This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Genetic Algorithm for Dynamic Calibration of Engine's Actuators
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 16, 2007 by SAE International in United States
Annotation ability available
Modern diesel engines are equipped with an increasing number of actuators set to improve human comfort and fuel consumptions while respecting the restricted emissions regulations. In spite of the great progress made in the electronic and data-processing domains, the physical-based emissions models remain time consuming and too complicated to be used in a dynamic calibrating process. Therefore, until these days, the calibration of the engine's cartographies is done manually by experimental experts on dynamic test bed, but the results are not often the best compromise in the consumption-emissions formula due to the increasing number of actuators and to the nonlinear and complex relations between the different variables involved in the combustion process. Recently, neural networks are successfully used to model dynamic multiple inputs - multiple outputs processes by learning from examples and without any additional or detailed information about the process itself.
In this paper, we fully describe the construction and applications of a nine inputs dynamic emissions' model based on neural networks. The simulations' results are in good agreement with real engine data measured on test bed. The emissions' model is conceived to be used in an upper-level dynamic optimization process based on genetic algorithm. Our goal is to present, while using the minimum number of experimental tests, a fast and practical optimization procedure capable of finding the optimal calibration values of the seven engine's actuators over the New European Driving Cycle (NEDC). The results are very promising.
CitationOmran, R., Younes, R., Champoussin, J., Fedeli, D. et al., "Genetic Algorithm for Dynamic Calibration of Engine's Actuators," SAE Technical Paper 2007-01-1079, 2007, https://doi.org/10.4271/2007-01-1079.
- Stefanopoulou A. Kolmanovsky I.V. Freudenberg J.S. “Control of variable geometry turbocharged Diesel engines for Reduced Emissions” IEEE Transactions on Control System Technology 8 4 2000
- Guerrassi N. Dupraz P. “A Common Rail Injection System For High Speed Direct Injection Diesel Engines” SAE technical paper 980803 1998
- Gao Z. Schreiber W. “The effect of egr and split fuel injection on Diesel engine emission” International Journal of Automotive Technology 2 4 123 133 2001
- George S. Morris G. Dixon J. Pearce D. Heslop G. “Optimal boost control for an electrical supercharging application” SAE technical paper 2004-01-0523 2004
- Mital R. Li J. Huang S.C. Stroia B.J. YU R.C. “Diesel exhaust emissions control for light duty vehicles” SAE technical paper 2003-01-0041 2003
- Olsson L. Abul-Milh M. Karlsson H. Jobson E. Thormahlen P. Hinz A. “The effect of a changing lean gas composition on the ability of NO2 formation and NOx reduction over supported Pt catalysts” Topics in catalysis 30 31 1 85 90 2004
- Hong S. Assanis D.N. Wooldridge M.S. Hong G.I. “Modeling of Diesel combustion and NO emissions based on a modified Eddy dissipation concept” SAE technical paper 04P-273 2004
- Jung D. Assanis D.N. “Multi-Zone DI Diesel spray combustion model for cycle simulation studies of engine performance and emissions” SAE technical paper 2001-01-1246 2001
- Rakopoulos C.D. Rakopoulos D.C. Kyritsis D.C. “Development and validation of a comprehensive two-zone model for combustion and emissions formation in a DI diesel engine” International Journal of Energy Research 27 1221 1249 2003
- Brahma I. He Y. Rutland C.J. “Improvement of neural network accuracy for engine simulations” SAE technical paper 2003-01-3227 2003
- Wu B. Filipi Z. Assanis D. “Using artificial neural networks for representing the air flow rate through a 2.4 liter VVT engine” SAE technical paper 2004-01-3054 2004
- Traver M.L. Atkinson R.J. Atkinson C.M. “Neural network-based Diesel engine emissions prediction using in-cylinder combustion pressure” 1999
- Hafner M. “Model based determination of dynamic engine control function parameters” SAE technical paper 01FL-319 2001
- Nieuwstadt M.J. Kolmanovsky I.V. Moraal P.E. “Coordinated EGT-VGT control Diesel engines: an experimental comparison” SAE technical paper 2000-01-0266 2000
- Bai L. Yang M. “Coordinated control of EGR and VNT in turbocharged Diesel engine based on Intake air mass observer” SAE technical paper 2002-01-1292 2002
- Ammann M. Fekete N.P. Guzzella L. Glattfelder A.H. “Model-based control of the VGT and EGR in a turbocharged common-rail Diesel engine: theory and passenger car implementation” SAE technical paper 2003-01-0357 2003
- Zweiri Y.H. “Diesel engine indicated torque estimation based on artificial neural networks” International Journal of Intelligent Technology 1 3 233 239 2006
- Li D. Lu D. Kong X. Wu G. “Implicit curves and surfaces based on BP neural network” Journal of Information & Computational Science 2 2 259 271 2005
- Hiroyasu T. Miki M. Kamiura J. Watanabe S. “Multi-objective optimization of Diesel engine emissions and fuel economy using genetic algorithms and phenomenological model” SAE technical paper 02FFL-183 2002
- De Risi A. Donateo D. Laforgia D. “Optimisation of the combustion chamber of direct injection Diesel engines” SAE technical paper 2003-01-1064 2003
- De Risi A. Donateo D. Paolo C. Ficarella A. “A combined optimization method for common rail Diesel engines” ASME 2002