Optimal EV Charging Using ANT Lion Optimized ANN Based MPPT with High Gain Modified Zeta Converter

Photovoltaic (PV) based power systems can be integrated with charging loads for Electric Vehicles (EVs) owing to their similar power forms, interfaces, and locations. Due to the random nature of solar generation and EV charging, this integration involves a high level of uncertainty, which demands robust control design and optimization to provide a constant power supply to the EV battery. Therefore, this research implements a novel optimized Artificial Neural Network based Maximum Power Point Tracking (ANN-MPPT) with High gain DC to DC converter for efficient EV battery charging. A highgain modified Zeta converter is deployed to convert the minimal voltage output of PV to a higher level of increased voltage. To maximize the power from the PV panel, the Ant Lion Optimized ANN (ALO-ANN) based MPPT is established in this work, which helps to preserve the stabilized DC link voltage. DC-AC conversion is accomplished using the High Frequency (HF) full bridge inverter, which provides a regulated high output voltage. Similarly, the HF isolation transformer operates to convert power while ensuring sufficient voltage balancing and galvanic isolation. A Proportional Integral controller (PI) is implemented to control the output of a proposed interleaved synchronous rectifier, which achieves the highest possible output to the battery for EVs by decreasing HF rectification losses while providing quicker transition response and input noise rejection. Furthermore, the proposed system’s functionality is validated by employing MATLAB simulation. According to the computational findings, the proposed system has a high-efficiency value of 93.6% and a better transient response to the system.