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

Abstract

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.

Yao Ma

Assistant Professor

My research interests focus on control and modeling of intelligent vehicle systems for improvement of efficiency, mobility, and safety.