Renewable sources-based efficient electric vehicle charging system through power forecasting and charge scheduling

The increased rate of battery-limited Electric Vehicles (EVs) creates inadequacy in the efficient charging of vehicles. This results in EV Charging (EVC) Scheduling (EVCS) issue, which occurs due to the restricted path finding. Most of the prevailing works did not focus on the EVCS issue as well as the required charging amount and charging option for the efficient charging of EVs. Therefore, in this paper, an efficient charging system is developed by predicting the energy requirement and scheduling the charge accordingly. The process initiates from the gathering of energy from renewable sources, namely solar PV and Wind Turbines. Then, their powers are converted into AC power and stored in the battery. Further, power forecasting is done using the 2NF-DenseNet algorithm. It determines whether the obtained power is enough to schedule the EV efficiently. Based on the predicted power, the optimal charge scheduling is optimally performed by using the proposed Si-BEOA method. The experimental outcome exhibits that the proposed approach scheduled charge within 5 s and forecasted power accurately with 0.85% MAPE and 113.24% MAE when compared with the existing techniques.