Methodology to standardize and improve the calibration process of a 1D model of a GTDI engine

Modeling techniques are one of most useful tools during the first stages of the ICE design. They allow to analyze and predict the entire engine performance, reduce costs and resources and surely accelerate its development. For that purpose, a former and suitable calibration process is needed in order to attain reliable and robust models. Therefore, in this work a methodology is proposed to standardize and improve the calibration of a whole 1D engine model. In this case, it has been applied to a gasoline, turbocharged, direct injection engine with variable nozzle turbine and variable valve timing technologies. This calibration procedure is mainly distinguished by insulating the different engine parts, decoupling the turbocharger, using PI controls to find fitting parameters and checking and validating mean and instantaneous variables related to flow conditions inside the engine. Moreover, it requires experimental data and a previous combustion analysis of some steady operating points. The methodology is completed with the determination of fitting correlations to estimate heat losses and pressure drops in engine systems. It also includes the training of an Artificial Neural Network (ANN) to predict the combustion process and their following insertion into the model and final validation. This validation is performed not only in steady state engine conditions but also in transient operation.