An effective online PMS is critical for the future electrified vehicles as it has direct impacts on fuel economy, greenhouse gasses (GHG) emission, as well as the durability of power-train components. In this paper, we considered a PMS developed based on the concept of model predictive control (MPC) which formulates the control design as an optimization problem that is solved based on forecasted system behaviors over a limited prediction horizon to obtain the optimal control actions for the current time instant. The main contribution of this paper is that we take a systematic approach to examine the link between model fidelity and controller performance for the case of a hybrid energy storage system in a light-duty hybrid electric vehicle. A sensitivity analysis approach is developed and presented in this paper along with preliminary simulation results to demonstrate the impact of battery model fidelity on the performance of the proposed PMS.


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    Titel :

    Sensitivity analysis of the battery model for model predictive control implemented into a plug-in hybrid electric vehicle


    Beteiligte:
    Sockeel, Nicolas (Autor:in) / Shi, Jian (Autor:in) / Shahverdi, Masood (Autor:in) / Mazzola, Michael (Autor:in)


    Erscheinungsdatum :

    01.06.2017


    Format / Umfang :

    2201744 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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