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Researcher
- Omer Onar
- Subho Mukherjee
- Vivek Sujan
- Mostak Mohammad
- Vandana Rallabandi
- Erdem Asa
- Shajjad Chowdhury
- Adam Siekmann
- Burak Ozpineci
- Emrullah Aydin
- Jon Wilkins
- Gui-Jia Su
- Isabelle Snyder
- Veda Prakash Galigekere
- Ali Riza Ekti
- Andrew F May
- Annetta Burger
- Ben Garrison
- Brad Johnson
- Carter Christopher
- Chance C Brown
- Charlie Cook
- Christopher Hershey
- Craig Blue
- Daniel Rasmussen
- Debraj De
- Gautam Malviya Thakur
- Hong Wang
- Hsin Wang
- Hyeonsup Lim
- James Gaboardi
- James Klett
- Jesse McGaha
- John Lindahl
- Kevin Sparks
- Lingxiao Xue
- Liz McBride
- Mike Zach
- Nedim Cinbiz
- Nishanth Gadiyar
- Rafal Wojda
- Todd Thomas
- Tony Beard
- Xiuling Nie

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.

This disclosure introduces an innovative tool that capitalizes on historical data concerning the carbon intensity of the grid, distinct to each electric zone.

This disclosure introduces an innovative tool that capitalizes on historical data concerning the carbon intensity of the grid, distinct to each electric zone.

Technologies directed to an integrated on-board charger for dual motor based electric vehicle power train are described.
Contact:
To learn more about this technology, email partnerships@ornl.gov or call 865-574-1051.

ORNL will develop an advanced high-performing RTG using a novel radioisotope heat source.

This invention proposes a Honeycomb-DD coupling structure that addresses the shortcomings of the conventional honeycomb coil array and gathering the advantage of DD and honeycomb designs advantages in a single design.

Wireless charging systems need to operate at high frequency, at or near resonance, to maximize power transfer distance and efficiency. High voltages appear across the inductors and capacitors. The use of discrete components reduces efficiency, increases system complexity.

Pairing hybrid neural network modeling techniques with artificial intelligence, or AI, controls has resulted in a unique hybrid system that creates a smart solution for traffic-signal timing.