Extended Kalman Filter to Estimate NO, NO ₂ , Hydrocarbon and Temperatures in a DOC during Active Regeneration and Under Steady State Conditions

Diesel Oxidation Catalysts (DOC) are used on heavy duty diesel engine applications and experience large internal temperature variations from 150 to 600°C. The DOC oxidizes the CO and HC in the exhaust to CO₂ and H₂O and oxidizes NO to NO₂. The oxidation reactions are functions of its internal temperatures. Hence, accurate estimation of internal temperatures is important both for onboard diagnostic and aftertreatment closed loop control strategies. This paper focuses on the development of a reduced order model and an Extended Kalman Filter (EKF) state estimator for a DOC. The reduced order model simulation results are compared to experimental data. This is important since the reduced order model is used in the EKF estimator to predict the CO, NO, NO₂ and HC concentrations in the DOC and at the outlet. The estimator was exercised using transient drive cycle engine data. The closed loop EKF improves the temperature estimate inside the DOC compared to the open loop estimator. This is supported by the lower error in the estimated NO₂ concentrations at the DOC outlet. The data used for both the modeling and estimator studies were obtained using a 2010 Cummins ISB engine with a production Cummins aftertreatment system consisting of a DOC, Catalyzed Particulate Filter (CPF) and Selective Catalytic Reduction (SCR) components.