Experiment analysis and computational optimization of the Atkinson cycle gasoline engine through NSGA Ⅱ algorithm using machine learning

摘要

This paper is pioneered in developing digital twins by GT-Power software and multi-objective evolutionary optimization (MOEO) using NSGA II algorithm for an Atkinson cycle gasoline engine under China VI emissions standards. Firstly, an experimental investigation is conducted and relevant experimental data is obtained. Based on this, the corresponding 1D GT-Power simulation model is established and calibrated according to the obtained test data. Secondly, the four decision variables including the spark advance angle (SA), exhaust gas recirculation (EGR) rate, exhaust variable valve timing (VVT-E) and intake variable valve timing (VVT-I) are input into the simulation model, the optimal values of the decision variables will be determined via MOEO to minimize NOx emissions and the brake specific fuel consumption (BSFC). Thirdly, based on the data obtained from the scanning test, a machine learning method is used to build an engine performance prediction model through the support vector machine (SVM) regression algorithm. The inputs (control parameters obtained from the optimization process) including SA, EGR, VVT-I and VVT-E are imported to predict the performance output of the engine. The results show that under the engine control parameters obtained by the NSGA II algorithm, the simulation values of engine performance parameters have been greatly optimized, the decreasing extent of fuel consumption is about 5.0%, besides, the decreasing extent of NOx is about 70%. What is more, the increased EER and EEE is up to 6.21% and 2.26%, respectively. And then most of the predicted values obtained by machine learning have been optimized. For BSFC, in general, the simulation value and the predicted value are in good agreement at the smaller value, indicating that the simulation model and the regression prediction model basically achieve the same value at the lower BSFC of the engine. For NOx, the simulated and predicted values have all been optimized. Furthermore, the method and platform developed in this paper will help to carry out a series of related work in the field of vehicle energy flow distribution and optimization when changing different control strategies and optimization methods in the future. Besides, the above work provides a reliable theoretical basis and digital model support for the development of energy-saving and efficient Atkinson cycle engines, which further drives the application of Atkinson cycle engines in new energy vehicles.