Development and Experimental Validation of a Control Oriented Model of SCR for Automotive Application

2018-01-1263

04/03/2018

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Event
WCX World Congress Experience
Authors Abstract
Content
1
The Selective Catalytic reduction (SCR) using urea as reducing agent is currently regarded as the most promising after-treatment technology in order to comply with strict RDE targets for NOX and particulate in Diesel application. Model-based control strategies are promising to satisfy the demands of high NOX conversion efficiency and low tailpipe ammonia slip.
This paper deals with the development of a control oriented model of a Cu-zeolite urea-SCR system for automotive Diesel engines. The model is intended to be used for the real-time urea-SCR management, depending on engine NOX emissions and ammonia storage. In order to ensure suitable computational demand for the on-board implementation, a reduced order one-state model of ammonia storage has been derived from a quasi-dimensional four-state model of the urea-SCR plant. The model has been developed with the aim of investigating the essential behavior of the system, such as the ammonia coverage ratio dynamics, to realize emission control objectives.
In the paper, particular attention is devoted to parameters identification and model validation, which have been performed vs. experimental data measured at the engine test bench. The accuracy of the reduced-order model is demonstrated by comparing NO, NO2 and NH3 concentrations with those measured during the typical engine transients performed to design the map-based SCR control in the Engine Management System (EMS). The results show that the model simulates the validation experiments with good accuracy.
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DOI
https://doi.org/10.4271/2018-01-1263
Pages
10
Citation
Arsie, I., D'Aniello, F., Pianese, C., De Cesare, M. et al., "Development and Experimental Validation of a Control Oriented Model of SCR for Automotive Application," SAE Technical Paper 2018-01-1263, 2018, https://doi.org/10.4271/2018-01-1263.
Additional Details
Publisher
Published
Apr 3, 2018
Product Code
2018-01-1263
Content Type
Technical Paper
Language
English