Design And Implementation Of An AI-Enhanced PV System With MIS Integration For Electric Vehicles

Abstract

Electric vehicles (EVs) powered by solar energy present a low-maintenance, eco-friendly transportation solution. A significant limitation of current EVs is their limited range, which can be mitigated by integrating solar photovoltaic (PV) panels that charge the vehicle's battery while in motion. This approach eliminates the need for mechanical components like the gearbox and differential, facilitating a more efficient direct drive to the wheels.

Our research examines the operational principles of electric vehicles, focusing on the use of brushless DC (BLDC) motors, which are commonly employed in EVs. These motors are typically powered by a 48V 39Ah lead-acid battery. The battery is recharged by connecting it to a standard wall socket (220-230V AC supply) using a dedicated battery charger. However, the widespread adoption of such vehicles can strain the electrical grid due to increased load demands.

To address this, we propose an AI-enhanced PV system integrated with maximum power point tracking (MPPT) charge controllers. This system optimizes the charging efficiency of the solar panels, ensuring that the battery is charged effectively under various environmental conditions. By incorporating artificial intelligence, the PV system can adapt to changing sunlight conditions, enhancing the overall efficiency and reliability of the electric vehicle.