Hybrid PV-Fuel Cell Powered EV Charging System With Intelligent State-Of-Charge Monitoring And Control Using RNN

This paper introduces an innovative hybrid power system for Electric Vehicle (EV) charging that integrates Photovoltaic (PV) panels and fuel cells, incorporating advanced intelligent State-of-Charge (SoC) monitoring and control using Recurrent Neural Networks (RNNs). The proposed system enhances EV charging efficiency and reliability by merging renewable solar energy with fuel cell technology. It addresses the intermittent nature of solar power with a boost converter for the fuel cells, which elevates the Fuel Cell (FC) output to a suitable level for efficient energy transfer. Additionally, bidirectional dc-dc converter employed to EV battery allowing for both charging and discharging operations. The RNN model forecasts energy availability and consumption patterns, optimizing power allocation among PV panels, fuel cells, and the EV battery. By accurately predicting SoC and energy demands, the system manages and balances power supply efficiently, minimizing reliance on non-renewable sources and reducing charging time. With an effectiveness rating of $91 \%$, validated through simulations and experimental results, the system demonstrates significant potential to revolutionize EV charging infrastructure by providing a reliable, efficient, and eco-friendly solution. The proposed design is evaluated and the simulation results are obtained in MATLAB platform with reduced THD of $0.89 \%$.