Investigation on the effect of boost pressure on performance characteristics of a diesel engine

Abstract

To predict the effect of boost pressure on the performance characteristics of a single cylinder diesel engine, a least squares support vector machine (LSSVM) method based on genetic algorithm (GA) is proposed in this study. The GA algorithm is employed to optimize the regularization and kernel parameters of the LSSVM. In order to develop the proposed GA-LSSVM model, a dataset including the intake pressure, speed, and power is used for evaluating indices, while one of the engine performance characteristics (e.g. efficiency, brake mean effective pressure, and brake specific fuel consumption) is the output. Further, the predictions from the proposed GA-LSSVM model are compared with those from an adaptive neuro-fuzzy inference system (ANFIS) model. The results show that the proposed GA-LSSVM model has potential in accurately predicting the effect of boost pressure on the different engine characteristics.