Diagnostics-oriented Model for Automotive SCR-ASC

This paper presents a diagnostics-oriented aging model for combined Selective Catalytic Reduction (SCR) and Ammonia Slip Catalyst (ASC) system, along with a model-based on-board diagnostic (OBD) method applied to both test-cell data and on-road data from commercial trucks. The key challenge with model development was unavailability of NOx and NH3 measurements between SCR and ASC. Since it would have been very difficult to calibrate both SCR and ASC dy-namics without any measurements between SCR and ASC, therefore ASC was modeled using static look-up tables to determine ASC's NH3 conversion efficiency and its selectiv-ity to NOx and N2O as a function of temperature and flow rate. The traditional three-state single-cell ordinary differen-tial equation (ODE) model was used for SCR. Hot Federal Test Procedure (hFTP) was used to calibrate the model. Cold FTP (cFTP) and Ramped Mode Cycle (RMC) were used for validation. Results show that the SCR-ASC model can cap-ture the aging signatures in tailpipe NOx, NH3, and N2O rea-sonably well for cFTP, hFTP, and RMC cycles in the test -cell data. After slight re-calibration and combining with a simple model for commercial NOx sensor's cross-sensitivity to NH3, the model works reasonably well for on-road data from commercial trucks. A model-based on-board diagnostic (OBD) method has been presented with enable conditions designed to detect operating conditions suitable for detecting aging signatures, while minimizing false positives and false negatives. The OBD method is applied to both test-cell and real-world truck data with commercial NOx sensors. Results on test-cell data demonstrate the challenges of robust SCR monitoring even on the limited data set used in this work. The model-based enable conditions are shown to be robust but extremely restrictive as the OBD gets enabled at very few points in the test-cell data. Application on truck data showed that the proposed OBD method can be implemented on com-mercial trucks with limited sensors. In the truck data, the en-able conditions were satisfied on many more points than the test-cell data. Results on truck data show encouraging trends between relative degradation level and the number of miles on four trucks. In future work, these trends will be validated using more data from commercial trucks with known aging levels.