Data-driven emission model structures for diesel engine management system development
Journal article, 2014

This article discusses some specific data-driven model structures suitable for prediction of NOx and soot emissions from a diesel engine. The model structures can be described as local linear regression models where the regression parameters are defined by two-dimensional lookup tables. It is highlighted that this structure can be interpreted as a B-spline function. Using the model structure, models are derived from measured engine data. The smoothness of the derived models is controlled by using an additional regularization term, and the globally optimal model parameters can be found by solving a linear least squares problem. Experimental data from a five-cylinder Volvo passenger car diesel engine is used to derive NOx and soot models, using a leave-one-out cross-validation strategy to determine the optimal degree of regularization. The model for NOx emissions predicts the NOx mass flow with an average relative error of 5.1% and the model for soot emissions predicts the soot mass flow with an average relative error of 29% for the mea- surement data used in this study. The behavior of the models for different engine management system settings regarding boost pressure, amount of exhaust gas recirculation, and injection timing has been studied. The models react to the dif- ferent engine management system settings in an expected way, making them suitable for optimization of engine manage- ment system settings. Finally, the model performance dependence on the selected model complexity and on the number of measurement data points used to derive the models has been studied.

soot

diesel

B-splines

data-driven

Emission

engine management system

modeling

NOx

Author

Markus Grahn

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Krister Johansson

Volvo Cars

Tomas McKelvey

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

International Journal of Engine Research

1468-0874 (ISSN)

Vol. 15 8 906-917

Areas of Advance

Information and Communication Technology

Transport

Energy

Driving Forces

Sustainable development

Subject Categories

Energy Engineering

Control Engineering

Signal Processing

DOI

10.1177/1468087413512308

More information

Latest update

11/20/2018