A Methodology to Enhance Design and On-Board Application of Neural Network Models for Virtual Sensing of Nox Emissions in Automotive Diesel Engines University of Salerno

Format:

Conference/Event

Author/Creator:

Arsie, Arsie, author.

Contributor:

Cricchio, Andrea

De Cesare, Matteo

Pianese, C. (Cesare)

Sorrentino, Marco

Conference Name:

11th International Conference on Engines & Vehicles (2013-09-15 : Capri, Italy)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2013

Summary:

The paper describes suited methodologies for developing Recurrent Neural Networks (RNN) aimed at estimating NOx emissions at the exhaust of automotive Diesel engines. The proposed methodologies particularly aim at meeting the conflicting needs of feasible on-board implementation of advanced virtual sensors, such as neural network, and satisfactory prediction accuracy. Suited identification procedures and experimental tests were developed to improve RNN precision and generalization in predicting engine NOx emissions during transient operation. NOx measurements were accomplished by a fast response analyzer on a production automotive Diesel engine at the test bench. Proper post-processing of available experiments was performed to provide the identification procedure with the most exhaustive information content. The comparison between experimental results and predicted NOx values on several engine transients, exhibits high level of accuracy

Notes:

Vendor supplied data

Publisher Number:

2013-24-0138

Access Restriction:

Restricted for use by site license