Comparative Analysis of Physics and Finite Element Method Based Multi-objective Optimization of High-Frequency Transformer For Electric Vehicle

As the growth of Electric vehicles (EV) continues, researchers are constantly conducting research on methods to increase the range of EVs. The emergence of Dynamic Wireless Power Transfer (DWPT) charging for Electric Vehicle Batteries (EVBs) has prompted the design of an On-board Charger, which utilizes a solid-state transformer (SST) instead of a low-frequency service transformer. The weight of the solid-state transformer (SST) is a significant factor that affects the range of the EV, thus highlighting the need to optimize the High-Frequency Transformers (HFTs) in the SST to increase efficiency and reduce the volume and weight of the EV electrical architecture. This article presents a comparative analysis of two techniques for generating the fitness function for the optimization of HFTs, including: i) Physics and ii) Finite Element Method Magnetics (FEMM) Based optimization strategies. The fitness functions (parameters) obtained from either of the strategies are then used as input to the multi-objective Genetic Algorithm (GA) design. The results of the GA from these two approaches are carefully analyzed, as each core material demonstrates different properties in terms of power loss, power density, and overall cost based on a collection of multiple Pareto-Optimal Solutions (POS). This study's comparative analysis of physics and FEMM-based optimization strategies provides insights into the optimization of HFTs in the SST of EVs, which can lead to more efficient and cost-effective solutions for the electric vehicle industry.