A multiphysics optimization approach to design low noise and light weight electric powertrain noise, vibration and harshness (NVH) prediction of electric powertrain using finite element analysis (FEA) and optimization

Kumar, Dinesh; Kottalgi, Santosh; Sambharam, Tushar; Mandloi, Padmesh

doi:10.1109/iecon.2017.8216287

Cited by 7 publications

(2 citation statements)

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“…Even worse, the relatively small-size and lightweight design for motors, the large electromagnetic forces as well as the poor rigidity of motor structures increase this serious problem of electromagnetic vibration and noise which will affect the riding comfort. Therefore, it is clearly justified to incorporate in the design the requirements of noise and vibration to avoid large retrofit expenses when the overall performance is being optimized/balanced as said in [10] and [7,9,14] also agree on this.…”

Section: Introductionmentioning

confidence: 88%

“…The electric powertrain is drastically growing its importance in industry, specifically in the automotive one, because of different reasons. One of them is the environmental and energy regulations laid out in United Nations Climate Change Conference held in Paris in 2015 [1,14]. In addition, the development of technologies such as electric vehicles (EVs), hybrid electric vehicles (HEVs) or self-driving cars encourages even more the research and development in this area.…”

Section: Introductionmentioning

confidence: 99%

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A novel sparse reduced order formulation for modeling electromagnetic forces in electric motors

2021

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A novel model order reduction (MOR) technique is presented to achieve fast and real-time predictions as well as high-dimensional parametric solutions for the electromagnetic force which will help the design, analysis of performance and implementation of electric machines concerning industrial applications such as the noise, vibration, and harshness in electric motors. The approach allows to avoid the long-time simulations needed to analyze the electric machine at different operation points. In addition, it facilitates the computation and coupling of the motor model in other physical subsystems. Specifically, we propose a novel formulation of the sparse proper generalized decomposition procedure, combining it with a reduced basis approach, which is used to fit correctly the reduced order model with the numerical simulations as well as to obtain a further data compression. This technique can be applied to construct a regression model from high-dimensional data. These data can come, for example, from finite element simulations. As will be shown, an excellent agreement between the results of the proposed approach and the finite element method models are observed.

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