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    Analysis and Control of Torque Split in Hybrid Electric Vehicles by Incorporating Powertrain Dynamics

    Source: Journal of Dynamic Systems, Measurement, and Control:;2018:;volume( 140 ):;issue: 011::page 111009
    Author:
    Bidarvatan, Mehran
    ,
    Shahbakhti, Mahdi
    DOI: 10.1115/1.4040219
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Hybrid electric vehicle (HEV) energy management strategies usually ignore the effects from dynamics of internal combustion engines (ICEs). They usually rely on steady-state maps to determine the required ICE torque and energy conversion efficiency. It is important to investigate how ignoring these dynamics influences energy consumption in HEVs. This shortcoming is addressed in this paper by studying effects of engine and clutch dynamics on a parallel HEV control strategy for torque split. To this end, a detailed HEV model including clutch and ICE dynamic models is utilized in this study. Transient and steady-state experiments are used to verify the fidelity of the dynamic ICE model. The HEV model is used as a testbed to implement the torque split control strategy. Based on the simulation results, the ICE and clutch dynamics in the HEV can degrade the control strategy performance during the vehicle transient periods of operation by around 8% in urban dynamometer driving schedule (UDDS) drive cycle. Conventional torque split control strategies in HEVs often overlook this fuel penalty. A new model predictive torque split control strategy is designed that incorporates effects of the studied powertrain dynamics. Results show that the new energy management control strategy can improve the HEV total energy consumption by more than 4% for UDDS drive cycle.
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      Analysis and Control of Torque Split in Hybrid Electric Vehicles by Incorporating Powertrain Dynamics

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4253891
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    contributor authorBidarvatan, Mehran
    contributor authorShahbakhti, Mahdi
    date accessioned2019-02-28T11:12:48Z
    date available2019-02-28T11:12:48Z
    date copyright6/18/2018 12:00:00 AM
    date issued2018
    identifier issn0022-0434
    identifier otherds_140_11_111009.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4253891
    description abstractHybrid electric vehicle (HEV) energy management strategies usually ignore the effects from dynamics of internal combustion engines (ICEs). They usually rely on steady-state maps to determine the required ICE torque and energy conversion efficiency. It is important to investigate how ignoring these dynamics influences energy consumption in HEVs. This shortcoming is addressed in this paper by studying effects of engine and clutch dynamics on a parallel HEV control strategy for torque split. To this end, a detailed HEV model including clutch and ICE dynamic models is utilized in this study. Transient and steady-state experiments are used to verify the fidelity of the dynamic ICE model. The HEV model is used as a testbed to implement the torque split control strategy. Based on the simulation results, the ICE and clutch dynamics in the HEV can degrade the control strategy performance during the vehicle transient periods of operation by around 8% in urban dynamometer driving schedule (UDDS) drive cycle. Conventional torque split control strategies in HEVs often overlook this fuel penalty. A new model predictive torque split control strategy is designed that incorporates effects of the studied powertrain dynamics. Results show that the new energy management control strategy can improve the HEV total energy consumption by more than 4% for UDDS drive cycle.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleAnalysis and Control of Torque Split in Hybrid Electric Vehicles by Incorporating Powertrain Dynamics
    typeJournal Paper
    journal volume140
    journal issue11
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.4040219
    journal fristpage111009
    journal lastpage111009-11
    treeJournal of Dynamic Systems, Measurement, and Control:;2018:;volume( 140 ):;issue: 011
    contenttypeFulltext
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