The surge in electric vehicle (EV) adoption presents issues related to heightened power demand, requiring the incorporation of renewable energy sources like photovoltaic (PV) electricity. The transportation sector is a major source of carbon dioxide (CO2) emissions, and electric vehicles (EVs) provide a sustainable alternative to internal combustion engine vehicles to alleviate these environmental effects. Electric vehicle charging infrastructure is categorised into three types: public charging (e.g., destinations or roadside), semi-private charging (e.g., workplaces), and private charging (e.g., households). The suggested technology independently modifies charging tactics according to fluctuating energy prices, therefore lowering charging costs for electric vehicle users. A comparative analysis demonstrates that the proposed method attains a cost reduction of up to 70.2% relative to benchmark procedures, highlighting its efficacy. This study emphasises the potential of clever charging strategies in enhancing energy efficiency and cost-effectiveness in electric vehicle utilisation.


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    Titel :

    Optimizing Energy Management for Hybrid Renewable Energy Electric Vehicle Charging Stations Using Convolutional LSTM Models


    Beteiligte:
    Reddy, K Balaji Nanda Kumar (Autor:in) / G, Abhishek (Autor:in) / M, Muthaiah (Autor:in) / V, Nagendra (Autor:in) / B, Thanooja (Autor:in)


    Erscheinungsdatum :

    21.02.2025


    Format / Umfang :

    512072 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch