Automotive engine power performance tuning under numerical and nominal data

Control Engineering Practice

Volumn 20, Issue 3, 2012, Pages 300-314

  Wong, Pak Kin ^a   Tam, Lap Mou ^a   Ke, Li ^a  

^a UNIVERSITY OF MACAU   (Macao)

Author keywords

Engine power performance; Multi input multi output least squares support vector machines; Multi objective optimisation; Nominal data

Indexed keywords

AUTOMOTIVE ENGINE; CODE SYNTHESIS; ELECTRONIC CONTROL UNITS; ENGINE COMPONENTS; ENGINE MODELS; ENGINE PARTS; ENGINE PERFORMANCE; ENGINE POWER; LEAST SQUARES SUPPORT VECTOR MACHINES; MULTIINPUT; MULTIPLE INPUTS; NOMINAL DATA; PARTICLE SWARM OPTIMISATION; QUASI-NEWTON METHODS; REMAPPING; TRIAL-AND-ERROR METHOD;

COMPUTER HARDWARE; CONTROL; IMAGE RETRIEVAL; LEAST SQUARES APPROXIMATIONS; MACHINE DESIGN; MULTIOBJECTIVE OPTIMIZATION; NEWTON-RAPHSON METHOD; NUMERICAL METHODS; POWER ELECTRONICS; SUPPORT VECTOR MACHINES; TUNING;

ENGINES;

EID: 84856452836     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2011.11.006     Document Type: Article

References (29)

1. Bell A.G. Four-stroke performance tuning: A practical guide 2006, Haynes Publishing Group. 3rd ed.
2. Celik V., Arcaklioglu E. Performance maps of a diesel engine. Applied Energy 2005, 81:247-259.
3. Clerc M., Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 2002, 6(1):58-73.
4. Cristianini N., Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods 2000, Cambridge University Press. 1st ed.
5. Dickinson P., Shenton A.T. Dynamic calibration of fuelling in the PFI SI engine. Control Engineering Practice 2009, 17:26-38.
6. Errico G.D., Cerri T., Pertusi G. Multi-objective optimization of internal combustion engine by means of 1D fluid-dynamic models. Applied Energy 2011, 88:767-777.
7. Garcia-Nieto S., Martinez M., Blasco X., Sanchis J. Nonlinear predictive control based on local model networks for air management in diesel engines. Control Engineering Practice 2008, 16:1399-1413.
8. Haykin S. Neural networks: A comprehensive foundation 1999, Prentice-Hall. 2nd ed.
9. Hu, X., Eberhart, R. C., & Shi, Y. (2003). Engineering optimization with particle swarm. In Proceedings of the IEEE swarm intelligence symposium (pp. 53-57).
10. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the international conference on neural networks (pp. 1942-1948).
11. Li, G.Y. (2007). Application of intelligent control and MATLAB to electronically controlled engines (1st ed.), Publishing House of Electronics Industry. (in Chinese).
12. Lunani M., Sudjianto A., Johnson P.L. Generating efficient training samples for neural networks using Latin hypercube sampling. Proceedings of the Artificial Neural Networks in Engineering 1995, 209-214.
13. Pyle D. Data preparation for data mining 1999, Morgan Kaufmann. 1st ed.
14. Pulkrabek W.W. Engineering fundamentals of the internal combustion engine 2004, Prentice Hall, Pearson. 2nd ed.
15. Qi K.P., Feng L.Y., Leng X.Y., Du B.G., Long W.Q. Simulation of quasi-dimensional combustion model for predicting diesel engine performance. Applied Mathematical Modelling 2011, 35:930-940.
16. Rask, E., & Sellnau, M. (2004). Simulation-based engine calibration: tools, techniques, and applications. SAE paper no. 2004-01-1264.
17. Refaat M. Data preparation for data mining using SAS 2007, Morgan Kaufmann.
18. Robert Bosch GmbH (2004). Gasoline-engine management (2nd ed.). Robert Bosch GmbH and Professional Engineering Publishing Limited.
19. Saerens B., Vandersteen J., Persoons T., Swevers J., Diehl M., Van den Bulck E. Minimization of the fuel consumption of a gasoline engine using dynamic optimization. Applied Energy 2009, 86:1582-1588.
20. Suykens J., Gestel T., De Brabanter J., De Moor B., Vandewalle J. Least squares support vector machines 2002, World Scientific. 1st ed.
21. Tan L.G., Gong J.K., Zhai L.Q., Liu J.W. Modeling and simulation of fuel-injection MAP of electronically controlled engine. Automotive Engineering 2006, 28(7):630-633. (In Chinese).
22. Togun N.K., Baysec S. Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks. Applied Energy 2010, 87:349-355.
23. Trelea I.C. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters 2003, 85(6):317-325.
24. Vapnik V. Statistical learning theory 1998, John Wiley, New York. 1st ed.
25. Vong C.M., Wong P.K., Li Y.P. Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference. Engineering Applications of Artificial Intelligence 2006, 19(3):227-287.
26. Wong P.K., Tam L.M., Li K., Wong H.C. Automotive engine idle speed control optimization using least squares support vector machines and genetic algorithm. International Journal of Intelligent Computing and Cybernetics 2008, 1(4):598-616.
27. Wu B., Filipi Z., Prucka R., Kramer D., Ohl G. A simulation-based approach for developing optimal calibrations for engines with variable valve actuation. Oil & Gas Science and Technology-Revue de l'IFP 2007, 62:539-553.
28. Zhang D., Xu Z., Mechefske C.M., Xi F. Optimum design of parallel kinematic toolheads with genetic algorithms. Robotica 2004, 22:77-84.
29. Zhang J.Y., Shen T.L., Marino R. Model-based cold-start speed control scheme for spark ignition engines. Control Engineering Practice 2010, 18:1285-1294.