Multi-objective optimization of ethanol fuelled HCCI engine performance using hybrid GRNN–PSO

Abstract

In this research study, the ethanol-fuelled homogeneous charge compression ignition (HCCI) engine operating conditions were optimized based on its performance and emission parameters. However, there is no proper relation among these performance and emission parameters. Therefore, it is a challenging task to optimize the HCCI engine. For this purpose, a hybrid generalised regression neural network (GRNN)–particle swarm optimisation (PSO) model was designed to optimize three input parameters, including the charge temperature, engine load, and EGR rate. The GRNN based prediction tool has been introduced to obtain the relation between input and output parameters. Initially, PSO generated a random particle and given as input to GRNN. The GRNN carries a nonlinear regression analysis among all the input and output parameters of the ethanol fuelled HCCI engine. The optimum kernel bandwidth in the GRNN model was determined by the grid search method for reducing the cross-validation error. The output of the GRNN regarding performance and emission parameters was given to the PSO algorithm to optimize the HCCI engine operating conditions. The developed algorithm has the flexibility to optimize the engine performance for any user-defined weights. The optimum HCCI engine operating conditions for the general criteria were found to be 170°C charge temperature, 72% engine load, and 4% EGR. The proposed model obtained the optimized HCCI engine input parameters within the short span of time i.e., 60–75ms. The new hybrid GRNN–PSO algorithm is anticipated to accomplish as a useful tool for a rapid engine optimization.