Offering better fuel economy and lower emissions than conventional vehicles, plug-in hybrid electric vehicles (PHEVs) are promising near-term options for high efficiency, 'sustainable' transportation. It has recently been found that these efficiency benefits can be further improved with access to upcoming trip and driving conditions. This study is organised into two parts: in part I, upcoming trip data is used to find the optimal battery state of charge (SOC) trajectory of our PHEV model that will help minimise the total cost of electricity and fossil fuel. In part II, the optimum SOC trajectory is applied within the real-time controller to optimally distribute propulsion power between two energy sources. Autonomie was used to develop and implement a high fidelity PHEV model. The optimal SOC trajectory which has been found by real-time optimisation technique is in close agreement with the global optimum solution of Dynamic Programming. Moreover, the real-time technique is much less computationally expensive.


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    Titel :

    Intelligent power management of plug-in hybrid electric vehicles, part I: real-time optimum SOC trajectory builder


    Beteiligte:


    Erscheinungsdatum :

    2014


    Format / Umfang :

    22 Seiten, Bilder, Tabellen, Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




    Intelligent power management of plug–in hybrid electric vehicles, part I: real–time optimum SOC trajectory builder

    Vajedi, Mahyar / Chehrehsaz, Maryyeh / Azad, Nasser L. | British Library Online Contents | 2014



    Intelligent power management of plug-in hybrid electric vehicles, part I: real-time optimum SOC trajectory builder

    Vajedi, M. / Chehrehsaz, M. / Azad, N.L. | British Library Online Contents | 2014