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Probabilistic Metamodels to Quantify Uncertainties in Electric Powertrain Whining Noise Contribution
ISSN: 0148-7191, e-ISSN: 2688-3627
Published May 08, 2023 by SAE International in United States
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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.
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.
CitationPrakash, V., Sauvage, O., Gagliardini, L., and Antoni, J., "Probabilistic Metamodels to Quantify Uncertainties in Electric Powertrain Whining Noise Contribution," SAE Technical Paper 2023-01-1071, 2023, https://doi.org/10.4271/2023-01-1071.
- Bibra , E.M. , Connelly , E. , Gorner , M. , Lowans , C. , Paoli , L. , Tattini , J. , and Teter , J. Global EV Outlook 2021: Accelerating Ambitions Despite the Pandemic 2021
- Qian , K. , Wang , J. , Gao , Y. , Sun , Q. et al. Interior Noise and Vibration Prediction of Permanent Magnet Synchronous Motor Journal of Vibroengineering 20 5 2018 2225 2236
- Gieras , J.F. , Wang , C. , and Lai , J.C. Noise of Polyphase Electric Motors CRC Press 2018 1-315-22098-9
- Deng , W. and Zuo , S. Electromagnetic Vibration and Noise of the Permanent-Magnet Synchronous Motors for Electric Vehicles: An Overview IEEE Trans. Transp. Electrific. 5 1 2019 59 70 10.1109/TTE.2018.2875481
- Vijayraghavan , P. Noise in Electric Machines: A Review IEEE Transactions on Industry Applications 35 5 1999 8
- Blanning , R.W. The Construction and Implementation of Metamodels Simulation 24 6 1975 177 184 10.1177/003754977502400606
- Wang , G.G. and Shan , S. Review of Metamodeling Techniques in Support of Engineering Design Optimization Journal of Mechanical Design 129 4 2007 370 380 10.1115/1.2429697
- Wang , B. , Rahman , T. , Chang , K. , Mohammadi , M.H. et al. A Neural Network Based Surrogate Model for Predicting Noise in Synchronous Reluctance Motors 2016 IEEE Conference on Electromagnetic Field Computation (CEFC) 1 2016 10.1109/CEFC.2016.7816297
- Mohammadi , M.H. , Rahman , T. , Silva , R.C.P. , Wang , B. et al. Effect of Acoustic Noise on Optimal SynRM Design Regions IEEE Trans. Magn. 54 3 2018 1 4 10.1109/TMAG.2017.2760859
- Ibrahim , I. , Silva , R. , Mohammadi , M.H. , Ghorbanian , V. et al. Surrogate-Based Acoustic Noise Prediction of Electric Motors IEEE Transactions on Magnetics 56 2 2020 1 4 10.1109/TMAG.2019.2945407
- Jeannerot , M. , Dupont , J.-B. , Sadoulet-Reboul , E. , Ouisse , M. et al. Design of Silent Electric Motors: Optimization under Constraints and Parameters Uncertainties 2020 International Conference on Electrical Machines (ICEM) 429 435 2020 10.1109/ICEM49940.2020.9271056
- Beltrán-Pulido , A. , Aliprantis , D. , Bilionis , I. , Munoz , A.R. et al. Uncertainty Quantification and Sensitivity Analysis in a Nonlinear Finite-Element Model of a Permanent Magnet Synchronous Machine IEEE Transactions on Energy Conversion 35 4 2020 2152 2161 10.1109/TEC.2020.3001914
- Prakash , V. , Sauvage , O. , Antoni , J. , and Gagliardini , L. Bayesian NVH Metamodels to Assess Interior Cabin Noise Using Measurement Databases Proceedings of the 30th International Conference on Noise and Vibration Engineering, ISMA 1666 1684 2022
- Bonneel , P. , Le Besnerais , J. , Pile , R. , and Devillers , E. Pyleecan: An Open-Source Python Object-Oriented Software for the Multiphysic Design Optimization of Electrical Machines 2018 XIII International Conference on Electrical Machines (ICEM) IEEE 948 954 2018 1-5386-2477-X
- Dupont , J.-B. , Bouvet , P. , and Wojtowicki , J.-L. Simulation of the Airborne and Structure-Borne Noise of Electric Powertrain: Validation of the Simulation Methodology SAE Technical Paper 2013-01-2005 2013 https://doi.org/10.4271/2013-01-2005
- Gieras , J.F. , Wang , C. , Joseph , C.L. , and Ertugrul , N. Analytical Prediction of Noise of Magnetic Origin Produced by Permanent Magnet Brushless Motors 2007 IEEE International Electric Machines & Drives Conference IEEE 148 152 2007
- Blevins , R.D. and Plunkett , R. Formulas for Natural Frequency and Mode Shape Journal of Applied Mechanics 47 2 1980 461
- Leissa , A.W. Vibration of Shells Scientific and Technical Information Office, National Aeronautics and Space Administration 1973
- Gelman , A. , Carlin , J.B. , Stern , H.S. , and Rubin , D.B. Bayesian Data Analysis Chapman and Hall/CRC 1995 0-429-25841-0
- Bolstad , W.M. and Curran , J.M. Introduction to Bayesian Statistics John Wiley & Sons 2016 1-118-09156-6
- Hoffman , M.D. and Gelman , A.
- Yang , Z. , Krishnamurthy , M. , and Brown , I.P. Electromagnetic and Vibrational Characteristic of IPM over Full Torque-Speed Range 2013 International Electric Machines & Drives Conference IEEE Chicago, IL, USA 295 302 2013 10.1109/IEMDC.2013.6556267