Model Predictive Supervisory Control for Integrated Emission Management of Diesel Engines

Johannes Ritzmann, Christian Peterhans, Oscar Chinellato, Manuel Gehlen and Christopher Onder
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Johannes Ritzmann: Department of Mechanical Engineering and Process Control, ETH Zürich, 8092 Zürich, Switzerland
Christian Peterhans: FPT Motorenforschung AG, 9320 Arbon, Switzerland
Oscar Chinellato: FPT Motorenforschung AG, 9320 Arbon, Switzerland
Manuel Gehlen: FPT Motorenforschung AG, 9320 Arbon, Switzerland
Christopher Onder: Department of Mechanical Engineering and Process Control, ETH Zürich, 8092 Zürich, Switzerland

Energies, 2022, vol. 15, issue 8, 1-22

Abstract: 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 NO x mass. This is achieved by optimally balancing the fuel consumption, the engine-out NO x 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 NO x 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, NO x limits, and mispredictions was demonstrated.

Keywords: integrated emission management; variable engine calibration; pollutant emissions; aftertreatment system; supervisory control; model predictive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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