Air Fuel Ratio Identification of Gasoline Engine during Transient Conditions Based on Elman Neural Networks

Author

Hou, Zhixiang ; Sen, Quntai ; Wu, Yihu

Author_Institution

Sch. of Inf. Sci. & Eng., Central South Univ.

Volume

1

fYear

2006

fDate

16-18 Oct. 2006

Firstpage

32

Lastpage

36

Abstract

Air fuel ratio is a key index affecting power performance and fuel economy and exhaust emissions of the gasoline engine, whose accurate model is the foundation of accuracy air fuel ratio control. Taking HL495 engine as experimental device, a method of indenting air fuel ratio based on Elman neural network was provided in this paper. Experiment results show the air fuel ratio model based on Elman neural network has simple structure and can accurately approximate the air fuel ratio transient process and average relative error is less than 1 %. The air fuel ratio based on Elman neural network is better than the air fuel ratio model based on BP neural network

Keywords

backpropagation; internal combustion engines; neural nets; Elman neural network; HL495 engine; air fuel ratio control; air fuel ratio identification; air fuel ratio indenting; air fuel ratio model; air fuel ratio transient process; exhaust emission; fuel economy; gasoline engine; power performance; transient condition; Automotive engineering; Engine cylinders; Feedforward neural networks; Feeds; Fuel economy; Information science; Mathematical model; Neural networks; Petroleum; Power engineering and energy;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on

Conference_Location

Jinan

Print_ISBN

0-7695-2528-8

Type

conf

DOI

10.1109/ISDA.2006.86

Filename

4021404