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Investigation of Black Box Modeling Approaches for Representation of Transient Gearshift Processes in Automotive Powertrains with Automatic Transmission
Technical Paper
2015-01-1143
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In this investigation two different nonlinear dynamic black box modelling approaches are compared. The purpose of the models is to reproduce the transient gearshift process. The models are used to compute the torque at the sideshafts, which is highly correlated to the gearshift comfort. The first model is a Gaussian process (GP) model. The GP is a probabilistic, non-parametric approach, which is additionally capable to compute the confidence interval of the simulated output signal. The second black box model uses the artificial neural net (ANN) approach. In addition to training algorithms the resulting model configurations for both black box approaches are shown in this investigation. Furthermore the empirical error of both modeling approaches is compared to the predictive variance of the GP model and to the intrinsic uncertainty of the gearshift process.
This research demonstrates that black box models are capable of representing the torque at the sideshafts of a transient gearshift process. The results show that both investigated black box approaches yield a sufficient accuracy. Nonetheless the models have their own characteristic advantages, so that the appropriate model approach should be chosen according to the individual intention.
Authors
Citation
Rot, I., Plöger, D., and Rinderknecht, S., "Investigation of Black Box Modeling Approaches for Representation of Transient Gearshift Processes in Automotive Powertrains with Automatic Transmission," SAE Technical Paper 2015-01-1143, 2015, https://doi.org/10.4271/2015-01-1143.Also In
References
- Hagerodt , A. Automatisierte Optimierung des Schaltkomforts von Automatikgetrieben Ph.D. thesis TU Braunschweig Braunschweig 2003
- Ažman K. and Kocijan J. Application of gaussian processes for black-box modelling of biosystems Proceedings of the ISA Transactions 46 4 443 457 June 2007 10.1016/j.isatra.2007.04.001
- Tietze , N. and Konigorski , U. and Nguyen-Tuong , D. Local Gaussian process regression for model-based calibration of engine control units Proceedings of the Simulation and Testing for Automotive Electronics V Berlin 2014
- Tan , Y. , Saif , M. Neural-networks-based nonlinear dynamic modeling for automotive engines Proceedings of the Neurocomputing 30 1-4 129 142 Jan. 2000 10.1016/S0925-2312(99)00121-6
- Ng , B. C. , Mat Darus , I. Z. , Jamaluddin , H. , Kamar , H. M. Dynamic modelling of an automotive variable speed air conditioning system using nonlinear autoregressive exogenous neural networks Proceedings of the Applied Thermal Engineering 2014 10.1016/j.applthermaleng.2014.08.043
- Förster , H. J. Automatische Fahrzeuggetriebe Springer-Verlag Berlin Heidelberg 978-3540522287 234 236 1991
- Zaglauer , S. The Evolutionary Algorithm SAMOA with Use of Design of Experiments Proceedings of the 14th annual conference companion on Genetic and evolutionary computation 637 638 2012 10.1145/2330784.2330897
- Bishop C. M. Pattern Recognition and Machine Learning Springer Science + Business Media New York 978-0-387-31073-2 2006
- Rasmussen C. E. and Williams C. K. I. Gaussian Processes for Machine Learning The MIT Press 2005
- Girard A. Approximate Methods for Propagation of Uncertainty with Gaussian Process Models Ph.D. thesis University of Glasgow Glasgow 2004
- Thompson K. R. Implementation of Gaussian process models for non-linear system identification Ph.D-thesis University of Glasgow Glasgow 2009
- Rasmussen C. E. and Nickisch H. Gaussian processes for machine learning (GPML) toolbox Proceedings of the Journal of Machine Learning Research 11 3011 3015 2010
- Cybenko G. Approximation by superpositions of a sigmoidal function Proceedings of the Mathematics of Control, Signals, and Systems 2 4 303 314 1989 10.1007/BF02551274
- Marquardt D. W. An algorithm for least-squares estimation of nonlinear parameters Proceedings of the Journal of the Society for Industrial and Applied Mathematics 11 2 431 441 June 1963 10.1137/0111030
- Hyndman R. J. and Koehler A. B. Another look at measures of forecast accuracy International Journal of Forecasting 22 4 679 688 Oct. Dec. 2006 10.1016/j.ijforecast.2006.03.001