LAPSE:2023.13862
Published Article
LAPSE:2023.13862
Model Predictive Supervisory Control for Integrated Emission Management of Diesel Engines
Johannes Ritzmann, Christian Peterhans, Oscar Chinellato, Manuel Gehlen, Christopher Onder
March 1, 2023
In this work, a predictive supervisory controller is presented that optimizes the interaction between a diesel engine and its aftertreatment system (ATS). The fuel consumption is minimized while respecting an upper bound on the emitted tailpipe NOx mass. This is achieved by optimally balancing the fuel consumption, the engine-out NOx emissions, and the ATS heating. The proposed predictive supervisory controller employs a two-layer model predictive control structure and solves the optimal control problem using a direct method. Through experimental validation, the resulting controller was shown to reduce the fuel consumption by 1.1% at equivalent tailpipe NOx emissions for the nonroad transient cycle when compared to the operation with a fixed engine calibration. Further, the controller’s robustness to different missions, initial ATS temperatures, NOx limits, and mispredictions was demonstrated.
Keywords
aftertreatment system, integrated emission management, Model Predictive Control, pollutant emissions, supervisory control, variable engine calibration
Suggested Citation
Ritzmann J, Peterhans C, Chinellato O, Gehlen M, Onder C. Model Predictive Supervisory Control for Integrated Emission Management of Diesel Engines. (2023). LAPSE:2023.13862
Author Affiliations
Ritzmann J: Department of Mechanical Engineering and Process Control, ETH Zürich, 8092 Zürich, Switzerland [ORCID]
Peterhans C: FPT Motorenforschung AG, 9320 Arbon, Switzerland
Chinellato O: FPT Motorenforschung AG, 9320 Arbon, Switzerland
Gehlen M: FPT Motorenforschung AG, 9320 Arbon, Switzerland
Onder C: Department of Mechanical Engineering and Process Control, ETH Zürich, 8092 Zürich, Switzerland
Journal Name
Energies
Volume
15
Issue
8
First Page
2755
Year
2022
Publication Date
2022-04-08
Published Version
ISSN
1996-1073
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Original Submission
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PII: en15082755, Publication Type: Journal Article
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LAPSE:2023.13862
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doi:10.3390/en15082755
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