Interior Sound of Today's Electric Cars: Tonal Content, Levels and Frequency Distribution

Lennström, David; Nykänen, Arne

doi:10.4271/2015-01-2367

Cited by 14 publications

(8 citation statements)

References 4 publications

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“…While the tyre-road contact area and the electric motor 1 are very close noise sources, the above noise component separation approach through parameter degrees of freedom becomes ineffective [9]. Moreover, electric 170 motor noise is characterized by emerging tones linked to orders, shifting from one octave band to the other depending on vehicle speed, with significant differences between EV models [15]. In the present situation where knowledge on outside electric propulsion noise is still incomplete, it would be a mistake to propose a definite speed-dependent mathematical expression describing the propulsion noise contribution in each octave.…”

Section: Selection and Description Of The Approach 155mentioning

confidence: 99%

Towards a model for electric vehicle noise emission in the European prediction method CNOSSOS-EU

Pallas

Bérengier²,

Chatagnon

et al. 2016

Applied Acoustics

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The CNOSSOS-EU method is recommended in Europe for environmental noise prediction. In regards to road traffic, it includes vehicle noise emission models implicitly referring to internal combustion vehicles. The development of electrically driven vehicles calls for the future consideration of these vehicles in prediction models. On the basis of experimental data, the study reported in this paper proposes a noise emission model for extending CNOSSOS-EU to light electric vehicles. Correction terms to be applied to the propulsion noise component are determined. Investigations on a sample of tyres with good rolling resistance performance, which is a main tyre selection criterion on these vehicles, indicated that no correction is required for the rolling noise component. Differences between the noise emission from conventional vehicles and electric vehicles are discussed for several road surfaces. Owing to the limited vehicle sample as well as transitional statements, this new model for electric vehicles running at constant speed over 20 km/h should be considered as a first step towards the definition of this vehicle technology in CNOSSOS-EU

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Section: Selection and Description Of The Approach 155mentioning

confidence: 99%

Towards a model for electric vehicle noise emission in the European prediction method CNOSSOS-EU

Pallas

Bérengier²,

Chatagnon

et al. 2016

Applied Acoustics

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show abstract

“…where u a1 is the boundary degrees of freedom, u a2 internal degrees of freedom, a1 rigid body vector, q pinned internal mode shapes and q modal degrees of freedom. Substituting equation (2) into equation (1) and pre-multiplied by the Craig-Bampton matrix to give For simplicity, the generalized mass and stiffness matrices can be defined as…”

Section: Modelling For Transmission Partsmentioning

confidence: 99%

“…When it comes to the acoustic properties of electric cars, the powertrain noise differs dramatically compared with traditional vehicles with internal combustion engines. 1 Automotive gear noise problem has received attention worldwide in the past few years. Many experimental researches and numerical simulation are done to investigate the transmission error for whine noise.…”

Section: Introductionmentioning

confidence: 99%

Precise model-based simulation for a deep hybrid transmission with compound planetary gear set

Zhang

Zou

2016

Proceedings of the Institution of Mechanical Engineers, Part K:

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Establishing a precise system level model for transmission system is quite necessary and feasible for today's computer technology. Taking into account of the exact parameters and actual working condition, an accurate model for a new deep hybrid transmission with compound planetary gear set is constructed, which in consideration of the gravity and centrifugal effect. The system level model contains the gears, shafts, bearings, clutches, electric motors and the housings. Few studies are found to investigate structure influence in system level for noise, vibration and harshness performance. Validation for the simulation results with test in three typical working conditions is carried out, which shows a quite well agreement. Furthermore, some parameters for this compound planetary gear set are investigated. The results show that for radial clearance between the ratchet and ring gear the smaller the better, while the axial clearance between the ratchet and ring gear requires cautious consideration to realize a better vibration performance for different planetary gear set. In the case of carrier, the radius of the connection pawl and the thickness of the carrier input shaft have remarkable influence on vibration characteristics. The vibration amplitude is the lowest when these two values equaling to 55 and 7 mm, respectively. Other than that, it is interesting to notice that the carrier position has a significant influence on the system vibration in pure electric drive mode. It is shown that if the planet carrier is fixed and sun gear rotates as input, the position of carrier should keep planet gears at high gravity center and symmetrical distribution on both sides of carrier centerline in the gravitational direction. The vibrational magnitude is the smallest as the carrier rotating 50 along z axis for this compound planetary gear set.

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“…This largely contributes to the fact that the SQ in EVs has become a major concern for their drivers and passengers [5]. Moreover, the experience in estimating noise in ICEVs cannot be simply used with EVs [6]. Therefore, a new SQ evaluation method is needed for EVs.…”

Section: Introductionmentioning

confidence: 99%

Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks

2020

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The sound quality (SQ) and sound perception assessments of electric vehicles (EVs) clearly differ from those of conventional internal combustion engine vehicles (ICEVs). Therefore, it is essential to describe and evaluate the SQ of EVs. To evaluate the SQ in EVs, it is necessary to organize evaluators for conducting subjective jury tests, which are time-consuming and labor-intensive. In addition, the evaluation results are subject to the evaluators themselves and other external interferences. With the advancement of machine learning and artificial neural networks (ANNs), this problem can be well solved. This paper outlines a model for SQ estimation in EVs based on a genetic algorithm-optimized back propagation artificial neural network (GA-BP ANN). Moreover, the correlation between the physical-psychoacoustical parameters and the subjective SQ estimations obtained from the jury tests was investigated in this study. It was found that the GA-BP ANN SQ model has many advantages in comparison with the multiple linear regression (MLR) model in terms of precision and generalization. In addition, this method is ready to be applied for rapidly evaluating the SQ in EVs without jury tests, and it can also be of high significance in dealing with the acoustical designs and improvements of EVs in the future.

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Interior Sound of Today's Electric Cars: Tonal Content, Levels and Frequency Distribution

Cited by 14 publications

References 4 publications

Towards a model for electric vehicle noise emission in the European prediction method CNOSSOS-EU

Towards a model for electric vehicle noise emission in the European prediction method CNOSSOS-EU

Precise model-based simulation for a deep hybrid transmission with compound planetary gear set

Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks

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