Prediction and control of knock at high load boundary for HCCI engine based on neural network

Abstract

Homogenous Charge Compression Ignition (HCCI) have drawn lots of attentions due to its high efficiency and low emissions. But HCCI engines are prone to random abnormal combustion phenomena at load boundaries. In this paper, innovatively implement online prediction and control of knock at high load boundary for HCCI utilizing only cylinder pressure as the diagnostic parameter based on neural networks. The results show that the neural network model with previous cycle heat release, previous cycle crank angle at 50 % cumulative heat release (CA50), peak cylinder pressure during previous and current cycle negative valve overlap (NVO) as inputs can realize the prediction of 87.5 % early combustion cycle in the intake phase before the early combustion occurs, and the diagnosis accuracy is 89.4 %. The frequency of early combustion can be reduced from 2.3 % to 0.3 % with the help of prediction model combining water injection and fuel injection reduction control, and no knocking occurs. The high load boundary of the HCCI engine is expanded by 7.1 % from 0.366 MPa to 0.392 MPa at 1500 r/min with the help of this control strategy.