This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Turbocharger Modeling for Automotive Control Applications
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 01, 1999 by SAE International in United States
Annotation ability available
Dynamic simulation models of turbocharged Diesel and gasoline engines are increasingly being used for design and initial testing of engine control strategies. The turbocharger submodel is a critical part of the overall model, but its control-oriented modeling has received limited attention thus far. Turbocharger performance maps are typically supplied in table form, however, for inclusion into engine simulation models this form is not well suited. Standard table interpolation routines are not continuously differentiable, extrapolation is unreliable and the table representation is not compact. This paper presents an overview of curve fitting methods for compressor and turbine characteristics overcoming these problems. We include some background on compressor and turbine modeling, limitations to experimental mapping of turbochargers, as well as the implications of the compressor model choice on the overall engine model stiffness and simulation times.
The emphasis in this paper is on compressor flow rate modeling, since this is both a very challenging problem as well as a crucial part of the overall engine model. For the compressor, four different methods, including neural networks, are presented and tested on three different compressors in terms of curve fitting accuracy, model complexity, genericity and extrapolation capabilities. Curve fitting methods for turbine characteristics are presented for both a wastegated and a variable geometry turbine.
CitationMoraal, P. and Kolmanovsky, I., "Turbocharger Modeling for Automotive Control Applications," SAE Technical Paper 1999-01-0908, 1999, https://doi.org/10.4271/1999-01-0908.
- Demuth, H., Beale, M., “Neural Network Toolbox, version 3.0”, The Mathworks, Inc., 1988.
- Flaxington, D., Allied Signal/Garrett. Personal communication. July1996.
- Fraden, J., “AIP Handbook of modern sensors”, AIP Press, 1993.
- Nelson, S.A., Filipi, Z.S., Assanis, D.N., “The use of neural networks for matching compressors with diesel engines,” Spring Technical Conference, volum ICE-26-3, pages 35-42, 1996.
- Jensen, J.P, Kristensen, A.F., Sorenson, S.C., Houbak, N., Hendricks, E., “Mean value modeling of a small turbocharged diesel engine,” SAE 910070.
- Kao, M., Moskwa, J.J., “Turbocharged diesel engine modeling for nonlinear engine control and estimation”, ASME Journal of Dynamic Systems, Measurement and Control, Vol 117, 1995.
- Kolmanovsky, I.V., Moraal, P.E., van Nieuwstadt, M.J., Criddle, M., Wood, P., “Modeling and identification of a 2.0 I turbocharged DI diesel engine”. Ford internal technical report SR-97-039, 1997.
- Mueller, M., “Mean value modeling of turbocharged spark ignition engines”, Master's thesis, DTU, Denmark, 1997.
- Puskorius, G.V., Feldkamp, L.A., “Decoupled extended Kalman filter training of feedforward layered networks”, Proceedings IJCNN, 1991.
- Sher, E., Rakib, S., Luria, D., “A practical model for the performance simulation of an automotive turbocharger”, SAE 870295.
- Sokolov, A.A., Glad, S.T., “Identifiability of turbocharged IC engine models”, SAE 1999.
- Watson, N., “Dynamic turbocharged diesel engine simulator for electronic control system development”, Journal of Dynamic Systems, Measurement, and Control 106, pp.27-45, 1984.
- Watson, N., Janota, M.S., “Turbocharging the internal combustion engine”, John Wiley & Sons, 1982.
- Winkler, G., “Steady state and dynamic modeling of engine turbomachinery systems”, PhD Thesis, University of Bath, 1977.