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    A Receding-Horizon Framework for Co-Optimizing the Velocity and Power-Split of Automated Plug-In Hybrid Electric Vehicles

    Source: ASME Letters in Dynamic Systems and Control:;2021:;volume( 001 ):;issue: 004::page 041006-1
    Author:
    Chen, Di
    ,
    Huang, Mike
    ,
    Stefanopoulou, Anna
    ,
    Kim, Youngki
    DOI: 10.1115/1.4050191
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This paper presents a control framework to co-optimize the velocity and power-split operation of a plug-in hybrid vehicle (PHEV) online in the presence of traffic constraints. The principal challenge in its online implementation lies in the conflict between the long control horizon required for global optimality and limits in available computational power. To resolve the conflict between the length of horizon and its computation complexity, we propose a receding-horizon strategy where co-states are used to approximate the future cost, helping to shorten the prediction horizon. In particular, we update the co-state using a nominal trajectory and the temporal-difference (TD) error based on co-state dynamics. Our simulation results demonstrate a 12% fuel economy improvement over the sequential/layered control strategy for a given driving scenario. Moreover, its real-time practicality is evidenced by a computation time per model predictive controller (MPC) step on average of around 80 ms within a 10 s prediction horizon.
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      A Receding-Horizon Framework for Co-Optimizing the Velocity and Power-Split of Automated Plug-In Hybrid Electric Vehicles

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4277115
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    contributor authorChen, Di
    contributor authorHuang, Mike
    contributor authorStefanopoulou, Anna
    contributor authorKim, Youngki
    date accessioned2022-02-05T22:12:11Z
    date available2022-02-05T22:12:11Z
    date copyright3/11/2021 12:00:00 AM
    date issued2021
    identifier issn2689-6117
    identifier otheraldsc_1_4_041006.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4277115
    description abstractThis paper presents a control framework to co-optimize the velocity and power-split operation of a plug-in hybrid vehicle (PHEV) online in the presence of traffic constraints. The principal challenge in its online implementation lies in the conflict between the long control horizon required for global optimality and limits in available computational power. To resolve the conflict between the length of horizon and its computation complexity, we propose a receding-horizon strategy where co-states are used to approximate the future cost, helping to shorten the prediction horizon. In particular, we update the co-state using a nominal trajectory and the temporal-difference (TD) error based on co-state dynamics. Our simulation results demonstrate a 12% fuel economy improvement over the sequential/layered control strategy for a given driving scenario. Moreover, its real-time practicality is evidenced by a computation time per model predictive controller (MPC) step on average of around 80 ms within a 10 s prediction horizon.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Receding-Horizon Framework for Co-Optimizing the Velocity and Power-Split of Automated Plug-In Hybrid Electric Vehicles
    typeJournal Paper
    journal volume1
    journal issue4
    journal titleASME Letters in Dynamic Systems and Control
    identifier doi10.1115/1.4050191
    journal fristpage041006-1
    journal lastpage041006-6
    page6
    treeASME Letters in Dynamic Systems and Control:;2021:;volume( 001 ):;issue: 004
    contenttypeFulltext
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