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

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 Arti...

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Vydáno v:Neural computing & applications Ročník 18; číslo 5; s. 469 - 476
Hlavní autoři: Karri, Vishy, Ho, Tien Nhut
Médium: Journal Article Konferenční příspěvek
Jazyk:angličtina
Vydáno: London Springer-Verlag 01.06.2009
Springer
Témata:
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science; control theory; systems
Data Mining and Knowledge Discovery
Exact sciences and technology
Image Processing and Computer Vision
Inference from stochastic processes; time series analysis
Isnn 2008
Learning and adaptive systems
Mathematics
Parametric inference
Probability and statistics
Probability and Statistics in Computer Science
Sciences and techniques of general use
Statistics
Artificial intelligent techniques
Back-propagation neural networks with Levenberg–Marquardt algorithm
UTAS artificial neural networks
Hydrogen powered car
Hydrogen emission prediction
Adaptive neuro-fuzzy inference systems
Neural computation
Fuzzy system
Temperature
Adaptive algorithm
Error estimation
Propagation
Adaptive system
Prediction
Prediction theory
Statistical estimation
Neural network
Mean estimation
Intelligent sensors
Mean square error
Fuzzy algorithm
Software
Mean error
Application
ISSN:0941-0643, 1433-3058
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Popis
Shrnutí: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-008-0218-y