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

Eero Immonen, Mika Lauren, Lassi Roininen, Simo Särkkä

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Industrial Technology and Management, ICITM 2020
PublisherIEEE
Pages138-143
Number of pages6
ISBN (Electronic)9781728143064
DOIs
Publication statusPublished - Feb 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Industrial Technology and Management - Oxford, United Kingdom
Duration: 11 Feb 202013 Feb 2020
Conference number: 9

Conference

ConferenceInternational Conference on Industrial Technology and Management
Abbreviated titleICITM
CountryUnited Kingdom
CityOxford
Period11/02/202013/02/2020

Keywords

  • Diesel en-gine
  • Fuel injection
  • Identification
  • Neural network
  • Uncertainty quantification

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  • Cite this

    Immonen, E., Lauren, M., Roininen, L., & Särkkä, S. (2020). Neural Network Based Identification of Fuel Injection Rate Profiles for Diesel Engines. In Proceedings of the 9th International Conference on Industrial Technology and Management, ICITM 2020 (pp. 138-143). [9080367] IEEE. https://doi.org/10.1109/ICITM48982.2020.9080367