Ultra-fast electric vehicle (EV) charging systems are among the most demanding converter-dominated applications due to their high power levels, wide battery-voltage range, strict thermal constraints, and the need for adaptive charging control. Conventional design and tuning approaches often rely on fixed control policies and computationally expensive iterative optimization, which limits their ability to address nonlinear multi-objective trade-offs across the full charging envelope. This paper proposes a hybrid AI–quantum co-design framework for a SiC-based dual active bridge (DAB) converter intended for ultra-fast EV charging applications. The proposed approach combines a physical converter model, an AI surrogate-learning layer for rapid prediction of converter performance, and a quantum-assisted optimization layer for multi-objective exploration of design and control variables. To demonstrate the framework, a representative modular 350 kW ultra-fast charging case study is considered, implemented by four parallel 87.5 kW SiC-based DAB modules and including converter-level optimization and adaptive charging-policy refinement. The revised manuscript introduces a complete system schematic, an explicit DAB converter topology, a clarified methodological workflow, and a simulation-based proof-of-concept evaluation. Representative results indicate improved design-space exploration and more balanced trade-offs between efficiency, thermal stress, ripple, and dynamic response compared with a conventional baseline tuning approach. Although the study does not claim hardware-level quantum advantage, it provides a structured and practically interpretable computational framework for intelligent co-design of high-power charging converters.
Nikolay Hinov (2026) studied this question.