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Date of Award


Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy in Mechanical Engineering-Engineering Mechanics (PhD)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

John H. Johnson

Advisor 2

Mahdi Shahbakhti

Committee Member 1

Jeffrey D. Naber

Committee Member 2

Sunil S. Mehendale


A multi-zone particulate filter (MPF) model along with the extended Kalman filter (EKF) based catalyzed diesel particulate filter (CPF) estimator was developed. The model has the potential to run in real-time within the engine control unit (ECU) to provide feedback on temperature and PM loading distribution within each axial and radial zone of the filter substrate. A high-fidelity SCR-F/CPF (selective catalytic reaction in a PM filter – the model was applied to CPF) model was developed. A new cake permeability model was also developed based on fundamental research findings in the literature to account for the potential damage in the PM cake layer during PM oxidation as well as the damage recovery of the PM cake layer during post loading of the CPF. This high-fidelity SCR-F/CPF model was calibrated with eighteen runs of data from a 2007 Cummins ISL engine that consisted of passive and active regeneration sets of data for ULSD, B10 and B20 fuels. The model had a maximum root mean square (RMS) error of 5oC for predicting temperature distribution along with the RMS error of 2 g for PM loading and 0.2 kPa for the pressure drop.

A reduced order MPF model was developed to reduce the computational complexity. The reduced order model using a 5x5 zone was selected to develop an EKF based CPF state estimator. The real-time estimator calculates the unknown states of the CPF such as temperature and PM distribution and pressure drop of the CPF using the ECU sensor inputs and the reduced order model in order to determine when to do active regeneration. A DOC estimator was also integrated with the CPF estimator in order to provide estimates of the DOC outlet concentrations and temperature for the CPF estimator. The EKF based DOC-CPF estimator was validated on one of the active regeneration experiments and results show that the estimator provides improved accuracy compared to the reduced order model by taking the feedback of the CPF outlet temperature measurement. Similarly, the pressure drop and its components estimation accuracy improved with the CPF estimator compared to the reduced order model using the delta-P sensor feedback.