Development of recurrent neural networks for virtual sensing of NOx emissions in internal combustion engines

Event
9th International Conference on Engines and Vehicles
Authors Abstract
Content
The paper focuses on the experimental identification and validation of recurrent neural networks (RNN) for virtual sensing of NO emissions in internal combustion engines (ICE). Suited training procedures and experimental tests are proposed to improve RNN precision and generalization in predicting NO formation dynamics. The reference Spark Ignition (SI) engine was tested by means of an integrated system of hardware and software tools for engine test automation and control strategies prototyping. A fast response analyzer was used to measure NO emissions at the exhaust valve. The accuracy of the developed RNN model is assessed by comparing simulated and experimental trajectories for a wide range of operating scenarios. The results evidence that RNN-based virtual NO sensor will offer significant opportunities for implementing on-board feedforward and feedback control strategies aimed at improving the performance of after-treatment devices.
Meta TagsDetails
DOI
https://doi.org/10.4271/2009-24-0110
Pages
8
Citation
Arsie, I., Pianese, C., and Sorrentino, M., "Development of recurrent neural networks for virtual sensing of NOx emissions in internal combustion engines," SAE Int. J. Fuels Lubr. 2(2):354-361, 2010, https://doi.org/10.4271/2009-24-0110.
Additional Details
Publisher
Published
Sep 13, 2009
Product Code
2009-24-0110
Content Type
Journal Article
Language
English