Optimized control techniques for enhancing the buck converter performance in lithium-ion battery charging

The rapid proliferation of electric vehicles (EVs) has necessitated the development of high-performance, reliable, and efficient battery charging systems, with a particular emphasis on optimizing power electronic converter control strategies. Conventional proportional-integral (PI) and proportional-integral-derivative (PID) controllers, though extensively used in DC-DC buck converters for lithium-ion battery charging, frequently encounter limitations in managing system nonlinearities, dynamic load variations, and parametric uncertainties, thereby impairing transient response, increasing overshoot, and compromising steady-state accuracy. In response to these persistent challenges, this study proposes a comprehensive comparative analysis of intelligent control techniques—namely, fuzzy logic controllers (FLC), artificial neural networks (ANN), and adaptive neuro-fuzzy inference systems (ANFIS)—benchmarking them against traditional PI and PID controllers for regulating lithium-ion battery charging via a buck converter topology. This investigation integrates both MATLAB/Simulink-based simulations and real-time Hardware-in-the-Loop (HIL) implementation using the OPAL-RT platform, allowing for a comprehensive evaluation of controller performance under practical operating conditions. Performance is assessed based on overshoot percentage, rise time, settling time, current ripple, steady-state error, and integrated error metrics. The proposed ANFIS-PI controller achieved an overshoot of 2.2 %, a settling time of 0.8 ms, and a ripple of 1.583 A, with the lowest integrated absolute error (IAE) of 0.484 under real-time OPAL-RT HIL validation, outperforming other conventional and intelligent control strategies. The results decisively indicate that intelligent controllers, particularly the ANFIS approach, deliver markedly enhanced dynamic behaviour, superior accuracy, and reduced current ripple relative to their conventional counterparts. This integrated simulation and real-time validation substantiates the practical feasibility and robustness of optimized ANFIS-based control strategies for high-performance EV battery charging systems.