Artificial Intelligence Techniques for Dual-Stage Interleaved Electric Vehicle Onboard Chargers: Trends and Challenges - D'Costa, Myeong (International Journal on Advanced Computer Engineering and Communication Technology, 2023)
The rapid global adoption of electric vehicles has increased the demand for efficient, compact, and intelligent onboard charging systems. Dual-stage interleaved onboard chargers have emerged as a preferred architecture due to their ability to reduce current ripple, improve power density, enhance thermal performance, and achieve high efficiency with low harmonic distortion. This paper presents a comprehensive review of advanced control and optimization techniques for dual-stage interleaved onboard chargers. It highlights the use of PIDD2-PD controllers, which extend classical PID structures by incorporating higher-order derivatives for improved transient response, disturbance rejection, and steady-state accuracy. The study further explores the Hybrid Adaptive Genghis Khan Shark Gold Rush (HAGKSGR) algorithm for optimizing controller parameters and switching strategies, enabling efficient handling of nonlinear and multi-objective optimization challenges in dynamic charging environments. Applications include Level 1 and Level 2 electric vehicle charging systems, focusing on improving power factor, minimizing total harmonic distortion, and enhancing overall system efficiency. Simulation and hardware-in-the-loop studies demonstrate superior performance compared to conventional control and optimization methods. However, challenges such as computational complexity, real-time implementation, and scalability remain. This review emphasizes the potential of integrating advanced control architectures with hybrid metaheuristic optimization to develop high-performance onboard charging systems for next-generation electric vehicles.