Real-Time Estimation of Engine NOx Emissions via Recurrent Neural Networks

Abstract 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 engine NO emissions in transient operation. 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 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 of NO emissions for a wide range of operating scenarios, with an estimation error lower than 2 % throughout the test transients. The results evidence that RNN-based virtual NO sensor offers significant opportunities for improving the performance of SCR after-treatment devices.