A predictive control method for automotive selective catalytic reduction systems

Yao Ma, Junmin Wang

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

Abstract

This paper presents a predictive control method for automotive Selective Catalytic Reduction (SCR) systems to minimize vehicle tailpipe Nitrogen Oxides (NOx) and ammonia (NH3) emissions. SCR systems have been indispensable in Diesel-powered vehicles to reduce the toxic emissions. To balance the tradeoff between NOxand NH3, the ammonia storage level in an SCR needs to be critically controlled. The proposed control method consists of an ammonia coverage ratio tracking controller and a predictive reference ammonia coverage ratio generator. The reference generator will utilize the predictive information, enabled by growing vehicle connectivity and intelligence, to determine an optimal level of ammonia coverage ratio within the preview horizon. The tracking controller will then drive ammonia coverage ratio to a desired level. The effectiveness of the proposed design approach is demonstrated through simulation studies with experimentally acquired data.

Original languageEnglish
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1593-1598
Number of pages6
ISBN (Electronic)9781538679265
DOIs
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
Volume2019-July
ISSN (Print)0743-1619

Conference

Conference2019 American Control Conference, ACC 2019
CountryUnited States
CityPhiladelphia
Period07/10/1907/12/19

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  • Cite this

    Ma, Y., & Wang, J. (2019). A predictive control method for automotive selective catalytic reduction systems. In 2019 American Control Conference, ACC 2019 (pp. 1593-1598). [8814808] (Proceedings of the American Control Conference; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/acc.2019.8814808