Powertrain Digital Twinning for Real-World Emissions Compliance HORIBA MIRA Limited

Format:

Book

Conference/Event

Author/Creator:

Roberts, Phil, author.

Contributor:

Bates, Luke

Headley, Aaron

Mason, Alex

Tabata, Kunio

Whelan, Steve

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

The context for real-world emissions compliance has widened with the anticipated implementation of EU7 emissions regulations. The more stringent emissions limits and deeper real-world driving test fields of EU7 make compliance more challenging. While EU6 emissions legislation provided clear boundaries by which vehicle and powertrain Original Equipment Manufacturers (OEMs) could develop and calibrate against, EU7 creates additional challenges.To ensure that emissions produced during any real-world driving comply with legal limits, physical testing conducted in-house and in-field to evaluate emissions compliance of a vehicle and powertrain will not be sufficient. Given this, OEMs will likely need to incorporate some type of virtual engineering to supplement physical testing. In this respect, the HORIBA Intelligent Lab virtual engineering toolset has been created and deployed to produce empirical digital twins of a modern light-duty electrified gasoline Internal Combustion Engine (ICE) and a commercial vehicle diesel ICE. The former was created with the specimen tested on an engine dynamometer with the latter ICE tested using chassis dynamometer methodology. Both powertrains were exercised across their entire operational ranges with the subsequent performance and emissions measurements used to generate corresponding transient empirical response models.The validated transient empirical performance and emissions models for the electrified ICE were then coupled with data extracted from virtual driving scenarios adopting a light-duty Sports Utility Vehicle (SUV); predictions of performance and emissions attributes for these scenarios were subsequently made. The diesel transient empirical models were coupled with data extracted from virtual driving scenarios utilising a commercial goods vehicle; again, predictions of performance and emissions attributes for these scenarios were subsequently made.The predictions obtained by creating an empirical digital twin of a powertrain utilising engine or chassis dynamometer testing procedures can be utilised to quickly detect unfavourable powertrain operating conditions or problematic driving situations prior to product launch

Notes:

Vendor supplied data

Publisher Number:

2024-01-2602

Access Restriction:

Restricted for use by site license