Search NASA⌕ Search

DOE OSTI · 1974605

Improved Double Pulse Test for Accurate Dynamic Characterization of Medium Voltage SiC Devices

Abstract

This article presents an improved double pulse test (DPT) for accurate dynamic characterization of the medium voltage (MV) silicon carbide device. The difference between low voltage (LV) and MV DPT setup grounding is first introduced, which results in different measurement considerations. Then, parasitic capacitances in the DPT and their impact on the DPT are discussed considering different grounding points and device connections. Approaches are proposed to minimize the impact of parasitic capacitances on DPT results. In addition, the impact of switching V-I timing alignment on the testing results is discussed, compared to that in the LV DPT; a V-I alignment approach is introduced for the MV DPT. A 10 kV/20 A SiC mosfet -based DPT is taken as an example of the improved DPT, and test results show that it can minimize the impact from parasitic and improve the accuracy of the device switching loss estimation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Li, Haiguo, Gao, Zihan, Chen, Ruirui, Wang, Fei. 2022-09-29. Improved Double Pulse Test for Accurate Dynamic Characterization of Medium Voltage SiC Devices. https://doi.org/10.1109/tpel.2022.3210749

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗