eCite Digital Repository

Predictive Models for Emission of Hydrogen Powered Car Using Various Artificial Intelligent Tools

Citation

Karri, V and Ho, NT, Predictive Models for Emission of Hydrogen Powered Car Using Various Artificial Intelligent Tools, Neural Computing & Applications, Online, (December 2008) pp. 1. ISSN 1433-3058 (2008) [Professional, Non Refereed Article]

DOI: doi:10.1007/s00521-008-0218-y

Abstract

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg-Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg-Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error. © 2008 Springer-Verlag London Limited.

Item Details

Item Type:Professional, Non Refereed Article
Research Division:Engineering
Research Group:Automotive Engineering
Research Field:Automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels)
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Engineering
Author:Ho, NT (Dr Tien Ho)
ID Code:55339
Year Published:2008
Web of Science® Times Cited:5
Deposited By:Engineering
Deposited On:2009-03-09
Last Modified:2009-03-09
Downloads:0

Repository Staff Only: item control page