With the expectation that series configuration would maximise the fuel economy as engine is decoupled from the wheels, a forward-facing, series hydraulic hybrid vehicle (SHHV) powertrain model for medium size trucks is developed in this paper, and is employed to investigate the application of stochastic model predictive control (SMPC) methodology for developing power management strategy. For properly managing all the control variables that arise from the significant freedoms introduced by the SHHV powertrain, two-level hierarchical control architecture is proposed. Simulation results over the urban driving cycle are presented to demonstrate the effectiveness of SMPC compared with other deterministic approaches and the potential of the selected hybrid system to substantially improve vehicle fuel economy.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Stochastic model predictive power management strategy for series hydraulic hybrid vehicle


    Contributors:
    Feng, Daiwei (author) / Huang, Dagui (author)


    Publication date :

    2012


    Size :

    13 Seiten, 11 Bilder, 1 Tabelle, 25 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




    Power management strategy for a parallel hydraulic hybrid passenger vehicle using stochastic dynamic programming

    Meyer, Jonathan J. / Stelson, Kim A. / Alleyne, Andrew G. et al. | Tema Archive | 2010


    Power management strategies for a series hydraulic hybrid drivetrain

    Molla,S. / Ayalew,B. / Clemson Univ.,US | Automotive engineering | 2011


    A neurofuzzy-controlled power management strategy for a series hybrid electric vehicle

    Chindamo, Daniel / Economou, John T / Gadola, Marco et al. | SAGE Publications | 2014


    A neurofuzzy-controlled power management strategy for a series hybrid electric vehicle

    Chindamo, Daniel / Economou, John T. / Gadola, Marco et al. | Tema Archive | 2014