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Machine-Learning-Based Modelling of Electric Powertrain Noise Control Treatments
ISSN: 0148-7191, e-ISSN: 2688-3627
Published May 08, 2023 by SAE International in United States
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Encapsulation of electric powertrains is a booming topic with the electrification of vehicles. It is an efficient way of reducing noise radiated by the machines even in later stages of the design and without altering the electromagnetic performance. However, it is still difficult to define the best possible treatment. The locations, thicknesses and material compositions need to be optimized within given constraints to reach maximum noise reduction while keeping added mass and cost at minimum. In this paper, a methodology to design the encapsulation based on numerical vibro-acoustic simulations is presented. In a first step, the covered areas are identified through post-processing of a finite element acoustic radiation model of the bare powertrain. In a second step, a design of experiment is performed to assess the influence of various cover parameters on the acoustic radiation results. This second step can be hugely computationally expensive as the number of required virtual experiments increases exponentially with the number of treated regions and parameters for each treated region. In this chapter, we present a physics-based reduced-order model to overcome this difficulty and do design of experiments in a much more affordable manner. It is then enriched with machine learning to provide finer tuning of the treatment definition. This would allow the final designer to iterate between treatment strategy in the matter of seconds, paving the road for an advanced optimization algorithm. The accuracy of the presented models is detailed.
CitationKocsis, A. and Jacqmot, J., "Machine-Learning-Based Modelling of Electric Powertrain Noise Control Treatments," SAE Technical Paper 2023-01-1132, 2023, https://doi.org/10.4271/2023-01-1132.
- Van Antwerpen , B. , Corveleyn , S. , Lielens , G. , and Van den Nieuwenhof , B. Validity of the Transfer Matrix Method for Modeling Trim Components in Vibro-Acoustic Applications Proceedings of ISMA2018 and USD2018 4511 4525 2018
- Migeot , J.-L. , Coyette , J.-P. , and Lielens , G. Acoustics Belgium IJK Numerics 2016
- Lewis-Beck , C. and Lewis-Beck , M. Applied Regression: An Introduction 22 Sage publications 2015
- Pedregosa , F. , Gaël , V. , Gramfort , A. , Michel , V. et al. Scikit-learn: Machine learning in Python Journal of Machine Learning Research 12 2011 2825 2830