Automated Model Calibration for Urea-SCR Systems Using Test-Rig Data

Selective catalytic reduction (SCR) systems with urea injectors are widely utilized and can meet stricter NO x regulations for both light- and heavy-duty diesel vehicles. In this study, we propose a systematic parameter estimation strategy for a one-dimensional SCR model. Dual-site kinetics with 12 kinetic reactions and 28 kinetic parameters are considered in the SCR model. We estimate subsets of parameters sequentially since it is difficult to estimate all of the parameters at once. To this end, four test-rig experimental data obtained under ammonia storage, ammonia oxidation, nitrogen monoxide oxidation, and SCR reaction were used separately. We estimate a subset of parameters corresponding to the relevant reactions using each rig data because only some reactions occur under each rig experiment. To demonstrate the efficacy of this approach, we estimate the parameters simultaneously using one set of real driving data and validate the model using a new set of real driving data that was not utilized for parameter estimation. With the prediction errors for the test set of real driving data, the proposed technique, which estimates a subset of parameters sequentially, has a 7.35% lower error.