Global power demand is driving the need to find an alternative source of energy to address the growing power issue. The integration of electric vehicles (EVs) into the power grid significantly affects the entire power system, introducing challenges such as supply-demand imbalances and fluctuations in voltage and frequency. Addressing these issues, vehicle-to-grid (V2G) technology emerges as a promising solution, facilitating seamless energy exchange between EVs and the grid. This paper is proposed to enhance the interaction between EVs and the grid by employing a converter alongside the Artificial Neural Network Maximum Power Point Tracking (ANN-MPPT) method. Through this approach, uninterrupted EV charging is ensured while alleviating strain on the grid caused by voltage and current irregularities. The system uses a Relift-Luo converter to harness solar energy, with the ANN-MPPT technique optimizing power extraction from solar panels. Furthermore, the integration of a battery system with a bidirectional battery converter enables effective energy management within the system. RNN controller is presented to manage the three phase VSI's functioning, which is connected to the three phase grid. Through the use of a MATLAB simulation, the entire system is verified (2021a). The proposed converter has an efficiency rate of 93.47% and that the accuracy of the ANN controller is 89.38%. © 2024 Elsevier B.V., All rights reserved.