Improved NO and NO2 Concentration Estimation for a Diesel-Engine-Aftertreatment System

The high nitrogen oxide (NO
_x
) emission is one of the main issues for diesel engines. To reduce the NO',, emissions with an after-treatment system, the information of NO and NO
₂
concentrations plays an important role. In this paper, we investigate the NO and NO
₂
concentration estimation for a diesel-engine-after-treatment system with NO
_x
sensor measurements. The main objective is to estimate the NO and NO
₂
concentrations after diesel particulate filter (DPF) such that the selective catalytic reduction system can be benefited from the estimated concentrations. Since the diesel oxidation catalyst (DOC) is connected in series with DPF, the estimation work for DOC is also necessary. An empirical engine-out NO
₂
/NO
_x
ratio model is adopted to predict the inputs of DOC. By considering the main chemical reactions inside DOC and DPF, the NO and NO
₂
concentration models are obtained. The nonlinear models are converted into linear-parameter-varying forms. Then, a gain-scheduling Luenburger observer is proposed for the DOC and DPF. A practical assumption in which the system modeling and sensor measurements are not perfectly precise is made. Therefore, the gain-scheduling Luenburger observer does not directly share the weighting factors of the original system. By defining the estimation error, an augmented compact system is obtained. Both the stability and the energy-to-peak performance are investigated for the compact system. Based on the derived conditions, the observer design approach is proposed. Experimental studies and comparisons are given to validate the results. Compared with an existing work, the designed observer can improve the estimation performance significantly.