Automotive engine tuning using least-squares support vector machines and evolutionary optimization

University of Macau === Faculty of Science and Technology === Department of Electromechanical Engineering

Bibliographic Details
Main Author:	Li, Ke
Language:	English
Published:	University of Macau 2012
Subjects:	Mechanical engineering Automobiles > Design and construction Automobiles > Motors > Modification Least squares Algorithms Electromechanical Engineering > Department of Electromechanical Engineering
Online Access:	http://umaclib3.umac.mo/record=b2580667

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spelling	ndltd-MACAU-oai-libdigital.umac.mo-b25806672013-01-07T23:07:36Z2012http://umaclib3.umac.mo/record=b2580667UM_THESESUniversity of MacauFaculty of Science and TechnologyDepartment of Electromechanical EngineeringUniversity of MacauMechanical engineeringAutomobiles -- Design and constructionAutomobiles -- Motors -- ModificationLeast squaresAlgorithmsElectromechanical Engineering -- Department of Electromechanical EngineeringengLi, KeAutomotive engine tuning using least-squares support vector machines and evolutionary optimizationModern automotive engines are controlled by electronic control units (ECUs), and the engine performance is significantly affected by the setup of the control parameters in the ECU and the selection of engine parts. Engine tuning is the adjustment or modification of ECU parameters and engine parts to yield optimal performance subject to different user requirements. The current practice of engine tuning relies on the experience of automotive engineers. An engine tuning is usually done by a trial-and-error method. Obviously, the current practice consumes large amounts of time and money. The problem can be solved by developing a mathematical engine model and optimizer for engine setup optimization. This research proposes a novel modeling and optimization approach for engine tuning. In the first phase of the approach, Latin hypercube sampling and multiple-input and multiple-output (MIMO) least-squares support vector machines (LS-SVMs) are proposed to create an engine performance model based on experimental sample data, and then typical deterministic optimization (such as quasi-Newton method) and evolutionary optimization (such as genetic algorithm, and particle swarm optimization) are applied to the engine model produced to automatically determine the optimal engine performance subject to different user-specific constraints. Both deterministic and evolutionary optimization techniques are examined in sake of comparison purpose. In addition, an equal and fair data pre-processing technique, namely one-of-n remapping and a simple binary code synthesis rule are proposed to handle engine-part selection issue in this research, because engine parts are complicated objects which are usually represented as nominal data. These data are meaningless values in terms of computation. By integrating such a data pre- processing technique with the MIMO LS-SVM modeling framework, engine performance can be simulated under different combinations of engine parts and ECU parameters. Since engine idle-speed control and power performance optimization are typical automotive engine dynamic and static performances, respectively, engine idle-speed control and power performance optimization are selected for case studies to demonstrate the feasibility and efficiency of the novel modeling and optimization approach. Both experimental and simulation results show that the proposed methodology can successfully produce optimal engine setup.
collection	NDLTD
language	English
sources	NDLTD
topic	Mechanical engineering Automobiles -- Design and construction Automobiles -- Motors -- Modification Least squares Algorithms Electromechanical Engineering -- Department of Electromechanical Engineering
spellingShingle	Mechanical engineering Automobiles -- Design and construction Automobiles -- Motors -- Modification Least squares Algorithms Electromechanical Engineering -- Department of Electromechanical Engineering Li, Ke Automotive engine tuning using least-squares support vector machines and evolutionary optimization
description	University of Macau === Faculty of Science and Technology === Department of Electromechanical Engineering
author	Li, Ke
author_facet	Li, Ke
author_sort	Li, Ke
title	Automotive engine tuning using least-squares support vector machines and evolutionary optimization
title_short	Automotive engine tuning using least-squares support vector machines and evolutionary optimization
title_full	Automotive engine tuning using least-squares support vector machines and evolutionary optimization
title_fullStr	Automotive engine tuning using least-squares support vector machines and evolutionary optimization
title_full_unstemmed	Automotive engine tuning using least-squares support vector machines and evolutionary optimization
title_sort	automotive engine tuning using least-squares support vector machines and evolutionary optimization
publisher	University of Macau
publishDate	2012
url	http://umaclib3.umac.mo/record=b2580667
work_keys_str_mv	AT like automotiveenginetuningusingleastsquaressupportvectormachinesandevolutionaryoptimization
_version_	1716479235231580160

Automotive engine tuning using least-squares support vector machines and evolutionary optimization

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