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.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Stochastic model predictive power management strategy for series hydraulic hybrid vehicle


    Beteiligte:
    Feng, Daiwei (Autor:in) / Huang, Dagui (Autor:in)


    Erscheinungsdatum :

    2012


    Format / Umfang :

    13 Seiten, 11 Bilder, 1 Tabelle, 25 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




    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 Archiv | 2010


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

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



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

    Chindamo,D. / Economou,J.T. / Gadola,M. et al. | Kraftfahrwesen | 2014


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

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