Comparative Studies of Several Modern Control Techniques To Harvest The Maximum Energy From A PV System In Order To Charge The Battery of An Electric Vehicle Under Different Irradiances

The study highlights the growing need for efficient and effective methods to charge EV batteries from a PV system. In this regard, the study investigates three different MPPT-based control methods and compares their performance through various performance metrics, including charging time, efficiency, and voltage stability. The double sliding mode control method employs nonlinear feedback control, enabling fast and accurate tracking of the maximum power point. The fuzzy logic control method utilizes a set of rules based on input variables and can handle uncertainties and nonlinearities in the system. The neural network control method relies on interconnected nodes that are trained to recognize patterns and adjust charging parameters accordingly. The simulation results demonstrate that all three control methods can successfully charge the EV battery and achieve good performance in terms of charging time and efficiency. However, the neural network control method outperforms the other methods by achieving a shorter charging time and higher efficiency. The findings of this study have significant implications for the widespread adoption of EVs powered by renewable energy sources, such as PV systems. The MPPT-based control methods investigated in this study have the potential to significantly improve the performance of EV battery charging systems, which will ultimately contribute to reducing the carbon footprint of the transportation sector. Although the study demonstrates the potential of MPPT-based control methods, further research is needed to validate these results in a real-world setting and explore other potential control methods for EV battery charging.