This paper proposes a novel modelling and optimization approach for steady state and transient performance tune-up of an engine at idle speed. In terms of modelling, Latin hypercube sampling and multiple-input and multiple-output (MIMO) least-squares support vector machines (LS-SVMs) are proposed to build an engine idle-speed model based on experimental sample data. Then, a genetic algorithm (GA) and particle swarm optimization (PSO) are applied to obtain an optimal electronic control unit setting automatically, under various user-defined constraints. All of the above techniques mentioned are artificial intelligence techniques. To illustrate the advantages of the MIMO LS-SVM, a traditional multilayer feedforward neural network (MFN) is also applied to build the engine idle-speed model. The modelling accuracies of the MIMO LS-SVM and MFN are also compared. This study shows that the predicted results using the estimated model from the LS-SVM are in good agreement with the actual test results. Moreover, both the GA and PSO optimization results show an impressive improvement on idle-speed performance in a test engine. The optimization results also indicate that PSO is more efficient than the GA in an idle-speed control optimization problem based on the LS-SVM model. As the proposed methodology is generic, it can be applied to different engine modelling and control optimization problems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.