We present a solution for intelligent planning of engine activations for series hybrid electric vehicles (HEVs), Beyond minimizing energy expenditure, other real-world objectives must be incorporated, such as minimizing the perceived engine noise and the frequency of mode transitions between activation and deactivation. We model this problem as a multiobjective stochastic shortest path (MOSSP) problem that takes a vehicle model and navigation map as input and outputs a engine activation policy. The vehicle model and navigation map are learned from GPS traces with metadata, and includes the topological road structure, traversal speeds/times, battery consumption/regeneration, and ambient noise. We analyze our results in simulation on different navigation maps generated from actual GPS traces learned from a real series HEV. Experiments in simulation demonstrate that our approach compared with the baseline system can reduce total energy expenditure (EE), namely on hills, by up to 3%; total additional noise (AN) generated by up to 15%; and total mode transition (MT) frequency by up to 12%. The approach is demonstrated on a real series hybrid vehicle, driving on real public roads.


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    Titel :

    Engine Activation Planning for Series Hybrid Electric Vehicles


    Beteiligte:
    Wray, Kyle Hollins (Autor:in) / Lui, Richard (Autor:in) / Pedersen, Liam (Autor:in)


    Erscheinungsdatum :

    11.07.2021


    Format / Umfang :

    7985723 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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