Study on Emission Control of CNG Engine Based on D-S Evidence Theory

Abstract:

Article Preview

To reduce emissions, CNG engines are commonly equipped with three-way catalytic converters. However when the engines run at transient conditions, the air fuel ratio can not be precisely controlled at theoretical value by traditional means, so the catalytic converters can not achieve their desired effect. This paper presents a new method for CNG engines to control air fuel ratio at transient conditions. The moments which intake and exhaust valves open are used as the trigger signals for ECU to collect the test data simultaneously. The dynamic information of CNG engine is detected by multiple sensors; the nonlinear coupling relationship between air fuel ratio of CNG engine and the operating conditions are established through information fusion and neural network control. The requirement of real time control for air fuel ratio is achieved, so the emissions of CNG engines are reduced further.

Info:

Periodical:

Edited by:

Yongping Zhang, Linhua Zhou and Elwin Mao

Pages:

281-284

DOI:

10.4028/www.scientific.net/AMM.109.281

Citation:

X. Q. Li et al., "Study on Emission Control of CNG Engine Based on D-S Evidence Theory", Applied Mechanics and Materials, Vol. 109, pp. 281-284, 2012

Online since:

October 2011

Export:

Price:

$35.00

[1] Li Ding-gen, Shu Yong-qiang, Li Xiao-zhong, Gasoline Engine Transient Air-fuel Ratio Control Based on Modified MVEM, Vehicle Engine. 1 (2010) 20-23, 27. In Chinese.

[2] Yao Jubiao, Wu Bin, Zhou Dasen, Analysis on the Transportation Characteristics of Air Fuel Ratio of Electronic Controlled LPG Engine, Small Internal Combustion Engine and Motorcycle. 2 (2009) 78-80. In Chinese.

[3] Arsie I, Pianese C, Rizzo G, An Adaptive Estimator of Fuel Film Dynamics in the Intake Port of a Spark Ignition Engine, Control Engineering Practice. 11 (2008)303~309.

DOI: 10.1016/s0967-0661(02)00040-0

[4] Wu Yi-hu,Hou Zhi-xiang,Song Dan-dan, Air Fuel Ratio Control of Gasoline Engine Based on Neural Network Multi-Step Predictive Model during Transient Conditions, Journal of Combustion Science and Technology. 14 (2008) 11-15. In Chinese.

DOI: 10.1049/cp:20061103

[5] Zhang Jili. Fuzzy Neural Network Control Theory and Application, Harbin Polytechnic University Press,Harbin, 2004. In Chinese.

In order to see related information, you need to Login.