INTELLIGENT BATTERY CHARGING USING ANN-BASED THREE-PHASE INTERLEAVED BUCK CONVERTER

Abstract

The increasing demand for electric vehicles (EVs), energy storage systems, and industrial battery applications necessitates the development of intelligent battery charging systems with high efficiency, improved charging performance, and enhanced battery lifespan. This paper proposes an Artificial Neural Network (ANN)-based control strategy for a three-phase interleaved buck converter employed in battery charging applications. The interleaved converter topology offers reduced current ripple, improved thermal distribution, increased power density, and enhanced efficiency compared with conventional single-phase buck converters. The proposed ANN controller intelligently regulates the charging process by adapting to battery conditions and varying load requirements. The controller generates optimal duty cycle commands to maintain desired charging profiles while minimizing overshoot and improving transient response. The charging system operates under Constant Current–Constant Voltage (CC–CV) charging modes to ensure safe and efficient battery charging. MATLAB/Simulink simulations are performed under different battery states of charge (SOC) and load conditions to validate the effectiveness of the proposed strategy. The results demonstrate superior charging efficiency, reduced output current ripple, faster dynamic response, and improved battery protection compared with traditional PI-based charging controllers.