Open AccessFuel Consumption Modeling of a Turbocharged Gasoline Engine Based on a Partially Shared Neural Network
Author(s) -
Diming Lou,
Yinghua Zhao,
Liang Fang
Publication year - 2021
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.1c02403
Subject(s) - fuel efficiency , artificial neural network , test bench , turbocharger , calibration , robustness (evolution) , computer science , automotive engineering , test data , sampling (signal processing) , set (abstract data type) , engineering , simulation , artificial intelligence , mechanical engineering , mathematics , statistics , biochemistry , chemistry , programming language , turbine , filter (signal processing) , computer vision , gene , embedded system
Fuel consumption is the most important parameter that characterizes the fuel economy of the engines. Instead of manual fuel consumption calibration based on the experience of engineers, the establishment of a fuel consumption model greatly reduces the time and cost of multiparameter calibration and optimization of modern engines and realizes the further exploration of the engine fuel economy potential. Based on the bench test, one-dimensional engine simulation, and design of experiment, a partially shared neural network with its sampling and training method to establish the engine fuel consumption model is proposed in this paper in view of the lack of discrete working conditions in the traditional neural network model. The results show that the proposed partially shared neural network applying Gauss distribution sampling and the frozen training method, after an analysis of the number of hidden neurons and epochs, showed optimal prediction accuracy and excellent robustness in full coverage over the whole load region on the test data set obtained through the bench test. Eighty-seven percent of the prediction errors are less than 3%, all prediction errors are less than 10%, and the R 2 value is improved to 0.954 on the test data set.