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

Eero Immonen, Mika Lauren, Lassi Roininen, Simo Särkkä

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu


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.

OtsikkoProceedings of the 9th International Conference on Industrial Technology and Management, ICITM 2020
ISBN (elektroninen)9781728143064
DOI - pysyväislinkit
TilaJulkaistu - helmik. 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Industrial Technology and Management - Oxford, Iso-Britannia
Kesto: 11 helmik. 202013 helmik. 2020
Konferenssinumero: 9


ConferenceInternational Conference on Industrial Technology and Management


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