Motor Motoring Texas Universal 
Teslarati
JUNE 26, 2024
A General Motors subsidiary, GM Defense, is providing commercial electric vehicle (EV) battery technology to a study by the University of Texas in Arlington (UTA) and the Naval Surface Warfare Center Philadelphia Division. General Motors has been playing electric vehicle designs for military use.
Green Car Congress
SEPTEMBER 16, 2020
in close collaboration with GTI and The University of Texas at Austin, has launched a US Department of Energy project, Demonstration and Framework for H2@Scale in Texas and Beyond. Frontier Energy, Inc., It is first time that both sources of renewable hydrogen will be used in the same project.
Green Car Congress
APRIL 23, 2022
Teams from the following universities have been selected to begin the competition this fall: Embry-Riddle Aeronautical University (Daytona Beach, FL) / Bethune-Cookman University (Daytona Beach, FL). McMaster University (Hamilton, Ontario, Canada). Mississippi State University (Starkville, MS).
Green Car Congress
OCTOBER 25, 2020
Texas A&M University researchers have developed a conceptual model to identify systematically the potential health impacts of self-driving vehicles. They identified 32 transportation-related risk factors that affected health and concluded that 17 could negatively impact public health, while eight could have a positive impact.
Electric Cars Report
JUNE 26, 2024
GM Defense, a subsidiary of General Motors, is providing commercial battery electric technology in support of the University of Texas at Arlington (UTA) Pulsed Power and Energy Laboratory (PPEL) and Naval Surface Warfare Center Philadelphia Division (NSWCPD).
Green Car Congress
DECEMBER 10, 2021
Beyond PNNL, collaborators continuing from Phase 1 are Brookhaven National Laboratory, Idaho National Laboratory, SLAC National Accelerator Laboratory, Binghamton University, the University of Texas at Austin, Stanford University, the University of California at San Diego, and the University of Washington.
Green Car Congress
MARCH 20, 2022
Researchers from Ames Laboratory and Texas A&M University have trained a machine-learning (ML) model to assess the stability of new rare-earth compounds. The framework they developed builds on current state-of-the-art methods for experimenting with compounds and understanding chemical instabilities.
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