Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1885389

Magnetic Shield Design for the Double-D Coil-Based Wireless Charging System

Mohammad, Mostak·Onar, Omer C.·Galigekere, Veda Prakash·Su, Gui-Jia·Wilkins, Jonathan
View original source ↗Read source document ↗Export metadata ↓

Abstract

In this work, magnetic field emission (MFE) of the high-power double-D (DD) coil-based wireless charging system is investigated and a shielding technique is proposed. The MFE pattern produced by the DD coils is significantly different from the MFE of the unipolar (e.g., circular, square, or rectangular) coils, and the traditional aluminum shield does not suppress the MFE from the DD coils. This study shows that a conventional aluminum shield increases the MFE, and a magnetic shield effectively suppresses the MFE of the DD coils. Therefore, a magnetic shield consisting of high-permeability magnetic material, such as ferrites, nanocrystalline, etc., is proposed for the DD pads. The shielding effectiveness of the proposed shield is evaluated through finite element analysis and verified through experiments. An 11 kW DD coil-based wireless charging system was used to test the proposed shielding technique. The experimental results show that a traditional aluminum shield increased the MFE by 29.8%, and the proposed magnetic shield suppressed the MFE by 50.5%.

Explore related subjects

42 ENGINEERINGaluminumelectric vehicleelectromagnetic field (EMF) emissionsferritesinductive chargingleakage fieldmagnetic noisemagnetic shieldingoptical wavelength conversionreceiversshielding effectiveness (SE)transmitterswireless power transfer

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mohammad, Mostak, Onar, Omer C., Galigekere, Veda Prakash, Su, Gui-Jia, Wilkins, Jonathan. 2022-07-19. Magnetic Shield Design for the Double-D Coil-Based Wireless Charging System. https://doi.org/10.1109/tpel.2022.3191911

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↗