Model-based selective catalytic reduction systems aging estimation


This paper presents an estimation method for automotive selective catalytic reduction (SCR) systems aging conditions. SCR has been widely recognized as one of the leading after treatment 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 during the entire vehicle service life, it is thus critical to have an accurate estimation of SCR system aging condition. In this paper, a Lyapunov-based observer is analytically developed for estimating the SCR aging condition and verified in simulations. 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 effectiveness of the proposed observer.

In Proceedings of the 2016 IEEE International Conference on Advanced Intelligent Mechatronics
Yao Ma
Assistant Professor

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