Development of recurrent neural networks for virtual sensing of NOx emissions in internal combustion engines 2009-24-0110
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.
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. Download Citation
Ivan Arsie, Cesare Pianese, Marco Sorrentino
Department of Mechanical Engineering, University of Salerno
9th International Conference on Engines and Vehicles
SAE International Journal of Fuels and Lubricants-V118-4EJ, SAE International Journal of Fuels and Lubricants-V118-4