Development of a MPF Model with a Kalman Filter State Estimator for Simulation and Control of Particulate Matter Distribution of a CPF for Aftertreatment System Control Applications

Boopathi  Singalandapuram Mahadevan, Michigan Technological University

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 aftertreatment electronic control unit (ECU) to provide feedback on temperature and PM loading distribution within each axial and radial zone of the filter substrate. A 2-D high-fidelity SCR-F/CPF model including selective catalytic reactions in a PM filter was developed along with the new cake permeability model to account for the potential damage in the PM cake layer during PM oxidation and damage recovery of the PM cake layer during post loading of the CPF. This high-fidelity SCR-F/CPF model was parameterized 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. A reduced order MPF model was developed to reduce the computational complexity of the high-fidelity SCR-F/CPF model. The reduced order model using a 5×5 zone model discretization was selected to develop an extended Kalman Filter (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 diesel oxidation catalyst (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. The validation results show that the estimated root mean square (RMS) temperature error with DOC-CPF estimator is within 2.7^oC compared to the experimental data by taking the feedback of the CPF outlet temperature measurement. Similarly, the pressure drop and its components estimation error reduced from 0.5 kPa to 0.1 kPa with the CPF estimator compared to the reduced order model using the delta-P sensor feedback.

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