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Development of a Multi-Zone Catalyze...

Singalandapuram Mahadevan, Boopathi.

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Development of a Multi-Zone Catalyzed Particulate Filter Model and Kalman Filter Estimator for Simulation and Control of Particulate Matter Distribution of a CPF for Engine ECU Applications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Development of a Multi-Zone Catalyzed Particulate Filter Model and Kalman Filter Estimator for Simulation and Control of Particulate Matter Distribution of a CPF for Engine ECU Applications./
Author:	Singalandapuram Mahadevan, Boopathi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	299 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
Subject:	Automotive engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10257953
ISBN:	9781369820089

Development of a Multi-Zone Catalyzed Particulate Filter Model and Kalman Filter Estimator for Simulation and Control of Particulate Matter Distribution of a CPF for Engine ECU Applications.
Singalandapuram Mahadevan, Boopathi.

Development of a Multi-Zone Catalyzed Particulate Filter Model and Kalman Filter Estimator for Simulation and Control of Particulate Matter Distribution of a CPF for Engine ECU Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 299 p.

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.

Thesis (Ph.D.)--Michigan Technological University, 2017.

This item is not available from ProQuest Dissertations & Theses.

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 5°C for predicting temperature distribution along with the RMS error of 2 g for PM loading and 0.2 kPa for the pressure drop.

ISBN: 9781369820089Subjects--Topical Terms:

2181195
Automotive engineering.

Development of a Multi-Zone Catalyzed Particulate Filter Model and Kalman Filter Estimator for Simulation and Control of Particulate Matter Distribution of a CPF for Engine ECU Applications.
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100 1 $a Singalandapuram Mahadevan, Boopathi. $3 3431396
245 1 0 $a Development of a Multi-Zone Catalyzed Particulate Filter Model and Kalman Filter Estimator for Simulation and Control of Particulate Matter Distribution of a CPF for Engine ECU Applications.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 299 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
500 $a Advisers: John H. Johnson; Mahdi Shahbakhti.
502 $a Thesis (Ph.D.)--Michigan Technological University, 2017.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a 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 5°C for predicting temperature distribution along with the RMS error of 2 g for PM loading and 0.2 kPa for the pressure drop.
520 $a 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.
590 $a School code: 0129.
650 4 $a Automotive engineering. $3 2181195
690 $a 0540
710 2 $a Michigan Technological University. $b Mechanical Engineering-Engineering Mechanics. $3 1043627
773 0 $t Dissertation Abstracts International $g 78-10B(E).
790 $a 0129
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10257953