Research Papers

NOx Sensor Ammonia-Cross-Sensitivity Factor Estimation in Diesel Engine Selective Catalytic Reduction Systems

[+] Author and Article Information
Hui Zhang

Merchant Marine College,
Shanghai Maritime University,
Shanghai 201306, China
e-mail: huizhang285@gmail.com

Junmin Wang

Department of Mechanical and
Aerospace Engineering,
The Ohio State University,
Columbus, OH 43210
e-mail: wang.1381@osu.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received April 18, 2014; final manuscript received December 4, 2014; published online February 4, 2015. Assoc. Editor: Ryozo Nagamune.

J. Dyn. Sys., Meas., Control 137(6), 061015 (Jun 01, 2015) (9 pages) Paper No: DS-14-1181; doi: 10.1115/1.4029347 History: Received April 18, 2014; Revised December 04, 2014; Online February 04, 2015

Since the NOx sensor is cross-sensitive to gaseous ammonia, the reading of NOx sensor may not be accurate when the gaseous ammonia is involved and it is a combination of the actual NOx concentration plus the ammonia concentration multiplied by a factor. The factor is named as NOx sensor cross-sensitivity factor. As the reading is inaccurate, this cross-sensitivity phenomenon restricts the application of NOx sensor in the selective catalytic reduction (SCR) system. A practical and economic approach is to design an observer to estimate the cross-sensitivity factor and correct the NOx sensor reading. In this work, the NOx sensor ammonia-cross-sensitivity factor observer design problem for diesel engine SCR systems is investigated. To achieve the objective, first, a three-state nonlinear model for the SCR system is adopted. To establish the model of the ammonia cross-sensitivity factor, it is assumed that the qth-order derivative of the factor is zero. Then, based on the nonlinear model, a proportional-multiple-integral (PMI) observer of the factor is proposed and a nonlinear estimation error system is obtained. With the linear-parameter-varying (LPV) technique, the stability, and the H performance of the nonlinear estimation error system are investigated. Based on the analysis results, the design methods of the stable and the H observer gains are developed. At the end, simulations and comparisons via an experimentally validated full vehicle simulator are carried out to illustrate the efficacy and the advantages of the proposed approach over the existing methods. Since the effect of disturbance to the estimation is considered and constrained in the H observer, the designed H observer has a better estimation performance when the system is subject to disturbance.

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Fig. 1

Schematic diagram of a typical SCR aftertreatment system and the corresponding sensors for the SCR system

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Fig. 2

NOx concentration, exhaust temperature, and flow rate during an FTP75 test cycle

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Fig. 7

Performance of the observer with the gain in Eq. (43)

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Fig. 8

Performance of the observer with the gain in Eq. (40)

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Fig. 9

Estimation error comparison between different observers (43) and (40)

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Fig. 3

Performance of the designed observer with the gains in Eq. (39) for a fixed cross-sensitivity factor

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Fig. 4

NOx concentration comparison with the gains in Eq. (39) for a fixed cross-sensitivity factor

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Fig. 5

NOx concentration error comparison with the gains in Eq. (39) for a fixed cross-sensitivity factor

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Fig. 6

Performance comparison for a fast varying cross-sensitivity factor




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