Dual-Converter Control Strategies for Electric-Vehicle Battery Charging: A Review

Abstract

Electric vehicle charging systems are a major factor where modern transportation will happen, specifically as there is a growing demand for fast, efficient, and good quality charging stations to be built. The use of advanced power-electronic converters and control systems are critical for both enhancing charging efficiency, improving battery protection and extending battery service life. There is currently a lot of interest in the utilization of dual converter systems based on dual-active-bridge DC-DC systems because of their ability to support both direct current and alternating current power distribution plus their capability to isolate the battery from the electrical supply source.
The intent of this paper is to provide a detailed overview of the charging system control strategies that exist for modern EVs that utilize a dual-converter system. This review will focus on Lithium-Ion battery systems, which are most charged using the constant current/constant voltage charging profile. Traditional control techniques, which include Proportional-Integral-Derivative and Sliding-Mode Control will be discussed along with the more recently introduced intelligent control techniques, such as Fuzzy Logic, Artificial Neural Networks, and Reinforcement Learning. The various control methodologies and their comparative advantages/disadvantages will be evaluated according to several different categories, including their dynamic response capabilities, knowledge robustness, computational complexity, real-time application capability and their impact upon the overall battery performance.
According to this review classical controllers are still suitable for low-cost and real-time applications, intellectual and hybrid control strategies have greater adaptability, efficiency, loaded current stress reduction, and greater electrification and battery lifespan when considering real-world operational constraints. This review provides a structured synthesis of existing literature, including comparative summary tables, and identifies opportunities for research into data-driven and battery-aware control strategies for future EV charging systems.