This content is not included in your SAE MOBILUS subscription, or you are not logged in.
An Artificial Neural Network-based Approach for Virtual NOx Sensing
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2008 by SAE International in United States
Annotation ability available
With the advent of advanced diesel after-treatment technologies, sophisticated sensors are becoming a critical cost challenge to OEMs. This paper describes an approach for replacing the engine out NOx sensor with an artificial neural network (ANN) based virtual sensor. The technique centers around inferring NOx concentration from readily available engine operating parameters, eliminating the need for physical sensing and the cost associated with it. A multi-layer perceptron network was trained to estimate NOx concentration from engine speed, load, exhaust gas recirculation, and air-fuel ratio information. This supervised learning was conducted with measured engine data. The network was validated against measured data that was excluded from the training data set. The paper details application of this technique to both a heavy duty and light duty diesel engine. Results show good agreement between predictions and measured data under the steady state conditions studied. Future work will attempt to extend the approach to estimate other previously measured thermodynamic quantities and pollutant species. The methodology will also be evaluated for use under transient operation, and over the operating lifetime of the associated application.
CitationSubramaniam, M., Tomazic, D., Tatur, M., and Laermann, M., "An Artificial Neural Network-based Approach for Virtual NOx Sensing," SAE Technical Paper 2008-01-0753, 2008, https://doi.org/10.4271/2008-01-0753.
- Hornik, K., Stinchcombe, M., and White Halbert, “Multilayer feedforward networks are universal approximators,” Neural Networks, Vol 2, No. 5, 1989
- Hornik, K., “Approximation capabilities of multilayer feedforward networks,” Neural Networks, Vol. 4, No 2, 1989
- Wu, B., Filipi, Z., Assanis, D., “Using Artificial Neural Networks for Representing the Air Flow Rate through a 2.4 Liter VVT Engine,” SAE Technical Paper No. 2004-01-3054
- Wu, B., Prucka, R. G., Filipi, Z. S., “Cam-phasing Optimization Using Artificial Neural Networks as Surrogate Models - Fuel Consumption and Nox Emissions,” SAE Technical Paper No. 2006-01-1512
- Manikandan, V., Suryanarayanan, S., Rao, G. L. N., Sampath, S., “Estimation of Engine Emissions Based on Physical and Chemical Properties of Biodiesels using Artificial Neural Networks,” SAE Technical Paper No. 2006-01-3533
- Brahma, I., Rutland, C. J., “Optimization of Diesel Engine Operating Parameters Using Neural Networks,” SAE Technical Paper No. 2003-01-3228
- Guerrier, M., Cawsey, P., “The Development of Model Based Methodologies for Gasoline IC Engine Calibration,” SAE Technical Paper 2004-01-1466
- Demuth, H., Beale, M., Hagan, M., “Neural Network Toolbox,” User's Guide, Version 5