2. ARTIFICIAL NEURAL NETWORK-BASED BATTERY MANAGEMENT AND POWER CONTROL IN A BIDIRECTIONAL MULTI-LEVEL CONVERTER FOR ELECTRIC VEHICLE

Electric Vehicles (EVs) have emerged as a sustainable alternative to conventional transportation, necessitating advanced energy management strategies for effective battery utilization. This project presents the modeling, simulation, and performance analysis of a smart Battery Management System (BMS) integrated with a Bidirectional DC -DC Converter, controlled using an Artificial Neural Network (ANN). The proposed system demonstrates intelligent battery selection, accurate SoC estimation, and stable voltage r egulation. It was developed in MATLAB/Simulink and validated by dynamic simulation scenarios. The initiative aims to contribute to the design of intelligent EV charging infrastructure in order to guarantee battery longevity and enhanced system performance. Suggested system, which is created in MATLAB/Simulink and verified by dynamic simulation scenarios, exhibits intelligent battery selection, precise SoC estimation, and stable voltage regulation. In order to ensure battery longevity and improved system p erformance, the effort intends to contribute to the design of intelligent EV charging infrastructure.