Innovative Strategies for designing acoustic encapsulations for e-motors to tackle NVH performance challenges

The automotive industry's rapid transition towards E-mobility is presenting novel challenges for NVH engineers having broadband frequency composition and tonal characteristics. The purpose of the paper is to present innovative strategies for electric motor NVH development by directly applying acoustic encapsulation made of poro-elastic materials onto the casing of the electric motor. The first strategy is a pure simulation approach with combination of finite element analysis (FEA) and statistical energy analysis (SEA). Finite Element Analysis is used to calculate radiated power of electric motor casing for Maxwell electromagnetic excitations on stator teeth. This radiated power is then applied as excitation on the SEA model of the electric motor casing for prediction of exterior noise using Statistical Energy Analysis. The second strategy is a hybrid approach of simulations and experimental measurements. Electric motor is characterized as a source in terms sound power from experimental measurements of sound pressure as per ISO Standard: ISO 3745:2003. This power is applied as excitation in the interior of SEA model of the electric motor for prediction of exterior noise. Both the strategies allow design of optimized sound package on the SEA model of electric motor using statistical energy analysis to achieve the NVH performance.

Authors

Institutions

Publication Details

Journal
NOISE-CON proceedings
Published
2024-10-04
DOI
https://doi.org/10.3397/in_2024_3339
Primary Topic
Aerosol Filtration and Electrostatic Precipitation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Innovative Strategies for designing acoustic encapsulations for e-motors to tackle NVH performance challenges

Atul Miskin, Uma Mahesh HIMAKUNTLA, Rahul SHINDE
NOISE-CON proceedings
Aerosol Filtration and Electrostatic Precipitation
article

Innovative Strategies for designing acoustic encapsulations for e-motors to tackle NVH performance challenges

Atul Miskin, Uma Mahesh HIMAKUNTLA, Rahul SHINDE
article en

Abstract

The automotive industry's rapid transition towards E-mobility is presenting novel challenges for NVH engineers having broadband frequency composition and tonal characteristics. The purpose of the paper is to present innovative strategies for electric motor NVH development by directly applying acoustic encapsulation made of poro-elastic materials onto the casing of the electric motor. The first strategy is a pure simulation approach with combination of finite element analysis (FEA) and statistical energy analysis (SEA). Finite Element Analysis is used to calculate radiated power of electric motor casing for Maxwell electromagnetic excitations on stator teeth. This radiated power is then applied as excitation on the SEA model of the electric motor casing for prediction of exterior noise using Statistical Energy Analysis. The second strategy is a hybrid approach of simulations and experimental measurements. Electric motor is characterized as a source in terms sound power from experimental measurements of sound pressure as per ISO Standard: ISO 3745:2003. This power is applied as excitation in the interior of SEA model of the electric motor for prediction of exterior noise. Both the strategies allow design of optimized sound package on the SEA model of electric motor using statistical energy analysis to achieve the NVH performance.

NOISE-CON proceedingsVol. 270(8)
Altair Engineering (United States) (US)
Openalex Percentile: Top 85%
Aerosol Filtration and Electrostatic Precipitation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.