A Tailpipe Nox Sensor Decoupling Algorithm For Integrated Scr And Amox Systems

Urea-based selective catalytic reduction (SCR) system coupled with a downstream ammonia oxidation catalyst (AMOX), has become a standard NOx reduction device for Diesel engines. On-board diagnostics of tailpipe NOx and ammonia (NH3) emissions and closed-loop controls using only tailpipe NOx sensor, are critical for SCR systems to achieve high NOx conversion efficiency and low tailpipe NH3 slip. However, due to commercial NOx sensor NH3 cross-sensitivity issue, the tailpipe NH3 emissions can be misinterpreted as NOx emissions, which may lead to erratic diagnostics and unstable control systems. The purpose of this study is to develop a high-fidelity control-oriented SCR-AMOX model and a model-based estimation algorithm using extended Kalman filter (EKF) for effectively decoupling NOx and NH3 concentrations from the mixed tailpipe NOx sensor signals for an SCR-AMOX system. The proposed model and EKF-based decoupling algorithm were validated using the experimental data collected from a Diesel engine platform during both steady-state and transient driving cycles. Experimental verification results demonstrated high accuracy of the control-oriented model and proved the efficacy of the proposed decoupling algorithm in meeting the preset thresholds. The robustness of the decoupling algorithm against the uncertainty from catalyst aging was also demonstrated and verified. Such EKF-based robust decoupling algorithm can be instrumental in the diagnostics and controls of urea-based SCR systems.