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    Optimal Gear Ratio Planning for Flywheel Based Kinetic Energy Recovery Systems in Motor Vehicles

    Source: Journal of Dynamic Systems, Measurement, and Control:;2015:;volume( 137 ):;issue: 007::page 71012
    Author:
    Dunne, J. F.
    ,
    Ponce Cuspinera, L. A.
    DOI: 10.1115/1.4029929
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: An efficient computational methodology is proposed for optimal gear ratio planning in motor vehicle kinetic energy recovery systems (KERS) using a flywheel and continuously variable transmission (CVT). Initial modeling of a clutchless KERS, comprising an input wheel, CVT, flywheel, and bearings, shows that the “least effortâ€‌ or “minimum energy lossâ€‌ optimal control problem can be formulated in two ways: one being a conventional twostate formulation involving input wheel angular velocity and CVT gear ratio, for which least effort control can be solved in simple cases with Pontryagin's maximum principle. The second formulation involves a singlestate CVT gear ratio equation for which the input wheel angular velocity and acceleration appear as unknown timedependent parameters. A novel multiparameter optimization methodology is proposed using the singlestate formulation to find optimal CVT gear ratios by adopting two discrete time scales: one being a small time scale for numerical integration of the model, and the second involving discrete transitions, hundreds of times larger. Using Chebyshev polynomial expansions (CPEs) to initially generate sets of zeroenergyloss least effort kinematics for use as the timedependent parameters in the CVT gear ratio equation, two solution approaches are developed. The first involves a single large discrete time transition, which only requires discretization of the input wheel angular acceleration at the start and endoftransition. The second approach involves multiple largescale discrete time transitions as a generalization of the first, but additionally needing discretization of the input wheel angular velocity, and the CVT gear ratio, plus dynamic programming to find the optimum. Both approaches are tested using the clutchless KERS model by assuming a “super CVTâ€‌ gear ratio range (but with no restrictions for use with slipping clutches). Comparison with least effort control via Pontryagin's maximum principle shows that the single transition approach is in practice far superior. The single transition approach is then used to compare a minimum energy loss clutchless KERS gear ratio plan, with one obtained using constant input wheel angular acceleration as a benchmark. This comparison, involving power losses throughout the KERS, shows the very clear benefits of adopting an optimal gear ratio plan.
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      Optimal Gear Ratio Planning for Flywheel Based Kinetic Energy Recovery Systems in Motor Vehicles

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    http://yetl.yabesh.ir/yetl1/handle/yetl/157565
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    • Journal of Dynamic Systems, Measurement, and Control

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    contributor authorDunne, J. F.
    contributor authorPonce Cuspinera, L. A.
    date accessioned2017-05-09T01:16:35Z
    date available2017-05-09T01:16:35Z
    date issued2015
    identifier issn0022-0434
    identifier otherds_137_07_071012.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/157565
    description abstractAn efficient computational methodology is proposed for optimal gear ratio planning in motor vehicle kinetic energy recovery systems (KERS) using a flywheel and continuously variable transmission (CVT). Initial modeling of a clutchless KERS, comprising an input wheel, CVT, flywheel, and bearings, shows that the “least effortâ€‌ or “minimum energy lossâ€‌ optimal control problem can be formulated in two ways: one being a conventional twostate formulation involving input wheel angular velocity and CVT gear ratio, for which least effort control can be solved in simple cases with Pontryagin's maximum principle. The second formulation involves a singlestate CVT gear ratio equation for which the input wheel angular velocity and acceleration appear as unknown timedependent parameters. A novel multiparameter optimization methodology is proposed using the singlestate formulation to find optimal CVT gear ratios by adopting two discrete time scales: one being a small time scale for numerical integration of the model, and the second involving discrete transitions, hundreds of times larger. Using Chebyshev polynomial expansions (CPEs) to initially generate sets of zeroenergyloss least effort kinematics for use as the timedependent parameters in the CVT gear ratio equation, two solution approaches are developed. The first involves a single large discrete time transition, which only requires discretization of the input wheel angular acceleration at the start and endoftransition. The second approach involves multiple largescale discrete time transitions as a generalization of the first, but additionally needing discretization of the input wheel angular velocity, and the CVT gear ratio, plus dynamic programming to find the optimum. Both approaches are tested using the clutchless KERS model by assuming a “super CVTâ€‌ gear ratio range (but with no restrictions for use with slipping clutches). Comparison with least effort control via Pontryagin's maximum principle shows that the single transition approach is in practice far superior. The single transition approach is then used to compare a minimum energy loss clutchless KERS gear ratio plan, with one obtained using constant input wheel angular acceleration as a benchmark. This comparison, involving power losses throughout the KERS, shows the very clear benefits of adopting an optimal gear ratio plan.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleOptimal Gear Ratio Planning for Flywheel Based Kinetic Energy Recovery Systems in Motor Vehicles
    typeJournal Paper
    journal volume137
    journal issue7
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.4029929
    journal fristpage71012
    journal lastpage71012
    identifier eissn1528-9028
    treeJournal of Dynamic Systems, Measurement, and Control:;2015:;volume( 137 ):;issue: 007
    contenttypeFulltext
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