Data-Based On-Board Diagnostics for Diesel Engine NOx-Reduction Aftertreatment Systems

Abstract

Abstract The NOx conversion efficiency of a combined Selective Catalytic Reduction and Ammonia Slip Catalyst (SCR-ASC) in a Diesel Aftertreatment (AT) system degrades with time. A novel model-informed data-driven On-Board Diagnostic (OBD) binary classification strategy is proposed in this paper to distinguish an End of Useful Life (EUL) SCR-ASC catalyst from Degreened (DG) ones. An optimized, supervised machine learning model was used for the classification with a calibrated single-cell 3-state Continuous Stirred Tank Reactor (CSTR) observer used for state estimation. Percentage of samples classified as EUL (%EUL), w.r.t. classification boundary of 50%, was used as an objective criterion of classification. The method resulted in 87.5% classification accuracy when tested on 8 day-files from 4 trucks (2 day-files per truck; 1 DG and 1 EUL) operating in real-world on-road conditions. Each day-file had ~86,000 samples of data. Mileage of the same truck was used as ground truth for classification. However, mileage across different trucks cannot be used for classification since the operating conditions would vary across trucks.