Hybrid Nonlinear Model Predictive Control of LNT and Urealess SCR Aftertreatment System

Abstract

In recent years, more stringent regulatory standards (EURO 6 emission standards) with a real driving test have been adopted for diesel vehicles. To meet the new regulations, a lean NOx trap (LNT) followed by a urealess selective catalytic reduction [passive SCR (pSCR)], i.e., LNT-pSCR, has been proposed as one of the promising aftertreatment systems for light-duty vehicles. In this brief, we propose hybrid nonlinear model predictive control (NMPC) that determines the optimal timing of rich mode operation for the LNT-pSCR system. First, a 1-D fundamental dynamic model of the LNT-pSCR is derived and parameters are estimated using chassis dynamometer test data. Second, a post-injection map, which describes the characteristics of the engine raw emission flowing into the LNT-pSCR system in the period affected by the rich mode, is constructed using the data. Third, the computational burden of hybrid NMPC is reduced by considering only feasible binary input cases with a successive linearization method. The proposed algorithm that uses the successive linearized LNT model with approximated pSCR NOx conversion is solved within the sample time. The performance of NMPC is investigated using the full LNT-pSCR model as a virtual plant, and the results show that it reduces more NOx with a shorter total duration of rich modes than those of reference control.