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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    Title :

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


    Contributors:


    Publication date :

    2025-02-21


    Size :

    512072 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English