PERFORMANCE ENHANCEMENT OF PMD BATTERY CHARGER USING ANFIS ASSISTED SLIDING MODE CONTROL

Abstract

Battery charging systems play a crucial role in electric vehicles, renewable energy systems, and portable electronic applications. Conventional battery chargers often suffer from slow transient response, reduced efficiency, and poor robustness under parameter variations and load disturbances. This paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) assisted Sliding Mode Control (SMC) strategy for enhancing the performance of a PMD (Permanent Magnet Drive)-based battery charger. The SMC provides strong robustness against uncertainties and external disturbances, while the ANFIS mechanism adaptively tunes the control parameters to minimize chattering effects and improve dynamic response. The proposed controller ensures accurate regulation of charging current and battery voltage during constant-current and constant-voltage charging modes. MATLAB/Simulink simulations are performed under varying operating conditions to validate the effectiveness of the proposed method. The results demonstrate improved charging efficiency, reduced settling time, lower overshoot, enhanced state-of-charge regulation, and superior robustness compared with conventional PI and standalone SMC approaches.