We tackle the challenge of learning to charge electric vehicles (EVs) with out-of-distribution (OOD) data. Traditional scheduling algorithms typically fail to balance near-optimal average performance with worst case guarantees, particularly with OOD data. Model predictive control (MPC) is often too conservative and data-independent, whereas reinforcement learning (RL) tends to be overly aggressive and fully trusts the data, hindering their ability to consistently achieve the best-of-both-worlds. To bridge this gap, we introduce a novel OOD-aware scheduling algorithm, denoted OOD-Charging. This algorithm uses a dynamic “awareness radius,” which updates in real-time based on the temporal difference (TD)-error that reflects the severity of OOD. The OOD-Charging algorithm allows for a more effective balance between consistency and robustness in EV charging schedules, thereby significantly enhancing adaptability and efficiency in real-world charging environments. Our results demonstrate that this approach improves the scheduling reward reliably under real OOD scenarios with remarkable shifts of EV charging behaviors caused by COVID-19 in the Caltech adaptive charging network (ACN)-Data.


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    Titel :

    Out-of-Distribution-Aware Electric Vehicle Charging


    Beteiligte:
    Li, Tongxin (Autor:in) / Sun, Chenxi (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.02.2025


    Format / Umfang :

    2110066 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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