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A Methodology to Enhance Design and On-Board Application of Neural Network Models for Virtual Sensing of No x Emissions in Automotive Diesel Engines
Technical Paper
2013-24-0138
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The paper describes suited methodologies for developing Recurrent Neural Networks (RNN) aimed at estimating NOx emissions at the exhaust of automotive Diesel engines. The proposed methodologies particularly aim at meeting the conflicting needs of feasible on-board implementation of advanced virtual sensors, such as neural network, and satisfactory prediction accuracy. Suited identification procedures and experimental tests were developed to improve RNN precision and generalization in predicting engine NOx emissions during transient operation. NOx measurements were accomplished by a fast response analyzer on a production automotive Diesel engine at the test bench. Proper post-processing of available experiments was performed to provide the identification procedure with the most exhaustive information content. The comparison between experimental results and predicted NOx values on several engine transients, exhibits high level of accuracy.
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Citation
Arsie, I., Cricchio, A., De Cesare, M., Pianese, C. et al., "A Methodology to Enhance Design and On-Board Application of Neural Network Models for Virtual Sensing of Nox Emissions in Automotive Diesel Engines," SAE Technical Paper 2013-24-0138, 2013, https://doi.org/10.4271/2013-24-0138.Also In
References
- Alberer , D. , Re , L. , Winkler , S. , and Langthaler , P. Virtual Sensor Design of Particulate and Nitric Oxide Emissions in a DI Diesel Engine SAE Technical Paper 2005-24-063 2005 10.4271/2005-24-063
- 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 10.4271/2009-24-0110
- Arsie , I. , Pianese , C. , Sorrentino , M. A Procedure to Enhance Identification of Recurrent Neural Networks for Simulating Air-Fuel Ratio Dynamics in SI Engines Engineering Applications of Artificial Intelligence 19 1 65 77 2006
- Atkinson , C. , Long , T. , and Hanzevack , E. Virtual Sensing: A Neural Network-based Intelligent Performance and Emissions Prediction System for On-Board Diagnostics and Engine Control SAE Technical Paper 980516 1998 10.4271/980516
- Ayeb , M. , Theuerkauf , H. , and Winsel , T. SI Engine Emissions Model Based on Dynamic Neural Networks and D-Optimality SAE Technical Paper 2005-01-0019 2005 10.4271/2005-01-0019
- Bebis , G. , Georgiopoulos , M. , Kasparis , T. Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization Neurocomputing 17 3-4 167 194 1997
- Brand , D. , Onder , C. , Guzzella , L. Virtual NO Sensor for Spark-Ignition Engines Int. J. Engine Res. 8 221 240 2007
- De Cesare , M. and Covassin , F. Neural Network Based Models for Virtual NOx Sensing of Compression Ignition Engines SAE Technical Paper 2011-24-0157 2011 10.4271/2011-24-0157
- del Re , L. , Langthaler , P. , Furtmueller , C. , Winkler , S. et al. NOx Virtual Sensor Based on Structure Identification and Global Optimization SAE Technical Paper 2005-01-0050 2005 10.4271/2005-01-0050
- Haykin , S. Neural Network Prentice Hall 1999
- Heywood , J.B. Internal Combustion Engine Fundamentals MC Graw Hill 1998
- Nørgaard , M. , Ravn , O. , Poulsen , N.K. NNSYSID and NNCTRL tools for system identification and control with neural networks Computing and Control Engineering Journal 12 1 29 36 2001
- Nørgaard , M. , Ravn , O. , Poulsen , N.L. , Hansen , L.K. Neural Networks for Modelling and Control of Dynamic Systems Springer-Verlag 2000
- Ramos , J.I. Internal Combustion Engine Modeling Hemisphere Publishing Corporation 1989
- Sekhar , P.K. , Broshaa , E.L. , Mukundana , R. , Li , W. , Nelsona , M.A. , Palanisamy , P. , Garzona , F.H. 2009 Application of commercial automotive sensor manufacturing methods for NOx NOx/NH3 mixed potential sensors for on-board emissions control Sens.Actuators : chem. 10.1016/j.snb.2009.10.45
- Subramaniam , 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 10.4271/2008-01-0753
- Tschanz , F. , Amstutz , A. , Onder , C.H. and Guzzella , L. A real-time soot model for emission control of a diesel engine Proc. of the 6th IFAC Symposium on Advances in Automotive Control 222 227 2010
- Walker , A. , Allansson , R. , Blakeman , P. , Lavenius , M. et al. The Development and Performance of the Compact SCRTrap System: A 4-Way Diesel Emission Control System SAE Technical Paper 2003-01-0778 2003 10.4271/2003-01-0778