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    Model-Based Predictive Control and Dithering Control for an Integrated Gasoline Engine and Three-Way Catalytic Converter System

    Source: Journal of Dynamic Systems, Measurement, and Control:;2021:;volume( 143 ):;issue: 009::page 091007-1
    Author:
    Yang, Kuo
    ,
    Chen, Pingen
    DOI: 10.1115/1.4050846
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Controls of integrated gasoline engine and aftertreatment systems are critical for fuel efficiency improvement and emission regulation. This paper aims to develop novel model-based three-way catalytic converter (TWC) controls to reduce the fuel consumption and tailpipe emissions for a gasoline engine. A model-based dither control and a nonlinear model predictive control (MPC)-based control are presented, respectively. The proposed TWC dither control utilizes a systematically designed dither cycle configuration (including dithering amplitude, offset, and frequency) based on a control-oriented model, with the capability to adapt the dither cycle configuration to various engine operating conditions. The MPC control can optimize engine air–fuel ratio (AFR) to maintain the oxygen storage of TWC at a desired level and thus meet the tailpipe NOx, CO, and HC emission requirements. The efficacies of both model-based TWC controls are validated in simulation with MPC control improving CO emission conversion efficiencies by 8.42% and 4.85% in simplified US06 and urban dynamometer driving schedule (UDDS) driving cycles, when compared to a baseline dithering-based AFR control. Meanwhile, NOx emission conversion efficiency is maintained above the required limit of 95%, while the fuel efficiency remains at the same level as the baseline control methodology.
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      Model-Based Predictive Control and Dithering Control for an Integrated Gasoline Engine and Three-Way Catalytic Converter System

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    contributor authorYang, Kuo
    contributor authorChen, Pingen
    date accessioned2022-02-06T05:26:44Z
    date available2022-02-06T05:26:44Z
    date copyright5/13/2021 12:00:00 AM
    date issued2021
    identifier issn0022-0434
    identifier otherds_143_09_091007.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4278037
    description abstractControls of integrated gasoline engine and aftertreatment systems are critical for fuel efficiency improvement and emission regulation. This paper aims to develop novel model-based three-way catalytic converter (TWC) controls to reduce the fuel consumption and tailpipe emissions for a gasoline engine. A model-based dither control and a nonlinear model predictive control (MPC)-based control are presented, respectively. The proposed TWC dither control utilizes a systematically designed dither cycle configuration (including dithering amplitude, offset, and frequency) based on a control-oriented model, with the capability to adapt the dither cycle configuration to various engine operating conditions. The MPC control can optimize engine air–fuel ratio (AFR) to maintain the oxygen storage of TWC at a desired level and thus meet the tailpipe NOx, CO, and HC emission requirements. The efficacies of both model-based TWC controls are validated in simulation with MPC control improving CO emission conversion efficiencies by 8.42% and 4.85% in simplified US06 and urban dynamometer driving schedule (UDDS) driving cycles, when compared to a baseline dithering-based AFR control. Meanwhile, NOx emission conversion efficiency is maintained above the required limit of 95%, while the fuel efficiency remains at the same level as the baseline control methodology.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleModel-Based Predictive Control and Dithering Control for an Integrated Gasoline Engine and Three-Way Catalytic Converter System
    typeJournal Paper
    journal volume143
    journal issue9
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.4050846
    journal fristpage091007-1
    journal lastpage091007-10
    page10
    treeJournal of Dynamic Systems, Measurement, and Control:;2021:;volume( 143 ):;issue: 009
    contenttypeFulltext
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