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Oak Ridge National Laboratory researchers took a connected and automated vehicle out of the virtual proving ground and onto a public road to determine energy savings when it is operated under predictive control strategies. Credit: ORNL, U.S. Dept. of Energy

ORNL researchers  determined that a connected and automated vehicle, or CAV, traveling on a multilane highway with integrated traffic light timing control can maximize energy efficiency and achieve up to 27% savings.

Researchers have shown how an all-solid lithium-based electrolyte material can be used to develop fast charging, long-range batteries for electric vehicles that are also safer than conventional designs. Credit: ORNL, U.S. Dept. of Energy

Currently, the biggest hurdle for electric vehicles, or EVs, is the development of advanced battery technology to extend driving range, safety and reliability.

Steven Campbell’s technical expertise supports integration of power electronics innovations from ORNL labs to the electrical grid. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Steven Campbell can often be found deep among tall cases of power electronics, hunkered in his oversized blue lab coat, with 1500 volts of electricity flowing above his head. When interrupted in his laboratory at ORNL, Campbell will usually smile and duck his head.

ORNL’s additive manufacturing compression molding, or AMCM, technology can produce composite-based, lightweight finished parts for airplanes, drones or vehicles in minutes and could acclerate decarbonization for the automobile and aeropsace industries. 

An Oak Ridge National Laboratory-developed advanced manufacturing technology, AMCM, was recently licensed by Orbital Composites and enables the rapid production of composite-based components, which could accelerate the decarbonization of vehicles

ORNL’s David Sholl is director of the new DOE Energy Earthshot Non-Equilibrium Energy Transfer for Efficient Reactions center to help decarbonize the industrial chemical industry. Credit: Genevieve Martin, ORNL/U.S. Dept. of Energy

ORNL has been selected to lead an Energy Earthshot Research Center, or EERC, focused on developing chemical processes that use sustainable methods instead of burning fossil fuels to radically reduce industrial greenhouse gas emissions to stem climate change and limit the crisis of a rapidly warming planet.
 

Benefit breakdown, 3D printed vs. wood molds

Oak Ridge National Laboratory researchers have conducted a comprehensive life cycle, cost and carbon emissions analysis on 3D-printed molds for precast concrete and determined the method is economically beneficial compared to conventional wood molds.

Oak Ridge National Laboratory entrance sign

The Department of Energy’s Office of Science has selected three ORNL research teams to receive funding through DOE’s new Biopreparedness Research Virtual Environment initiative.

The DEMAND single crystal diffractometer at the High Flux Isotope Reactor, or HFIR, is the latest neutron instrument at the Department of Energy’s Oak Ridge National Laboratory to be equipped with machine learning-assisted software, called ReTIA. Credit: Jeremy Rumsey/ORNL, U.S. Dept. of Energy

Neutron experiments can take days to complete, requiring researchers to work long shifts to monitor progress and make necessary adjustments. But thanks to advances in artificial intelligence and machine learning, experiments can now be done remotely and in half the time.

ORNL Vehicle Power Electronics Research group R&D Associate Subho Mukherjee has been elevated to the senior member grade IEEE. Credit: ORNL, U.S. Dept. of Energy

Subho Mukherjee, an R&D associate in the Vehicle Power Electronics Research group at the Department of Energy’s Oak Ridge National Laboratory, has been elevated to the grade of senior member of the Institute of Electrical and Electronics Engineers.

ZEISS Head of Additive Manufacturing Technology Claus Hermannstaedter, left, and ORNL Interim Associate Laboratory Director for Energy Science and Technology Rick Raines sign a licensing agreement that allows ORNL’s machine-learning algorithm, Simurgh, to be used for rapid evaluations of 3D-printed components with industrial X-ray computed tomography, or CT. Using machine learning in CT scanning is expected to reduce the time and cost of inspections of 3D-printed parts by more than ten times.

A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory and research partner ZEISS will enable industrial X-ray computed tomography, or CT, to perform rapid evaluations of 3D-printed components using ORNL’s machine