A4 Conference proceedings

Neural Network Based Identification of Fuel Injection Rate Profiles for Diesel Engines


Publication Details
Authors: Immonen Eero, Lauren Mika, Roininen Lassi, Särkkä Simo
Publication year: 2020
Language: English
Title of parent publication: 2020 9th International Conference on Industrial Technology and Management (ICITM)
ISBN: 978-1-7281-4307-1
eISBN: 978-1-7281-4306-4
JUFO-Level of this publication: 0
Open Access: Not an Open Access publication

Abstract

The rate profile at which fuel is injected into an inter-nal combustion (IC) diesel engine is among the most important parameters affecting the engine performance and exhaust emissions. However, it is notoriously difficult to measure on-line in practice. This article studies the application of neural network based methods for identification of the diesel fuel in-jection rate profile from in-cylinder pressure data, for which measurements are easy to obtain online from a running en-gine. The proposed approach provides a prediction of the injection rate profile as a function of the crank angle, and an estimate of the uncertainty associated with the prediction. Among others, the results presented herein may be benefi-cial for real-time injector fault detection and also for devising novel optimal control strategies for minimizing exhaust emissions of diesel engines.


Last updated on 2020-19-05 at 13:33