Removal of NOx sensor ammonia cross sensitivity from contaminated measurements in Diesel-engine selective catalytic reduction systems. (15th June 2015)

• Record Type: Journal Article
• Authors: Zhang, Hui; Wang, Junmin; Wang, Yue-Yun
• Is Part Of: Fuel. Volume 150(2015)
• Page Start: 448
• Page End: 456
• Publication Date: 2015-06-15
• Subjects: Selective catalytic reduction (SCR) -- Cross-sensitivity factor -- Mixed H2/H∞ filtering -- Robust filtering; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2015.02.053
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: A new model for engine NOx sensor cross-sensitivity. Linear parameter-varying model for SCR systems. Mixed H 2 / H ∞ filtering for linear parameter-varying systems. Improved algorithm for the estimation problem. Abstract: In this paper, we study the robust observer design problem for removal of NO x sensor ammonia cross-sensitivity from contaminated measurements in Diesel-engine selective catalytic reduction (SCR) systems via the mixed H 2 / H ∞ approach. Due to the fact that the NO x sensor is cross-sensitive to gaseous ammonia, the reading of NO x sensor is contaminated severely by the ammonia concentration. Moreover, the cross-sensitivity factor is not constant but varying with respect to the temperature. As the real NO x concentration in the downstream tailpipe is useful for the closed-loop control or fault diagnosis, it is necessary and meaningful to estimate the actual value of NO x concentration and non-constant cross-sensitivity factor from the application perspective. Therefore, we aim to design an observer to achieve the objectives. To design the observer, it is assumed that the variation of cross-sensitivity factor is bounded and the bounds are known in advanced. This assumption is practical since the factor is non-negative and generally smaller than 2.5. With the assumption, a design approach for a robust gain-scheduling mixed H 2 / H ∞ observer is proposed. The gain-scheduling strategy is adaptive to compensate for the existing nonlinearities and the involved time-varying parameters of the SCR model. At the end, simulations and comparisons via an experimentally-validated full vehicle simulator cX-Emission are carried out to show the efficacy and the advantages of proposed approach over the existing extended Kalman filter (EKF).