State this study looks at how well a three-phase bidirectional converter works for Vehicle-to-Grid (V2G) services by using both Adaptive Neuro-Fuzzy Inference System (ANFIS) and Proportional-Integral (PI) controllers.When compared with ANFIS controllers

traditional controllers such as PI and PID show challenges.They may not sufficiently react to changing conditions or non-linearity’s and use fixed gain values requiring hand tuning.By means of learning

ANFIS controllers can thus dynamically change their parameters

so providing enhanced accuracy and flexibility in real-time control.The main objectives are to control the DC link voltage

lower total harmonic distortion (THD)

and lower the errors.The Synchronous Reference Frame (SRF) transformation changes three-phase AC into a two-axis (d-q) system

making it easier to control active and reactive power separately.We developed a thorough Simulink model in MATLAB 2023a to model the bidirectional off-board fast charger at a power level of 60 kW.After validation

a 5-kW hardware prototype was built in the lab.The main platform is an AC-DC converter

followed by a DC-DC converter.A programmable DC power supply

Chroma 62050H-600S

connected to the DC-DC converter

mimics the dynamic characteristics of a battery.The control algorithm

deployed on a Spartan-6 LX9 FPGA

manages both voltage and current

maintaining a stable DC link voltage of 800 V.The results obtained indicate that the ANFIS controller outperforms a conventional PI controller when handling dynamic load variations.