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Title: Development of a Kalman filter estimator for simulation and control of particulate matter distribution of a diesel catalyzed particulate filter

Abstract

The knowledge of the temperature and particulate matter mass distribution is essential for monitoring the performance and durability of a catalyzed particulate filter. A catalyzed particulate filter model was developed, and it showed capability to accurately predict temperature and particulate matter mass distribution and pressure drop across the catalyzed particulate filter. However, the high-fidelity model is computationally demanding. Therefore, a reduced order multi-zone particulate filter model was developed to reduce computational complexity with an acceptable level of accuracy. In order to develop a reduced order model, a parametric study was carried out to determine the number of zones necessary for aftertreatment control applications. The catalyzed particulate filter model was further reduced by carrying out a sensitivity study of the selected model assumptions. The reduced order multi-zone particulate filter model with 5 × 5 zones was selected to develop a catalyzed particulate filter state estimator considering its computational time and accuracy. Next, a Kalman filter–based catalyzed particulate filter estimator was developed to estimate unknown states of the catalyzed particulate filter such as temperature and particulate matter mass distribution and pressure drop (Δ P) using the sensor inputs to the engine electronic control unit and the reduced order multi-zone particulate filter model. A dieselmore » oxidation catalyst estimator was also integrated with the catalyzed particulate filter estimator in order to provide estimates of diesel oxidation catalyst outlet concentrations of NO 2 and hydrocarbons and inlet temperature for the catalyzed particulate filter estimator. The combined diesel oxidation catalyst–catalyzed particulate filter estimator was validated for an active regeneration experiment. The validation results for catalyzed particulate filter temperature distribution showed that the root mean square temperature error by using the diesel oxidation catalyst–catalyzed particulate filter estimator is within 3.2 °C compared to the experimental data. Similarly, the Δ P estimator closely simulated the measured total Δ P and the estimated cake pressure drop error is within 0.2 kPa compared to the high-fidelity catalyzed particulate filter model.« less

Authors:
 [1];  [1];  [1]
  1. Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, MI, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1512529
Grant/Contract Number:  
EE0000204
Resource Type:
Published Article
Journal Name:
International Journal of Engine Research
Additional Journal Information:
Journal Name: International Journal of Engine Research Journal Volume: 21 Journal Issue: 5; Journal ID: ISSN 1468-0874
Publisher:
SAGE Publications
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Singalandapuram Mahadevan, Boopathi, Johnson, John H., and Shahbakhti, Mahdi. Development of a Kalman filter estimator for simulation and control of particulate matter distribution of a diesel catalyzed particulate filter. United Kingdom: N. p., 2018. Web. doi:10.1177/1468087418785855.
Singalandapuram Mahadevan, Boopathi, Johnson, John H., & Shahbakhti, Mahdi. Development of a Kalman filter estimator for simulation and control of particulate matter distribution of a diesel catalyzed particulate filter. United Kingdom. doi:https://doi.org/10.1177/1468087418785855
Singalandapuram Mahadevan, Boopathi, Johnson, John H., and Shahbakhti, Mahdi. Tue . "Development of a Kalman filter estimator for simulation and control of particulate matter distribution of a diesel catalyzed particulate filter". United Kingdom. doi:https://doi.org/10.1177/1468087418785855.
@article{osti_1512529,
title = {Development of a Kalman filter estimator for simulation and control of particulate matter distribution of a diesel catalyzed particulate filter},
author = {Singalandapuram Mahadevan, Boopathi and Johnson, John H. and Shahbakhti, Mahdi},
abstractNote = {The knowledge of the temperature and particulate matter mass distribution is essential for monitoring the performance and durability of a catalyzed particulate filter. A catalyzed particulate filter model was developed, and it showed capability to accurately predict temperature and particulate matter mass distribution and pressure drop across the catalyzed particulate filter. However, the high-fidelity model is computationally demanding. Therefore, a reduced order multi-zone particulate filter model was developed to reduce computational complexity with an acceptable level of accuracy. In order to develop a reduced order model, a parametric study was carried out to determine the number of zones necessary for aftertreatment control applications. The catalyzed particulate filter model was further reduced by carrying out a sensitivity study of the selected model assumptions. The reduced order multi-zone particulate filter model with 5 × 5 zones was selected to develop a catalyzed particulate filter state estimator considering its computational time and accuracy. Next, a Kalman filter–based catalyzed particulate filter estimator was developed to estimate unknown states of the catalyzed particulate filter such as temperature and particulate matter mass distribution and pressure drop (Δ P) using the sensor inputs to the engine electronic control unit and the reduced order multi-zone particulate filter model. A diesel oxidation catalyst estimator was also integrated with the catalyzed particulate filter estimator in order to provide estimates of diesel oxidation catalyst outlet concentrations of NO 2 and hydrocarbons and inlet temperature for the catalyzed particulate filter estimator. The combined diesel oxidation catalyst–catalyzed particulate filter estimator was validated for an active regeneration experiment. The validation results for catalyzed particulate filter temperature distribution showed that the root mean square temperature error by using the diesel oxidation catalyst–catalyzed particulate filter estimator is within 3.2 °C compared to the experimental data. Similarly, the Δ P estimator closely simulated the measured total Δ P and the estimated cake pressure drop error is within 0.2 kPa compared to the high-fidelity catalyzed particulate filter model.},
doi = {10.1177/1468087418785855},
journal = {International Journal of Engine Research},
number = 5,
volume = 21,
place = {United Kingdom},
year = {2018},
month = {7}
}

Journal Article:
Free Publicly Available Full Text
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DOI: https://doi.org/10.1177/1468087418785855

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Works referenced in this record:

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