NDLI: The application of dynamic neural networks to the estimation of feedgas vehicle emissions

Content Provider	IEEE Xplore Digital Library
Author	Jesion, G. Gierczak, C.A. Puskorius, G.V. Feldkamp, L.A. Butler, J.W.
Copyright Year	1998
Description	Author affiliation: Sci. Res. Lab., Ford Motor Co., Dearborn, MI, USA (Jesion, G.)
Abstract	We describe the application of dynamic neural networks to the estimation of moment-by-moment (here referred to as "instantaneous") feedgas emissions. We base such estimates on engine variables available in normal operation to the powertrain processor. Training data were acquired from a single vehicle on a chassis dynamometer facility using standard driving schedules (as used for emissions certification tests). The trained networks were tested using driving trajectories both similar to those used in training as well as trajectories that are distinctly different. The method described allows us to estimate instantaneous levels of carbon monoxide (CO), total hydrocarbon (HC), and oxides of nitrogen (NO/sub x/) in the feedgas (i.e., in the exhaust stream prior to the catalytic converter) of a particular vehicle for a wide variety of trajectories, including cold start, using information readily available to the vehicle's powertrain control module. We discuss briefly the prospects for further generalization of this capability.
Starting Page	69
Ending Page	73
File Size	513308
Page Count	5
File Format	PDF
ISBN	0780348591
ISSN	10987576
DOI	10.1109/IJCNN.1998.682238
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-05-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Vehicle dynamics Mechanical power transmission Testing Vehicles Engines Training data Vehicle driving Processor scheduling Certification
Content Type	Text
Resource Type	Article

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