Abstract

continuously growing penetration of electric (EV) and hybrid electric (HEV) vehicles, reinforced by the need for increased energy savings, drove the research towards power electronic components with superior material properties. Wide-bandgap (WBG) power semiconductor materials such as Silicon Carbide (SiC) offer significant improvements in power converter’s performance as they allow higher operating voltage, higher switching frequency and higher maximum operating temperature. As a consequence, it is expected to be utilized in traction DC-DC converters where high efficiency, high power density and lower cooling requirements are desired while, simultaneously, achieving significa nt reductions in the total weight and volume of the powertrain. The improved features introduced by SiC MOSFETs on DC-DC converter topologies employed in EVs and HEVs are investigated and experimentally verified. Initially, a non-isolated half-bridge DC-DC converter topology is designed with operating principles that correspond to a converter found on energy management systems of HEVs or fuel cell vehicles. The operating principle of the converter is presented and the bi-directional flow of energy aspect is explained. Subsequently, the design has been modelled and evaluated on a simulation software that considered the operating stages of the converter by also accounting for the gate driving circuit and the predefined cooling requirements. Finally, a test prototype was built in custom-made printed circuit board (PCB) and validated in terms of reliable operation and circuit efficiency. The benefits from utilizing SiC power semiconductors are presented under constant 24 V loading conditions and 80 kHz switching frequency. A constant voltage was maintained through automatically modulating the gate driving signal, whereas an efficiency of around 96% was achieved due to fast switching times introduced by SiC MOSFETs and thus the reduced switching losses. The efficie ncy of the system is dependable on the MOSFET operating performance and therefore an exposure of the transistors in environmental conditions was considered for each individual SiC MOSFET provided by different suppliers. Significant attention has been paid to rapid temperature change, extreme humidity, mechanical vibration and accelerated ageing tests that aim to address possible degradation and defects in the SiC material. The effect of the testing process was examined in component-level, by assessing the electrical characteristics of the SiC MOSFETs, as well as in converter-level by evaluating potential efficiency reductions and operating flaws. Finally, verification of the conditions under which each individual change occurred is of equal importance for prospective improvements in the transistor and converter alike.

Abstract

As machine learning potentials for molecular dynamics (MD) simulations, Spectral Neighbor Analysis Potential (SNAP) and quadratic SNAP (qSNAP) were constructed for silicon (Si) and silicon carbide (SiC). The reproducibility of the basic material properties about perfect crystal, free surface and dislocation cores in Si and 3C-SiC was investigated. The coefficients of SNAP and qSNAP were optimized using liner regression to present energy and force obtained by DFT. In addition, hyperparameters (cutoff length and weights for optimization, here) were determined using genetic algorithm to reproduce elastic moduli obtained by DFT. Lattice constant and elastic moduli of Si crystal by MD using our SNAP or qSNAP agree well with the values of DFT, and they have higher accuracy than those by any empirical potential. Additionally, melting point and specific heat at constant pressure were calculated by MD correctly. Especially in qSNAP of Si, the surface energy of {100} and {111} planes and the reconstructed {100} surface structure were almost reproduced. For 3C-SiC, SNAP reproduces lattice constant and elastic moduli of DFT. Furthermore, edge dislocation cores were generated successfully. However, the potentials we constructed have insufficient reproducibility in the plastic region, so it is necessary to continue development.