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Research Papers

Observer-Based Estimation of Aging Condition for Selective Catalytic Reduction Systems in Vehicle Applications

[+] Author and Article Information
Yao Ma

Department of Mechanical
and Aerospace Engineering,
The Ohio State University,
Columbus, OH 43210
e-mail: ma.506@osu.edu

Junmin Wang

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

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 21, 2016; final manuscript received August 5, 2016; published online October 20, 2016. Assoc. Editor: Ardalan Vahidi.

J. Dyn. Sys., Meas., Control 139(2), 021002 (Oct 20, 2016) (9 pages) Paper No: DS-16-1042; doi: 10.1115/1.4034508 History: Received January 21, 2016; Revised August 05, 2016

This paper presents two observers for estimating the aging condition of selective catalytic reduction (SCR) systems in vehicle applications. SCR systems have been widely recognized as one of the leading engine exhaust gas aftertreatment systems for reducing diesel powertrain tailpipe NOx emissions in ground vehicle applications. While fresh SCRs are quite effective in reducing tailpipe NOx emissions, their NOx reduction capabilities and performances may substantially degrade with in-service aging. To maintain the emission control performance of a SCR system for a diesel engine during the entire vehicle service life, it is thus critical to have an accurate estimation of the SCR system aging condition. In this paper, two Lyapunov-based observers utilizing the measurements of NOx and ammonia concentrations are analytically developed and verified in simulations for estimating the SCR aging condition. The measurement uncertainty is explicitly considered in the observer design process. A sufficient condition for the boundedness of the estimation error is derived. Simulation results under the US06 test cycle demonstrate the effectiveness of the proposed observers.

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References

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Figures

Grahic Jump Location
Fig. 4

Estimated NO concentration and model computed NO concentration using NO dynamics (50% aged)

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Fig. 5

Estimated aging factor and actual aging factor using NO dynamics (50% aged)

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Fig. 1

Exhaust temperature and flow rate under the US06 cycle

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Fig. 2

Estimated NO concentration and model computed NO concentration using NO dynamics (10% aged)

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Fig. 3

Estimated aging factor and actual aging factor using NO dynamics (10% aged)

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Fig. 6

Contaminated NO concentration measurement signal

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Fig. 7

NO concentration measurement noise

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Fig. 8

NO concentration estimation with disturbed measurement (10% aged)

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Fig. 12

Estimated NO concentration and model computed NO concentration using NO and NH3 dynamics (10% aged)

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Fig. 9

Aging factor estimation with disturbed measurement (10% aged)

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Fig. 10

NO concentration estimation with disturbed measurement (50% aged)

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Fig. 11

Aging factor estimation with disturbed measurement (50% aged)

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Fig. 15

Estimated NO concentration and model computed NO concentration using NO and NH3 dynamics (50% aged)

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Fig. 16

Estimated NH3 concentration and model computed NH3 concentration using NO and NH3 dynamics (50% aged)

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Fig. 17

Estimated aging factor and actual aging factor using NO and NH3 dynamics (50% aged)

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Fig. 13

Estimated NH3 concentration and model computed NH3 concentration using NO and NH3 dynamics (10% aged)

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Fig. 14

Estimated aging factor and actual aging factor using NO and NH3 dynamics (10% aged)

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Fig. 18

NO concentration estimation with disturbed measurement using NO and NH3 dynamics (10% aged)

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Fig. 19

NH3 concentration estimation with disturbed measurement using NO and NH3 dynamics (10% aged)

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Fig. 20

Aging factor estimation with disturbed measurement using NO and NH3 dynamics (10% aged)

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Fig. 21

NO concentration estimation with disturbed measurement using NO and NH3 dynamics (50% aged)

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Fig. 22

NH3 concentration estimation with disturbed measurement using NO and NH3 dynamics (50% aged)

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Fig. 23

Aging factor estimation with disturbed measurement using NO and NH3 dynamics (50% aged)

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