. Appl Acoust 2015; 87: 183–189.

14.

Zhang

Hou

Shen

, et al. Sound quality prediction of vehicle interior noise and mathematical modeling using a backpropagation neural network (BPNN) based on particle swarm optimization (PSO). Meas Sci Technol 2016; 27(1): 015801.

15.

Duvigneau

Liefold

Hoechstetter

, et al. Analysis of simulated engine sounds using a psychoacoustic model. J Sound Vib 2016; 366: 544–555.

16.

Otto

Amman

Eaton

, et al. Guidelines for jury evaluations of automotive sounds. J Sound Vib 2001; 35(4): 24–47.

17.

Schneider

Feldmann

. Psychoacoustic evaluation of fan noise. In: Proceedings of the Fan 2015, Lyon, France, 15–17 April 2015.

18.

Prezelj

Novaković

. Centrifugal fan with inclined blades for vacuum cleaner motor. Appl Acoust 2018; 140: 13–23.

19.

Novaković

Ogris

Prezelj

. Validating impeller geometry optimization for sound quality based on psychoacoustics metrics. Appl Acoust 2020; 157: 107013.

20.

Choi

Pate

. An evaluation and comparison of two psychoacoustic loudness models used in low-noise ventilation fan testing. Build Environ 2017; 120: 41–52.

21.

Töpken

van de Par

. Determination of preference-eq9 levels for fan noise and their prediction by indices based on specific loudness patterns. J Acoust Soc Am 2019; 145(6): 3399–3409.

22.

Gao

Tang

Liang

, et al. Sound quality prediction and weight analysis of vehicles based on GA-BP neural network. Optics Precis Eng 2013; 21(2): 462–468.

23.

Yang

Wang

Yang

. Parameters optimization of polygonal fuzzy neural networks based on GA-BP hybrid algorithm. Int J Mach Learn Cybern 2014; 5(5): 815–822.