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Predictive models for emission of hydrogen powered car using various artificial intelligent tools

  • ISNN 2008
  • Published:
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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.

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Acknowledgments

The authors are deeply grateful Dr Sergio Giudici, Hydro Tasmania Pty Ltd for financial support and all of the Hydrogen and Allied Renewable Technology research members as well as Intelligent Hydrogen Car project for sharing ideas and concept along the way.

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Authors and Affiliations

  1. Australian College of Kuwait, P.O. Box 1411, Safat, 13015, Kuwait

    Vishy Karri

  2. School of Engineering, University of Tasmania, GPO Box 252-65, Hobart, TAS, 7001, Australia

    Tien Nhut Ho

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  1. Vishy Karri
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  2. Tien Nhut Ho
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Correspondence to Tien Nhut Ho.

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Karri, V., Ho, T.N. Predictive models for emission of hydrogen powered car using various artificial intelligent tools. Neural Comput & Applic 18, 469–476 (2009). https://doi.org/10.1007/s00521-008-0218-y

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  • DOI: https://doi.org/10.1007/s00521-008-0218-y

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