Computational Fluid Dynamics Analysis of Gearbox Churning Loss and Oil Splash Characteristics in Electric Vehicle Drive Units

<div class="section abstract"><div class="htmlview paragraph">The efficient operation of electric vehicles (EVs) heavily relies on the proper lubrication of the E-drive unit components, particularly the transmission gears and bearings. Improper oil supply can lead to mechanical failures, while excessive oil can increase power loss due to churning. This study focuses on utilizing Computational Fluid Dynamics (CFD) simulations to analyze the impact of drive speed, oil level, and temperature on gear churning loss in E-drive units. The research also investigates the influence of a baffle plate on power loss and oil splash characteristics. The simulations, conducted using the volume of fluid (VOF) method in Simerics-MP+, consistently illustrate a reduction in power loss with rising oil temperature and reveal decreased gear churning loss with a baffle plate, especially under high-speed conditions, highlighting its potential for enhancing energy efficiency in EVs. Additionally, post-processing analysis of oil splash patterns sheds light on the lubrication process of the gears, offering valuable insight into optimizing E-drive unit design for enhanced energy efficiency and performance.</div></div>

Authors

Institutions

Publication Details

Journal
SAE technical papers on CD-ROM/SAE technical paper series
Published
2025-03-31
DOI
https://doi.org/10.4271/2025-01-8182
Citations
1
Primary Topic
Gear and Bearing Dynamics Analysis
Type
article
Field-Weighted Citation Impact
0.71
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Computational Fluid Dynamics Analysis of Gearbox Churning Loss and Oil Splash Characteristics in Electric Vehicle Drive Units

Raj Ranganathan, Haiyang Gao, Abdul Motin, Ashutosh Kumar Pandey et al.
1 citations
SAE technical papers on CD-ROM/SAE technical paper series
Gear and Bearing Dynamics Analysis
0.71
article

Computational Fluid Dynamics Analysis of Gearbox Churning Loss and Oil Splash Characteristics in Electric Vehicle Drive Units

Raj Ranganathan, Haiyang Gao, Abdul Motin, Ashutosh Kumar Pandey, P. Madhan Kumar, Dipak Kumar Maiti, Alain Ganamet
article en
1 citations

Abstract

<div class="section abstract"><div class="htmlview paragraph">The efficient operation of electric vehicles (EVs) heavily relies on the proper lubrication of the E-drive unit components, particularly the transmission gears and bearings. Improper oil supply can lead to mechanical failures, while excessive oil can increase power loss due to churning. This study focuses on utilizing Computational Fluid Dynamics (CFD) simulations to analyze the impact of drive speed, oil level, and temperature on gear churning loss in E-drive units. The research also investigates the influence of a baffle plate on power loss and oil splash characteristics. The simulations, conducted using the volume of fluid (VOF) method in Simerics-MP+, consistently illustrate a reduction in power loss with rising oil temperature and reveal decreased gear churning loss with a baffle plate, especially under high-speed conditions, highlighting its potential for enhancing energy efficiency in EVs. Additionally, post-processing analysis of oil splash patterns sheds light on the lubrication process of the gears, offering valuable insight into optimizing E-drive unit design for enhanced energy efficiency and performance.</div></div>

SAE technical papers on CD-ROM/SAE technical paper seriesVol. 1
Simerics (United States) (US), Rivian
Openalex Percentile: Top 34%
Gear and Bearing Dynamics Analysis
0.71
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.