Probabilistic Metamodels to Quantify Uncertainties in Electric Powertrain Whining Noise Contribution

Prakash, Vinay; Sauvage, Olivier; Gagliardini, Laurent; Antoni, Jérôme

doi:10.4271/2023-01-1071

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…(1), all three major categories of sources of noise are dependent on the operating conditions and when combined, different key performance indicators can be estimated, like the prominence ratio, which results in the prominent tones in the SPL spectra. In a recent article, Prakash et al [35] developed Bayesian surrogates to consider such background noises (dashed blue box in Fig. (1)) using measurement databases.…”

Section: Icementioning

confidence: 99%

“…Before proceeding with the stochastic analysis using Bayesian approach, a fundamental practice is to perform the deterministic model evaluation to check the generalization error of the model developed. As discussed in [35], k-fold cross-validation (CV) technique can be used to validate the model with sufficient accuracy. Note that, the number of folds depends on the amount of data available, so that each training set is representative of the entire available dataset.…”

Section: Deterministic Check For Multi-target Regression Modelmentioning

confidence: 99%

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Stochastic assessment of electric powertrain whining noise under early-stage design uncertainties

Prakash,

Sauvage,

Antoni

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part D:

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Despite the advantage of being quieter than traditional internal combustion engine vehicles, electric vehicles are often distinguished by high-frequency tonal components, which can be perceived as unpleasant to the occupants and the environment. To ensure optimal acoustic comfort in electric vehicles, it is important to analyze the NVH behavior of e-powertrains during the early stages of the design process which poses inherent uncertainties, such as varying operating conditions, partial knowledge of design parameters, dispersion in measurement-based data, etc. To effectively address these uncertainties, it is necessary to use fast and comprehensive stochastic models during the design phase. In this work, a probabilistic framework is presented to estimate the electric powertrain’s interior whining noises considering the structure-borne contribution. Hence, two different stochastic metamodels are developed for efficient quantification and propagation of uncertainties from electric motor stage to powertrain mounting system. Multivariate Bayesian regression models help to incorporate prior knowledge on the uncertain parameters and generate the respective posterior distributions using Markov chains Monte Carlo (MCMC) techniques. For this particular application, the data is generated through weakly-coupled multi-physical domains estimated using semi-analytical approaches and combined with measured vehicle transfer functions. Importantly, the validation of each domain is conducted separately to ensure accurate representation. The results obtained from the developed probabilistic framework will aid in the early design stages by guiding engineers in making informed decisions to optimize NVH performance.

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Section: Icementioning

confidence: 99%

Section: Deterministic Check For Multi-target Regression Modelmentioning

confidence: 99%

Stochastic assessment of electric powertrain whining noise under early-stage design uncertainties

Prakash,

Sauvage,

Antoni

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…However, most of the studies in the past have approached the problem considering either a deterministic setting for the operating conditions and design architectures or focusing only on the local acoustic performance of emotors without considering the different transfer paths leading to the interior cabin noise. As shown in [4], Bayesian surrogates (or metamodels) can be developed from masking noise measurement data (dashed blue box in Figure 1) utilizing the prior-domain knowledge resulting in the uncertainty estimates of the resulting interior sound pressure level (SPL). These probabilistic metamodels are easy-toevaluate functional mappings where the desired response is a distribution of probable outputs instead of a single pointestimate.…”

Section: Introductionmentioning

confidence: 99%

Assessment of electric powertrain whining noise contribution under early-stage design uncertainties

Prakash,

Sauvage,

Antoni

et al. 2024

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

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Despite the advantage of being quieter than traditional internal combustion engine vehicles, electric vehicles are often distinguished by high-frequency tonal components, which can be perceived as unpleasant to the occupants. To ensure optimal acoustic comfort in electric vehicles, it is important to analyze the NVH behavior of e-powertrains during the early stages of the design process which poses inherent uncertainties, such as varying operating conditions, partial knowledge of design parameters, dispersion in measurement data, etc. To effectively address these uncertainties, it is necessary to use fast and comprehensive stochastic models during the design phase. In this work, a deterministic framework is first presented to estimate the e-powertrain's interior whining noises combining both airborne & structure-borne contribution. Subsequently, under probabilistic framework, the uncertainties are introduced in the deterministic models considering random sampling of operating conditions, and the chosen geometrical design parameters for the e-machine under assessment. A multivariate Bayesian metamodel helps to incorporate prior knowledge on the uncertain parameters and generate the posterior distribution of harmonic forces. The uncertainties in sound pressure level are propagated through weakly-coupled multi-physical domains estimated using semi-analytical approaches and combined with measured vehicle transfer functions. Sensitivity analysis using the developed metamodel provides an efficient way to reinstate prior beliefs about the unknown parameters.

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Prediction and Improvement of Structure-Borne and Airborne Whines of an Electric Vehicle for Virtual Development

Yoo,

Chae,

Choi

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Many sources and paths cause interior cabin noise. Some noise from an electric vehicle is unique and different from a vehicle with an internal combustion engine. Especially, whine noise occurs due to the particular orders of the electromagnetic force of an electric motor and transmission gears, which is tonal and usually reaches high frequencies. This paper covers structure-borne (SB) and airborne (AB) aspects to estimate whine, and the difference between the two characteristics is distinguished. The focus lies mainly on the process of virtual vehicle development and application for performance improvement. First, to predict SB whine, an e-powertrain is modeled as a finite element model (FEM), and electromagnetic (EM) forces are calculated. A vehicle model is also modeled as an FEM, in which interior sound packages are carefully modeled as they play an important role in the medium-frequency region. The e-powertrain and vehicle models (being simulated separately) are combined to obtain cabin noise up to 1.5 kHz. Design studies show that the stiffness of mount insulators and the panel stiffness of the vehicle can be substantial design variables to reduce the SB whine. Second, the study highlights a simulation method to predict interior airborne whine up to 8 kHz by combining the FEMs of the e-powertrain and the vehicle’s exterior cavity with a statistical energy analysis (SEA) model of a vehicle. Path contribution can be identified by defining source strength and acoustic transfer function of airborne paths. Design modifications, including encapsulation of the e-powertrain, show this simulation process could be practically useful to reduce the airborne whine at high frequencies.</div></div>

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Probabilistic Metamodels to Quantify Uncertainties in Electric Powertrain Whining Noise Contribution

Cited by 3 publications

References 18 publications

Stochastic assessment of electric powertrain whining noise under early-stage design uncertainties

Stochastic assessment of electric powertrain whining noise under early-stage design uncertainties

Assessment of electric powertrain whining noise contribution under early-stage design uncertainties

Prediction and Improvement of Structure-Borne and Airborne Whines of an Electric Vehicle for Virtual Development

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