Incremental modelling of automotive engine performance using LS-SVM

Modern automotive engines are controlled by the electronic control unit (ECU). The engine performance referred to as output torque is significantly affected by the setup of control parameters in the ECU. Traditional ECU tune-up is done by trial-and-error method through repeated dynamometer tests. LS-SVM (Least Squares Support Vector Machines) is a powerful machine learning technique which can handle complex and nonlinear function estimation problems. It was employed to estimate the above engine performance function. However, current LS-SVM is an offline algorithm, i.e., the estimated torque functions built from LS-SVM can not be updated with the subsequent expensive dynamometer tests for verification. In the paper, online LS-SVM is presented and used for estimating the engine torque functions for precision prediction so that the number of dynamometer tests can be significantly reduced.