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Researcher
- Vivek Sujan
- Peeyush Nandwana
- Omer Onar
- Adam Siekmann
- Amit Shyam
- Blane Fillingim
- Brian Post
- Erdem Asa
- Lauren Heinrich
- Rangasayee Kannan
- Subho Mukherjee
- Sudarsanam Babu
- Thomas Feldhausen
- Yousub Lee
- Alex Plotkowski
- Andres Marquez Rossy
- Bruce A Pint
- Bryan Lim
- Christopher Fancher
- Gordon Robertson
- Hong Wang
- Hyeonsup Lim
- Isabelle Snyder
- Jay Reynolds
- Jeff Brookins
- Peter Wang
- Ryan Dehoff
- Shajjad Chowdhury
- Steven J Zinkle
- Tim Graening Seibert
- Tomas Grejtak
- Weicheng Zhong
- Wei Tang
- Xiang Chen
- Yanli Wang
- Ying Yang
- Yiyu Wang
- Yutai Kato

The growing demand for electric vehicles (EVs) has necessitated significant advancements in EV charging technologies to ensure efficient and reliable operation.

The growing demand for renewable energy sources has propelled the development of advanced power conversion systems, particularly in applications involving fuel cells.

The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.

This invention presents a multiport converter (MPC) based power supply to charge the 12 V and 24 V auxiliary batteries in heavy duty (HD) fuel cell (FC) electric vehicle (EV) power train.

This invention presents an integrated strategy to reduce end-user electricity costs and grid carbon emissions by efficiently utilizing Distributed Energy Resources (DER) and grid-scale electrical energy storage systems, such as batteries.

This work seeks to alter the interface condition through thermal history modification, deposition energy density, and interface surface preparation to prevent interface cracking.

Additive manufacturing (AM) enables the incremental buildup of monolithic components with a variety of materials, and material deposition locations.

No readily available public data exists for vehicle class and weight information that covers the entire U.S. highway network. The Travel Monitoring Analysis System, managed by the Federal Highway Administration covers only less than 1% of the US highway network.