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

Paper #:
  • 2014-01-2391

Published:
  • 2014-09-30
Citation:
Mohd Azmin, F., Stobart, R., Rutledge, J., and Winward, E., "Using a Statistical Machine Learning Tool for Diesel Engine Air Path Calibration," SAE Technical Paper 2014-01-2391, 2014, https://doi.org/10.4271/2014-01-2391.
Pages:
17
Abstract:
A 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.
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