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

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.