Application of the Newly Developed KLSA Model into Optimizing the Compression Ratio of a Turbocharged SI Engine with Cooled EGR

Owing to the stochastic nature of engine knock, determination of the knock limited spark angle (KLSA) is difficult in engine cycle simulation. Therefore, the state-of-the-art knock modeling is mostly limited to either merely predicting knock onset (i.e. auto-ignition of end gas) or combining a simple unburned mass fraction (UMF) model representative of knock intensity (KI). In this study, a newly developed KLSA model, which takes both predictions of knock onset and intensity into account, is firstly introduced. Multiple variables including the excess air ratio, EGR ratio, cylinder pressure and the end gas temperature are included in the knock onset model. Based on the auto-ignition theory of hot spots in end gas, both the energy density and heat release rate in hot spots are taken into consideration in the KI model. Assuming the lognormal distribution of KI in consecutive cycles, the knock factor based on the likelihood ratio is employed as the criterion for definition of knocking cycles. After validation of the KLSA model with the experimental data, the geometric compression ratio of a boosted port fuel injection spark ignition (SI) engine modified with cooled EGR is optimized by using a strategy combining the artificial neural networks (ANNs) and genetic algorithm (GA) with the one-dimensional engine cycle simulation. The results reveal that the newly developed model predicts the KLSA better than the existing other models in the engine cycle simulations. With combined optimization of the geometric compression ratio and the operative control variables including the spark timing, intake valve closure, EGR ratio and so on, the engine thermal efficiency is improved by 2-8% at the most frequently operated points.