Using a Statistical Machine Learning Tool for Diesel Engine Air Path Calibration Loughborough University

Format:

Conference/Event

Author/Creator:

Mohd Azmin, Mohd Azmin, author.

Contributor:

Rutledge, John

Stobart, Richard K.

Winward, Edward

Conference Name:

SAE 2014 Commercial Vehicle Engineering Congress (2014-10-07 : Rosemont, Illinois, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2014

Summary:

AbstractA full calibration exercise of a diesel engine air path can take months to complete (depending on the number of variables). Model-based calibration approach can speed up the calibration process significantly. This paper discusses the overall calibration process of the air-path of the Cat® C7.1 engine using statistical machine learning tool. The standard Cat® C7.1 engine's twin-stage turbocharger was replaced by a VTG (Variable Turbine Geometry) as part of an evaluation of a novel air system. The changes made to the air-path system required a recalculation of the air path's boost set point and desired EGR set point maps. Statistical learning processes provided a firm basis to model and optimize the air path set point maps and allowed a healthy balance to be struck between the resources required for the exercise and the resulting data quality

Notes:

Vendor supplied data

Publisher Number:

2014-01-2391

Access Restriction:

Restricted for use by site license