Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference

Authors:
Chi-Man Vong;Pak-Kin Wong;Yi-Ping Li
Affiliations:
Department of Computer and Information Science, University of Macau, P.O. Box 3001, Macau, China;Department of Electromechanical Engineering, University of Macau, P.O. Box 3001, Macau, China;Department of Computer and Information Science, University of Macau, P.O. Box 3001, Macau, China
Venue:
Engineering Applications of Artificial Intelligence
Year:
2006

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Abstract

Automotive engine power and torque are significantly affected with effective tune-up. Current practice of engine tune-up relies on the experience of the automotive engineer. The engine tune-up is usually done by trial-and-error method, and then the vehicle engine is run on the dynamometer to show the actual engine output power and torque. Obviously, the current practice costs a large amount of time and money, and may even fail to tune up the engine optimally because a formal power and torque model of the engine has not been determined yet. With an emerging technique, least squares support vector machines (LS-SVM), the approximated power and torque model of a vehicle engine can be determined by training the sample data acquired from the dynamometer. The number of dynamometer tests for an engine tune-up can therefore be reduced because the estimated engine power and torque functions can replace the dynamometer tests to a certain extent. Besides, Bayesian framework is also applied to infer the hyperparameters used in LS-SVM so as to eliminate the work of cross-validation, and this leads to a significant reduction in training time. In this paper, the construction, validation and accuracy of the functions are discussed. The study shows that the predicted results using the estimated model from LS-SVM are good agreement with the actual test results. To illustrate the significance of the LS-SVM methodology, the results are also compared with that regressed using a multilayer feed forward neural networks.