DESIGN AND PERFORMANCE ANALYSIS OF ANFIS-CONTROLLED BIDIRECTIONAL DC–DC CONVERTER FOR EV CHARGING

Abstract

The rapid growth of electric vehicles (EVs) has intensified the need for efficient and stable energy management systems capable of minimizing output fluctuations and improving overall performance. In EV applications, the energy storage unit—primarily composed of lithium-ion batteries and auxiliary storage elements such as supercapacitors—plays a critical role in ensuring reliable power delivery and regenerative energy utilization. However, conventional charging and control strategies often suffer from voltage instability and performance distortion under dynamic operating conditions. This paper presents a dynamically designed bidirectional DC–DC converter integrated with a hybrid energy storage system for enhanced EV charging and discharging operations. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based intelligent control strategy is proposed to regulate power flow between the battery and supercapacitor while maintaining optimal state-of-charge conditions. The controller employs rule-based dynamic constraint management to improve energy distribution and reduce transient fluctuations. Simulation results demonstrate that the proposed ANFIS-controlled system achieves improved voltage stability, reduced output distortion, and enhanced energy efficiency compared to conventional control approaches. The proposed framework contributes to improved endurance, reliability, and cost-effective operation of electric vehicle energy storage systems.