The paper deals with the simulation of the wall wetting dynamics in SI engines, making use of Recurrent Neural Networks (RNN). RNN are derived from the Multi Layer Feedforward Neural Networks, largely adopted for static mapping, by considering feedback connections between output and input layers. A Multi Input-Single Output structure has been adopted, assuming injected fuel, manifold pressure and engine speed as external input variables; the Air-Fuel Ratio at the exhaust gas oxygen sensor location has been considered as system output. The RNN has been trained (i.e. identified) and tested vs. a set of transient data measured on a commercial 4 cylinders SI engine at the test bench. The results show a good level of accuracy confirming the suitability of RNN for both HIL simulation or off-line identification of classical Mean Value Models with a drastic reduction of the calibration effort.
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ASME 2004 International Mechanical Engineering Congress and Exposition
November 13–19, 2004
Anaheim, California, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
0-7918-4706-3
PROCEEDINGS PAPER
Experimental Validation of a Recurrent Neural Network for Air-Fuel Ratio Dynamic Simulation in S.I. I.C. Engines
Michele M. Marotta,
Michele M. Marotta
University of Salerno
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Marco Sorrentino
Marco Sorrentino
University of Salerno
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Ivan Arsie
University of Salerno
Michele M. Marotta
University of Salerno
Cesare Pianese
University of Salerno
Marco Sorrentino
University of Salerno
Paper No:
IMECE2004-59079, pp. 127-136; 10 pages
Published Online:
March 24, 2008
Citation
Arsie, I, Marotta, MM, Pianese, C, & Sorrentino, M. "Experimental Validation of a Recurrent Neural Network for Air-Fuel Ratio Dynamic Simulation in S.I. I.C. Engines." Proceedings of the ASME 2004 International Mechanical Engineering Congress and Exposition. Dynamic Systems and Control, Parts A and B. Anaheim, California, USA. November 13–19, 2004. pp. 127-136. ASME. https://doi.org/10.1115/IMECE2004-59079
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