Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2324744

Power Conversion Systems Enabled by SiC BiDFET Device

Bhattacharya, Subhashish·Narwal, Ramandeep·Shah, Suyash Sushilkumar·Baliga, B. Jayant·Agarwal, Aditi·Kanale, Ajit·Han, Kijeong·Hopkins, Douglas C.·Cheng, Tzu-Hsuan
View original source ↗Read source document ↗Export metadata ↓

Abstract

The BiDirectional Field-Effect Transistor (BiDFET) can enable circuit topologies requiring four quadrant switches, that were earlier designed using discrete combinations of MOSFETs, IGBTs, GaN HEMTs and PiN diodes. The monolithic nature of the BiDFET allows lower device count, smaller switch volume, lower inductance, and simpler packaging, and hence more reliable and commercially viable implementation in power electronics converters. Furthermore, the matrix converter topologies, now feasible using BiDFETs, can eliminate the bulky and unreliable dc link capacitors or inductors required for conventional voltage-source or current-source converters in ac-ac and ac dc applications. The 1.2 kV BiDFET has the potential to disrupt all the applications utilizing 1.2 kV switches, including electric vehicle (EV) drivetrain, bidirectional EV chargers, industrial motor drives, solid-state transformers, datacenter power supplies, elevator drives, dc microgrids, energy storage grid integration, solid-state breakers, etc.

Explore related subjects

42 ENGINEERINGMotor drivesPIN photodiodesPackagingPower suppliesSilicon carbideSwitchesTransformers

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bhattacharya, Subhashish, Narwal, Ramandeep, Shah, Suyash Sushilkumar, Baliga, B. Jayant, Agarwal, Aditi, Kanale, Ajit, Han, Kijeong, Hopkins, Douglas C., Cheng, Tzu-Hsuan. 2023-03-06. Power Conversion Systems Enabled by SiC BiDFET Device. https://doi.org/10.1109/mpel.2023.3237060

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

KEEP EXPLORING

Related reports

DOE OSTI2026

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↗