Electric Vehicle Interior Noise Contribution Analysis

2016 ◽  
Author(s):  
Yuntao Cao ◽  
Dengfeng Wang ◽  
Tonghang Zhao ◽  
Xining Liu ◽  
Chao Li ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Interior Noise ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Contribution Analysis ◽  
Noise Contribution

Case study: Separating source contributions of vehicle interior noise by operational transfer path analysis

2021 ◽  
Vol 69 (1) ◽  
pp. 39-52
Author(s):  
Ming-Hung Lu ◽  
Ming Une Jen ◽  
Dennis de Klerk
Keyword(s):  
Path Analysis ◽  
Interior Noise ◽  
Vehicle Interior ◽  
Noise Sources ◽  
Tire Noise ◽  
Transfer Path ◽  
Vehicle Interior Noise ◽  
Transfer Path Analysis ◽  
Noise Contribution

The perception of vehicle interior noise is a key quality index to customers and automakers alike. By tracing noise back to key noise sources and paths, one can focus their refinement efforts. Aiming at the most efficient way to identify the primary noise sources in a vehicle cabin, this article establishes a framework of operational transfer path analysis (OTPA) for separating contributions of noise sources by operational measurements only. OTPA model design, measuring essentials and synthesis method used for separating vehicle interior noise contributions from the powertrain, tires andwindwere described in detail. To comprehend the implementation of OTPA on noise source separation, this article also addresses an exemplification study on an electric vehicle. In the case study illustrated, both spectral map and order extractions were used to validate if the OTPA synthesized results of the powertrain noise contribution agreed with the measured results. Tire noise contribution was validated using the tires driven by the dynamometer along with all other systems switched off. With well-validated OTPA model for the powertrain and tires, further individual path breakdown of the powertrain and tire noise then was investigated to identify key contributors to the interior noise. After clearly separating interior noise contributions, one therefore could design effective countermeasures to mitigate the dominant noise sources. With appropriate scheme of measurement and synthesis, the OTPA technique could therefore effectively serve target setting and refinement focus at foremost noise contributors.

Plane Wave Resonance in the Tire Air Cavity as a Vehicle Interior Noise Source

1995 ◽  
Vol 23 (1) ◽  
pp. 2-10 ◽  
Author(s):  
J. K. Thompson
Keyword(s):  
Closed Form Solution ◽  
Form Solution ◽  
Interior Noise ◽  
Cavity Resonance ◽  
Test Results ◽  
Vehicle Interior ◽  
Air Cavity ◽  
Vehicle Interior Noise ◽  
Wave Resonance ◽  
Resonance Frequencies

Abstract Vehicle interior noise is the result of numerous sources of excitation. One source involving tire pavement interaction is the tire air cavity resonance and the forcing it provides to the vehicle spindle: This paper applies fundamental principles combined with experimental verification to describe the tire cavity resonance. A closed form solution is developed to predict the resonance frequencies from geometric data. Tire test results are used to examine the accuracy of predictions of undeflected and deflected tire resonances. Errors in predicted and actual frequencies are shown to be less than 2%. The nature of the forcing this resonance as it applies to the vehicle spindle is also examined.

Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms

2019 ◽  
Vol 67 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Ningning Liu ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Hui Guo ◽  
Bin Gao ◽  
...  
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Convergence Speed ◽  
Interior Noise ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Variable Step

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

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