Model based failure detection of diesel particulate filter

Aniket Gupta, Matthew Franchek, Karolos Grigoriadis, Daniel J. Smith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Improvements in diesel engine technology have resulted in their expanded usage as powertrains in automotive applications. The Diesel Particulate Filter (DPF) is a common component of the exhaust after-treatment system of Diesel engines that removes the harmful Particulate Matter (PM) in the exhaust gas. To ensure that the filter is able to reduce PM levels of the diesel exhaust below regulated limits, On Board Diagnostics (OBD) of DPFs is required to provide alerts in the case of filter malfunction or failure. In the present study a method for performing the failure detection of Diesel Particulate Filter is proposed based on an adaptive model based technique. To detect a failure the coefficients of a healthy model of the pressure difference across the filter are compared with the adapted model coefficients since the presence of failure alters the dynamics of the system. This approach is robust to modeling errors, sensor noise and process variability and has OBD capability without the need of any additional sensors. The proposed approach is experimentally validated on a federal test procedure (FTP-75) drive cycle for healthy and failed filters in a heavy duty diesel engine test cell.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1567-1572
Number of pages6
ISBN (Print)9781457700804
DOIs
StatePublished - 2011

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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