Real-time Empirical NOx Model Based on In-cylinder Pressure Measurements for Light-duty Diesel Engines
This paper proposes a real-time empirical model of NOₓ emissions for diesel engines. The proposed model predicts the level of NOₓ emissions using an empirical model developed based on the thermal NO formation mechanism, the extended Zeldovich mechanism. Since it is difficult to consider the exact physical NO formation phenomena in real-time applications, the proposed algorithm adapts the key factors of the NO formation mechanism from the extended Zeldovich mechanism: temperature of the burned gas, concentration of the gas species, and combustion duration where NO is generated. These factors are considered in a prediction model as four parameters: exhaust gas recirculation rate (EGR rate), crank angle location of 50% of mass fraction burned (MFB50), exhaust lambda value, and combustion acceleration. The proposed prediction model is validated with various steady engine experiments that showed a high linear correlation with the NOₓ emission measured by a NOₓ sensor. Furthermore, it is also validated for transient experiments.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/12299138
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Supplemental Notes:
- Copyright © 2016, The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg.
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Authors:
- Chung, J
- Min, K
- Sunwoo, M
- Publication Date: 2016-8
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 549-554
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Serial:
- International Journal of Automotive Technology
- Volume: 17
- Issue Number: 4
- Publisher: Korean Society of Automotive Engineers
- ISSN: 1229-9138
- EISSN: 1976-3832
- Serial URL: http://link.springer.com/journal/12239
Subject/Index Terms
- TRT Terms: Algorithms; Combustion; Diesel engines; Exhaust gases; Pollutants; Real time control; Sensors; Temperature
- Subject Areas: Energy; Environment; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01603181
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 28 2016 4:22PM