A Hybrid Approach Using Design of Experiment and Artificial Neural Network in a Camless Heavy-Duty Engine

Abstract

Abstract Increasingly stricter emission regulations and fleet CO2 targets drive the engine development toward clean combustion and high efficiency. To achieve this goal, planning and conducting experiments in a time- and cost-effective way play a vital role in finding the optimal combinations of all selectable parameters. This study investigated the effects of five engine parameters on two engine-out responses in a camless variable valve actuation (VVA) heavy-duty engine. Five engine parameters were intake valve lift (IVL), inlet valve closing (IVC), injection pressure, start of injection (SOI), and exhaust gas recirculation (EGR). Initially, a design of experiment (DoE) model was generated to predict both engine-out responses: brake-specific fuel consumption (BSFC) and BSNOx emissions. Due to a poor fit of the BSFC regression model from DoE analysis, an artificial neural network (ANN) model was developed to predict BSFC instead. A d-optimal design with five engine parameters at five levels was used to design the experiment. Extra test points together with d-optimal design points were utilized to train the ANN model. The well-trained ANN model for BSFC and DoE model for BSNOx were combined with a genetic algorithm (GA) to generate the Pareto-optimal front. The results proved the concept of using a hybrid statistical approach (DoE + ANN) with GA as an effective tool to generate a range of compromise design solutions. By extracting designs along the Pareto-optimal front, the impact of engine parameters on the system can be explained.