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Tailpipe particle number (PN) emission limits for heavy-duty diesel engines have been introduced as part of the off-highway Stage V standards. To meet the required limits a diesel particulate filter (DPF) with high filtration efficiency is required. The DPF relies on formation of a soot cake layer on the channel walls to achieve this high filtration efficiency. Off highway Stage V certification cycles are significantly higher in temperature than their on-highway counterparts, leading to difficulty in creating and maintaining a soot cake in the DPF. Hence for these applications meeting particle number requirements is challenging. To meet the high filtration efficiency requirements the DPF will have to reduce mean pore size, pore standard deviation, and increase wall thickness, in turn increasing backpressure, which results in a fuel consumption penalty. Another option is to evaluate the impact of temperature stable ash accumulation on DPF filtration efficiency.

This paper focuses on the development of an Extended Kalman Filter for estimating internal species concentration and storage states of an SCR using NOX and NH₃ sensors. The motivation for this work was twofold. First, knowledge of internal states may be useful for onboard diagnostic strategy development. In particular, significant errors between the outlet NOX or NH₃ sensors, reconstructed from estimated states, and the measured NOX or NH₃ concentrations may aid OBD strategies that attempt to identify particular system failure modes. Second, the EKF described estimates not only stored ammonia but also NO, NO₂ and NH₃ gas concentrations within and exiting the SCR. Exploiting knowledge of the individual species concentrations, instead of lumping them together as NOX, can yield improved closed loop urea controller performance in terms of reduced urea consumption and better NOX conversion.

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.