Hybrid Intelligent Controller Design for Advanced EV Onboard Chargers

Abstract

The rapid advancement of electric vehicles (EVs) has increased the demand for highly efficient, compact, and intelligent onboard charging systems. Traditional onboard chargers rely on conventional control strategies such as PID or fixed-parameter controllers, which often struggle to handle nonlinear dynamics, varying load conditions, and bidirectional power flow requirements. This study proposes a Hybrid Intelligent Controller (HIC) for Advanced EV Onboard Chargers, integrating artificial intelligence-based adaptive control with classical control theory and power electronic optimization techniques. The hybrid controller combines neural network-based prediction, fuzzy logic decision-making, and robust feedback control to improve charging efficiency, stability, and dynamic response. The proposed system is evaluated under varying load conditions, grid disturbances, and battery state-of-charge variations. Performance metrics include efficiency, voltage stability, harmonic distortion, and response time. Experimental results demonstrate superior performance compared to conventional control strategies in EV onboard charging systems.