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    An Investigation of Dynamic Behavior of Electric Vehicle Gear Trains

    Source: Journal of Computational and Nonlinear Dynamics:;2024:;volume( 020 ):;issue: 002::page 21003-1
    Author:
    Donmez, Ata
    ,
    Kahraman, Ahmet
    DOI: 10.1115/1.4067153
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The automotive industry has been experiencing a significant transition toward electrified powertrains in recent years. A torsional model of a common type of electric vehicle (EV) drivetrains is proposed to demonstrate certain dynamic behaviors that are unique to such high-speed applications. This two-stage helical gear drive train is supported by three shafts and connects the electric motor to the vehicle axle. The gear mesh interfaces are modeled by periodically time-varying stiffnesses subjected to backlash and displacement excitations to represent gear tooth errors and modifications. In addition to these internal excitations, torque fluctuations caused by electric motor are included as the external excitations. Two different operating conditions are studied here: (i) steady-state response as the vehicle is operated under steady torque conditions and (ii) transient response during EV system transitions between the drive and regenerative (regen) braking modes of operation. The torsional model predictions are verified through comparisons to simulations from a deformable-body contact model. Parameter sensitivity studies are performed to demonstrate nonlinear behavior of a helical gear train caused by external torque fluctuations as well as the interactions between external and internal excitations. Finally, drivetrain structural modes are shown to respond to drive-regen transitions resulting in certain transient (vibro-impact) behavior with elevated dynamic mesh forces.
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      An Investigation of Dynamic Behavior of Electric Vehicle Gear Trains

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4305532
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    contributor authorDonmez, Ata
    contributor authorKahraman, Ahmet
    date accessioned2025-04-21T10:07:06Z
    date available2025-04-21T10:07:06Z
    date copyright12/9/2024 12:00:00 AM
    date issued2024
    identifier issn1555-1415
    identifier othercnd_020_02_021003.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305532
    description abstractThe automotive industry has been experiencing a significant transition toward electrified powertrains in recent years. A torsional model of a common type of electric vehicle (EV) drivetrains is proposed to demonstrate certain dynamic behaviors that are unique to such high-speed applications. This two-stage helical gear drive train is supported by three shafts and connects the electric motor to the vehicle axle. The gear mesh interfaces are modeled by periodically time-varying stiffnesses subjected to backlash and displacement excitations to represent gear tooth errors and modifications. In addition to these internal excitations, torque fluctuations caused by electric motor are included as the external excitations. Two different operating conditions are studied here: (i) steady-state response as the vehicle is operated under steady torque conditions and (ii) transient response during EV system transitions between the drive and regenerative (regen) braking modes of operation. The torsional model predictions are verified through comparisons to simulations from a deformable-body contact model. Parameter sensitivity studies are performed to demonstrate nonlinear behavior of a helical gear train caused by external torque fluctuations as well as the interactions between external and internal excitations. Finally, drivetrain structural modes are shown to respond to drive-regen transitions resulting in certain transient (vibro-impact) behavior with elevated dynamic mesh forces.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleAn Investigation of Dynamic Behavior of Electric Vehicle Gear Trains
    typeJournal Paper
    journal volume20
    journal issue2
    journal titleJournal of Computational and Nonlinear Dynamics
    identifier doi10.1115/1.4067153
    journal fristpage21003-1
    journal lastpage21003-16
    page16
    treeJournal of Computational and Nonlinear Dynamics:;2024:;volume( 020 ):;issue: 002
    contenttypeFulltext
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