Design of silent electric motors: optimization under constraints and parameters uncertainties

Jeannerot, Martin; Dupont, Jean‐Baptiste; Sadoulet-Reboul, Émeline; Ouisse, Morvan; Lanfranchi, Vincent; Bouvet, Pascal

doi:10.1109/icem49940.2020.9271056

Cited by 2 publications

(2 citation statements)

References 15 publications

(10 reference statements)

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“…As the electromagnetic excitations in e-machines can be sensitive to even slight variations of the geometric and control parameters of the active magnetic parts, it is important to consider such variations in the output predictions. In this regard, Jeannerot et al [23] conducted a time-consuming FEM-based probabilistic robust optimization of an e-machine to reduce SPL, taking into account the variability of random parameters. Pulido et al [2], on the other hand, developed a Gaussian process surrogate model of an e-machine using nonlinear FEM to account for uncertainty in torque, flux linkage, and core loss.…”

Section: Icementioning

confidence: 99%

“…In the structural domain, the excitations coming from the EM-domain are first transformed from time-domain to frequency domain and are then mapped onto the structural mesh. This can easily be achieved using commercially available FE solvers as was done in many previous studies, see for example, [23,12]. Despite being more accurate than analytical or semi-analytical methods, such purely-numerical methods are time consuming and prediction on wide-speed range becomes a challenge, as mentioned in [11].…”

Section: Multiphysical Nature Of Electric Powertrain Noise Assessmentmentioning

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: Multiphysical Nature Of Electric Powertrain Noise Assessmentmentioning

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

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Probabilistic Metamodels to Quantify Uncertainties in Electric Powertrain Whining Noise Contribution

Prakash¹,

Sauvage²,

Gagliardini³

et al. 2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">With electromobility, vehicles are becoming quieter due to the presence of electric motors that replace internal combustion engines. The interior cabin noise of electric vehicles is characterized by high-frequency components that can be annoying and unpleasant. Therefore, it is essential to analyse the NVH behaviour of e-powertrains early in the design-phase. However, this induces inherent uncertainties during the design process related to the operating conditions, geometrical parameters, measurement techniques, etc. that need to be quantified with fast and comprehensive stochastic models.</div><div class="htmlview paragraph">In this work, we first present a deterministic framework to provide first-order estimations of the e-powertrain’s interior whining noises, combining both the airborne & structure-borne contribution with data-driven NVH transfers meta-models. Subsequently, stochasticity is induced in the deterministic models considering random sampling of operating conditions and the chosen geometrical parameters for the e-machine under assessment. At each stage, metamodels (or surrogates), developed in the Bayesian framework, take into account the uncertainties which are propagated from operating conditions to whining noise contribution inside the cabin. The work is demonstrated on an interior permanent magnet synchronous motor which is widely used in electric vehicles traction drives.</div></div>

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Design of silent electric motors: optimization under constraints and parameters uncertainties

Cited by 2 publications

References 15 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

Probabilistic Metamodels to Quantify Uncertainties in Electric Powertrain Whining Noise Contribution

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