Lubrication Testing Methodology for Vehicle Class and Usage Based Validation

2022-01-1101

08/30/2022

Features
Event
SAE Powertrains, Fuels & Lubricants Conference & Exhibition
Authors Abstract
Content
System lubrication in automotive powertrains is a growing topic for development engineers. Hybrid and pure combustion system complexity increases in search of improved efficiency and better control strategy, increasing the number of components with lubrication demand and the interplay between them, while fully electric systems drive for higher input speeds to increase e-motor efficiency, increasing bearing and gear feed rate demands. Added to this, many e-axle and hybrid systems are in development with a shared medium and circuit for e-motor cooling and transmission lubrication. Through all this, the lubricant forms a common thread and is a fundamental component in the system, but no standardized tests can provide a suitable methodology to investigate the adequate lubrication of components at powertrain level, to support the final planned vehicle usage.
Here, a new testing methodology will be presented utilizing a vehicle class and usage space approach to determine extreme but realistic transient operating conditions that allow a validation of the overall system lubrication, regardless of the powertrain architecture. The methodology will be shown to encapsulate key aspects of the usage space that are critical to the lubrication of the system, as well as suitable operational conditions to be applied. Some of these maneuvers are fundamentally linked to standardized vehicle handling tests while others are derived from a vast collection of real measured usage space data or from tracks specific to certain vehicle classes, such as the Nürburgring laps or off-road driving data.
Meta TagsDetails
DOI
https://doi.org/10.4271/2022-01-1101
Pages
11
Citation
Hessinger, N., Yadav, R., Volk, A., and Leighton, M., "Lubrication Testing Methodology for Vehicle Class and Usage Based Validation," SAE Technical Paper 2022-01-1101, 2022, https://doi.org/10.4271/2022-01-1101.
Additional Details
Publisher
Published
Aug 30, 2022
Product Code
2022-01-1101
Content Type
Technical Paper
Language
English