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Robust, Model-Based Urea Dosing Control for SCR Aftertreatment Systems using a Cross-Sensitive Tailpipe NOx Sensor
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
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This article describes a NOx sensor based urea dosing control strategy for heavy-duty diesel aftertreatment systems using Selective Catalytic Reduction. The dosing control strategy comprises of a fast-response, model-based ammonia storage control system in combination with a long-timescale tailpipe-feedback module that adjusts the dosing quantity according to current aftertreatment conditions. This results in a control system that is robust to system disturbances such as biased NOx sensors and variations in AdBlue concentrations. The cross-sensitivity of the tailpipe NOx sensor to ammonia is handled by a novel, smart signal filter that can reliably identify the contributions of NOx and NH3 in the tailpipe sensor signal, without requiring an artificial perturbation of the dosing signal.
The tailpipe feedback module compares the signal from the cross-sensitive tailpipe NOx sensor to the modeled tailpipe sensor signal to estimate the measured and modeled NOx conversion and NH3 slip, without the need for an NH3 sensor. The difference between these measured and modeled quantities is used to adjust the dosing quantity to the aftertreatment system, thereby maintaining nominal performance of the aftertreatment system in the presence of disturbances. Simulation results are presented of the urea dosing control system covering a wide range of disturbances, demonstrating the robustness properties of the controlled system. The robust urea dosing control strategy was implemented on a rapid prototyping platform and tested on a state-of-the-art Euro VI engine and aftertreatment systems, confirming the expected performance and robustness properties.
CitationHommen, G., Kupper, F., and Seykens, X., "Robust, Model-Based Urea Dosing Control for SCR Aftertreatment Systems using a Cross-Sensitive Tailpipe NOx Sensor," SAE Technical Paper 2017-01-0938, 2017, https://doi.org/10.4271/2017-01-0938.
Data Sets - Support Documents
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