Neural network control of air-to-fuel ratio in a bi-fuel engine

被引：6
|
作者：
Gnanam, Gnanaprakash [1 ]
Habibi, Said R.
Burton, Richard T.
Sulatisky, Michael T.
机构：
[1] Univ Saskatchewan, Dept Mech Engn, Saskatoon, SK S7N 5A9, Canada
[2] Saskatchewan Res Council, Alternat Energy & Intelligent Syst Sect, Saskatoon, SK, Canada
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS | 2006年 / 36卷 / 05期
关键词：
artificial neural networks; bi-fuel engines; compressed natural gas (CNG); fuel injection control;
D O I：
10.1109/TSMCC.2005.855524
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a neural network-based control system is proposed for fine control of the intake air/fuel ratio in a bi-fuel engine. This control system is an add-on module for an existing vehicle manufacturer's electronic control units (ECUs). Typically the ECU is calibrated for gasoline and provides a good control of the intake air/fuel ratio with gasoline. The neural network-based control system is developed to allow the conversion of a gasoline ECU to a bi-fuel form with compressed natural gas at minimal cost. The effectiveness of the neural control system is demonstrated by using a simulation of a Dodge four-stroke bi-fuel engine.
引用
收藏
页码：656 / 667
页数：12
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