Artificial Intelligence-Assisted Multi-Objective Design and Dynamic Validation of High-Efficiency Solid-State Transformers for Electric Vehicle Integration

Integrating electric vehicles (EVs) into power grids requires efficient, compact high-frequency transformers (HFTs) for solid-state-transformers (SST) and charging stages. This paper combines a physics-informed multi-objective genetic algorithm (NSGA-II, an evolutionary AI method), two-dimensional finite-element (FE) evaluation and dual-active-bridge (DAB) simulation to size HFTs at 85 kHz for ferrite 3C94 (Fe), Metglas 2605SA1 (Me) and Vitroperm 500F (Vi). In a first, FEMM-based stage (10 kW), analytical power density differed from FEMM by 73.7% (Fe), 22.7% (Vi) and 11.4% (Me); Vi reached 11.84 W/cm3 at a simulated peak transformer-only efficiency of 99.46%, and Litz optimisation identified AWG 44–46 and AWG 38–42 operating points. A second, independent stage (22 kW, five seeds per material) shows that the FE model raises the total loss by 3–11%, lowers the leakage inductance by about 48% and reveals hotspots about four times the lumped rise, excluding 31–54% of the designs at a 100 K limit. Fe and Vi then reach 6–37 and 7–27 W/cm3 at 99.8–99.9% efficiency and Me 5–9 W/cm3, and a designed DAB current loop is well damped (55° phase margin). The ranking is sensitive to loss fits and cooling prediction.