This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Neural Network Based Fast-Running Engine Models for Control-Oriented Applications
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 11, 2005 by SAE International in United States
Annotation ability available
A structured, semi-automatic method for reducing a high-fidelity engine model to a fast running one has been developed. The principle of this method rests on the fact that, under certain assumptions, the computationally expensive components of the simulation can be substituted with simpler ones. Thus, the computation speed increases substantially while the physical representation of the engine is retained to a large extent. The resulting model is not only suitable for fast running simulations, but also usable and updatable in later stages of the development process. The thrust of the method is that the calibration of the fast running components is achieved by use of automatically selected neural networks. Two illustrative examples demonstrate the methodology. The results show that the methodology achieves substantial increase in computation speed and satisfactory accuracy.
CitationPapadimitriou, I., Warner, M., Silvestri, J., Lennblad, J. et al., "Neural Network Based Fast-Running Engine Models for Control-Oriented Applications," SAE Technical Paper 2005-01-0072, 2005, https://doi.org/10.4271/2005-01-0072.
- Chen, S., Cowan C.F.N., Grant, P.M., “Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks”, IEEE Transactions on Neural Networks, vol. 2, No. 2, March 2001, pp. 302-309.
- Hagan M. T., and Menhaj M., “Training feedforward networks with the Marquardt algorithm,” IEEE Transactions on Neural Networks, vol. 5, no.6, 1994, pp. 989-993.
- Heywood, J.B., “Internal Combustion Engine Fundamentals”, McGraw-Hill Inc., 1988.
- Isaacson, E., Keller, H.B., “Analysis of Numerical Methods”, John Wiley & Sons Inc, 1966.
- Karayiannis, N. B., Mi, W., “Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques,” IEEE Transactions on Neural Networks, vol. 8, no. 6, pp. 1492-1506, 1997
- Kohonen, T., “Self-organizing feature maps”, Springer Verlag, 1995.
- Krug, C., Liebl, J., Munk, F., Kammer, A., Reuss, H., “Physical Modelling and Use of Modern System Identification for Real-Time Simulation of Spark Ignition Engines in all Phases of Engine Development”, SAE World Congress, SAE No. 2004-01-0421, Detroit, MI, 2004.
- Pearson, R.K., Ogunnaike, B.A., Doyle, F.J., “Identification of Structurally Constrained Second-order Volterra Models”, IEEE Transactions on Signal Processing, vol. 44, 1996, p. 2837-2846.
- Principe, J.C., Wang, L., Motter M.A.“,Local Dynamic Modeling with Self-Organizing Maps and Applications to Nonlinear System Identification and Control”, Proceedings of IEEE, vol. 86, Nov. 1998, pp. 2240-2258.
- Shin, M., Sargent, R.G., Goel, A.L., “Gaussian Radial Basis Functions For Simulation Metamodeling”, Proceedings of the 2002 Winter Simulation Conference, pp. 483-488.
- Winsel, T., Ayeb, M., Theuerkauf, H.J., Pischinger, S., Schernus, C., Lutkemeyer, G., “HiL-Calibration of SI Engine Cold Start and Warm-Up Using Neural Real-Time Model”, SAE World Congress, SAE No. 2004-01-1362, Detroit, MI, 2004.