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Black Box Dynamic Modeling of a Gasoline Engine for Constrained Model-Based Fuel Economy Optimization
Technical Paper
2015-01-1618
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
New environmental legislation on emission and fuel efficiency targets increasingly requires good transient engine performance and this in turn means that the previously acceptable static engine calibration and control methodologies based on steady-state testing must be re-placed by dynamical optimization using dynamical models. Although many advances have been made in predictive models for internal combustion engines, the phenomena involved are so many, complex and nonlinear that dynamical black-box models typically employing neural network structures must be determined from system identification through experimental testing. Such identified dynamical models are required to provide high accuracy multiple step-ahead predictions of emissions but must accordingly also be compactly implementable for speed and memory to allow for the required large scale optimization involving possibly many thousands of iterations.
This paper presents a novel methodology of using black box modeling techniques to build compact efficiently implementable nonlinear dynamic engine models with high predictive accuracy in the form of Neural Network and polynomial equations. The black box models obtained are shown to be efficient for state-of-the-art model-based fuel economy dynamical optimization with emission constraints. The effectiveness and relative efficiency of using polynomial models V.S. full Neural Network (NN) models in the fuel economy optimization are demonstrated. A novel multi-step ahead (simulation) output based parameter estimation method is proposed to improve the predictive accuracy of polynomial models.
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Fang, K., Li, Z., Shenton, A., Fuente, D. et al., "Black Box Dynamic Modeling of a Gasoline Engine for Constrained Model-Based Fuel Economy Optimization," SAE Technical Paper 2015-01-1618, 2015, https://doi.org/10.4271/2015-01-1618.Also In
References
- Guzzella , L. and Onder , C.H Introduction to Modeling and Control of Internal Combustion Springer-Verlag 2004
- Sun , J. , Kolmanvosky , I. , Cook , J.A and Buckland , J.H Modeling and Control of Automotive Powertrain Systems: a Tutorial In Proceedings of American Control Conference 5 2005 3271 3283
- Tan , Y. and Saif , M. Nonlinear Dynamic Modeling of Automotive Engines Using Neural Networks In Proceeding of the IEEE International Conference on Control Application 1997 408 410
- Saraswati , S. Reconstruction of Cylinder Pressure for S.I. Engine Using Recurrent Neural Network Journal of Neural Computing and Applications 19 2010 935 994
- Xia , Y. , Hao , G. , Shan , C. , Ni , Z. and Zhang , W. Reconstruction of Cylinder Pressure of I.C. Engine Based on Neural Networks In Proceedings of the 1 st International Conference on Pervasive Computing, Signal Processing and Applications 2010 924 927
- Arsie , I. , Marotta , M.M. , Pianese , C. and Sorrentino , M. Experimental Validation of a Recurrent Neural Network for Air-Fuel Ratio Dynamic Simulation in SI IC Engines In Proceedings of ASME International Mechanical Engineering Congress and Exposition 2004 127 136
- Hou , Z. , Sen , Q. and Wu , Y. Air-Fuel Ratio Identification of Gasoline Engine During Transient Conditions Based on Elman Neural Networks In Proceedings of the 6 th International Conference on Intelligent Systems Design and Applications 1 2006 32 36
- Luh , G.C. and Wu , C.Y. Inversion Control of Non-linear Systems with an Inverse narx model identified using genetic algorithms In Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 214 2000 259 271
- Billings , S.A and Chen , S. The Identification of Linear and Non-linear Models of a Turbocharged Automotive Diesel Engine Mechanical Systems and Signal Processing 3 1989 123 142
- Hirsch , M. and Re , L. Sequential Identification of Engine Subsystems by Optimal Input Design SAE Int. J. Engines 2 2 562 569 2010 10.4271/2009-24-0132
- Butt , Q.R Estimation of Gasoline-Engine Parameters Using Higher Order Sliding Mode IEEE Transactions on Industrial Electronics 55 2008 3891 3898
- Du , Q.Y. , Ni , J.M. , Chen , M. , Zhang , X.M. and Ping , Y.S. The Application of DOE in Engine Design In Proceedings of the 6 th Conference on Design of Experiments in Engine Development 2011 136 153
- Gaikwad and Rivera , D. Control-relevant Input Signal Design for Multivariable System Identification: Application to High-purity Distillation In Proceedings of the 13 th IFAC World Congress 1996 349 354
- Priddy , K.L. and Keller , P.E. Artificial Neural Networks: an Introduction Bellingham SPIE 2005
- Nelles , O. Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models Springer-Verlag Berlin Heidelberg 2001
- Mathwork Optimization Toolbox Matlab 2010b