Abstract
Keywords: Idle speed control, Least squares support vector machines, Control optimization, Genetic algorithm
Nowadays, automotive engines are controlled by the electronic control unit (ECU), and the engine idle speed performance is significantly affected by the setup of control parameters in the ECU. Usually, the engine ECU tune-up is done empirically through tests on dynamometer (dyno). In this way, a lot of time, fuel and human resources are consumed, while the optimal control parameters may not be obtained. This paper presents a novel ECU setup optimization approach for engine idle speed control. In the first phase of the approach, Least Squares Support Vector Machines (LS-SVM) is proposed to build up an engine idle speed model based on dyno test data, and then genetic algorithm (GA) is applied to obtain optimal ECU setting automatically subject to various user-defined constraints. The study shows that the predicted results using the estimated model from LS-SVM are in good agreement with the actual test results. Moreover, the optimization results show a significant improvement on idle speed performance in a test engine.