Real-time transient soot and NO x virtual sensors for diesel engine using neuro-fuzzy model tree and orthogonal least squares

Journal of Engineering for Gas Turbines and Power

Volumn 134, Issue 9, 2012, Pages

  Johri, Rajit ^a   Salvi, Ashwin ^a   Filipi, Zoran ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

hierarchical models; multilevel pseudo random signal (MPRS); neuro fuzzy model tree; orthogonal least squares (OLS); soot model; transient diesel emissions

Indexed keywords

DIESEL EMISSION; HIERARCHICAL MODEL; NEURO-FUZZY MODEL; ORTHOGONAL LEAST SQUARES; PSEUDORANDOM SIGNALS; SOOT MODEL;

EXHAUST SYSTEMS (ENGINE); FORESTRY; HIERARCHICAL SYSTEMS; NONLINEAR FEEDBACK; SOOT;

DIESEL ENGINES;

COMBUSTION; DIESEL ENGINES; FUZZY LOGIC; HIERARCHICAL SYSTEMS; MATHEMATICAL MODELS; NONLINEAR PROGRAMING; RANDOM PROCESSES; SOOT;

EID: 84864375716     PISSN: 07424795     EISSN: 15288919     Source Type: Journal    
DOI: 10.1115/1.4006942     Document Type: Article

References (22)

1. Filipi, Z., Hagena, J., and Fathy, H., 2008, "Investigating the Impact of In-Vehicle Transients on Diesel Soot Emissions," J. Therm. Sci., 12(1), pp. 53-72.
2. Kirchen, P., Obrecht, P., and Boulouchos, K., 2009, "Soot Emission Measurements and Validation of a Mean Value Soot Model for Common-Rail Diesel Engines During Transient Operation," SAE Int. J. Engines, 2, pp. 1663-1678.
3. Bayer, J., and Foster, D. E., 2003, "Zero-Dimensional Soot Modeling," SAE Technical Paper No. 2003-01-1070.
4. Brahma, I., Rutland, C. J., Foster, D. E., and He, Y., 2005, "New Approach to System Level Soot Modeling," SAE Technical Paper No. 2005-01-1122.
5. Dec, J. E., 1997, "A Conceptual Model of DI Diesel Combustion Based on Laser-Sheet Imaging," SAE Paper No. 970873.
6. Galindo, J., Bermudez, V., Serrano, J. R., and Lopez, J. J., 2001, "Cycle-to-Cycle Diesel Combustion Characterization During Engine Transient Operation," SAE Paper No. 2001-01-3262.
7. Filipi, Z., Fathy, H., Hagena, J., Knafl, A., Ahlawat, R., Liu, J., Jung, D., Assanis, D., Peng, H., and Stein, J., 2006, "Engine-in-the-Loop Testing for Evaluating Hybrid Propulsion Concepts and Transient Emissions-HMMWV Case Study," SAE Transactions, J. Commercial Vehicles, 115, pp. 23-41.
8. Godfrey, K., 1993, Perturbation Signals for System Identification, Prentice-Hall, Upper Saddle River, NJ.
9. Braun, M. W., Rivera, D. E., Stenman, A., Foslien, W., and Hrenya, C., 1999, "Multi-Level Pseudo-Random Signal Design and Model-On-Demand Estimation Applied to Nonlinear Identification of a RTP Wafer Reactor," Proceedings of the American Control Conference, Vol. 3, pp. 1573-1577. (Pubitemid 32078691)
10. Ljung, L., 1987, System Identification: Theory for the User, Prentice-Hall, Upper Saddle River, NJ.
11. Zhu, Y., 2001, Multivariable System Identification for Process Control, Pergamon, Elsevier Science, Kidlington, UK.
12. Hagena, J., 2008, "An Experimental Technique for Determining Cycle-Resolved Pre-Combustion in In-Cylinder Composition and its Application Towards the Understanding of Diesel Engine Emissions During Transient Operation," Ph.D. thesis, Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI.
13. Hagena, J. R., Filipi, Z. S., and Assanis, D. N., 2006, "Transient Diesel Emissions: Analysis of Engine Operation During a Tip-In," SAE Technical Paper No. 2006-01-1151.
14. Johansen, T. A., and Foss, B. A., 1995, "Identification of Non-Linear System Structure and Parameters Using Regime Decomposition," Automatica, 31(2), pp. 321-326.
15. Murray-Smith, R., and Johansen, T. Arne, 1995, "Local Learning in Local Model Networks," IEE Conference on Artificial Neural Networks, pp. 40-46.
16. Johansen, T. A., and Foss, B. A., 1997, "Operating Regime Based Process Modeling and Identification," Comput. Chem. Eng., 21(2), pp. 159-176.
17. Ragot, J., Mourot, G., and Maquin, D., 2003, "Parameter Estimation of Switching Piecewise Linear System," Proceedings of 42nd IEEE Conference Decision and Control, Vol. 6, pp. 5783-5788.
18. Nelles, O., 2001, Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models, Springer, Berlin.
19. Chen, S., Billings, S. A., and Luo, W., 1989, "Orthogonal Least Squares Methods and Their Application to Non-Linear System Identification," Int. J. Control, 50(5), pp. 1873-1896. (Pubitemid 20636409)
20. Chen, S., and Wigger, J., 1995, "Fast Orthogonal Least Squares Algorithm for Efficient Subset Model Selection," IEEE Trans. Signal Process., 43(7), pp. 1713-1715.
21. Filipi, Z., and Kim, Y. J., 2010, "Hydraulic Hybrid Propulsion for Heavy Vehicles: Combining the Simulation and Engine-In-the-Loop Techniques to Maximize the Fuel Economy and Emission Benefits," Oil Gas Sci. Technol., 65(1), pp. 155-178.
22. Johri, R., Salvi, A., and Filipi, Z., 2011, "Optimal Energy Management for a Hybrid Vehicle Using Neuro-Dynamic Programming to Consider Transient Engine Operation," Proceedings of the 4th Annual Dynamic Systems and Control Conference.