Design of extended Kalman filter observer coupled to ammonia cross-sensitivity model for an SCR system
Commercial diesel vehicles that comply the China/EURO VI emission regulations are generally equipped with selective catalytic reduction (SCR) systems for reducing N O x emission and N O x sensors at its downstream for accurate closed-loop control. However, N O x sensors have significant ammonia cros...
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Veröffentlicht in: | International journal of engine research 2023-07, Vol.24 (7), p.3061-3072 |
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Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Commercial diesel vehicles that comply the China/EURO VI emission regulations are generally equipped with selective catalytic reduction (SCR) systems for reducing
N
O
x
emission and
N
O
x
sensors at its downstream for accurate closed-loop control. However,
N
O
x
sensors have significant ammonia cross-sensitivity pushing up their readings. Moreover, for the closed-loop control of SCR systems effectively estimating the critical states from the
N
O
x
sensor readings remains a further challenge. In this paper, an ammonia cross-sensitivity model with segmented sin and quadratic composite functions describing high frequency characteristics is established by analyzing the experimental data of an SCR system. A control-oriented three-states model of an SCR system is established, and based on it an extended Kalman filter (EKF) observer coupled to the ammonia cross-sensitivity model is proposed. The simulation results show that from the
N
O
x
sensor readings the EKF observer can effectively estimate the deterioration trend of the
N
O
x
and
N
H
3
emissions, and describe the dynamic change process of the ammonia coverage rate. The experimental results under the four non-excitation cycles show that the EKF observer can effectively estimate the deterioration trend, that the average estimated accuracy of
N
H
3
and
N
O
x
during their deterioration stage can respectively reach 86.16% and 54.87%, and that the observer has better robustness to disturbances from the operating conditions. |
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ISSN: | 1468-0874 2041-3149 |
DOI: | 10.1177/14680874221140447 |