A team of researchers at ORNL demonstrated that a light-duty passenger electric vehicle can be wirelessly charged at 100-kW with 96% efficiency using polyphase electromagnetic coupling coils with rotating magnetic fields.
Electric vehicles can drive longer distances if their lithium-ion batteries deliver more energy in a lighter package. A prime weight-loss candidate is the current collector, a component that often adds 10% to the weight of a battery cell without contributing energy.
ORNL researchers created and tested new wireless charging designs that may double the power density, resulting in a lighter weight system compared with existing technologies.
Researchers at Oak Ridge National Laboratory proved that a certain class of ionic liquids, when mixed with commercially available oils, can make gears run more efficiently with less noise and better durability.
A team of researchers at Oak Ridge National Laboratory have demonstrated that designed synthetic polymers can serve as a high-performance binding material for next-generation lithium-ion batteries.
Using Summit, the world’s most powerful supercomputer housed at Oak Ridge National Laboratory, a team led by Argonne National Laboratory ran three of the largest cosmological simulations known to date.
In a step toward advancing small modular nuclear reactor designs, scientists at Oak Ridge National Laboratory have run reactor simulations on ORNL supercomputer Summit with greater-than-expected computational efficiency.
Oak Ridge National Laboratory’s latest Transportation Energy Data Book: Edition 37 reports that the number of vehicles nationwide is growing faster than the population, with sales more than 17 million since 2015, and the average household vehicle travels more than 11,000 miles per year.
Oak Ridge National Laboratory scientists have created open source software that scales up analysis of motor designs to run on the fastest computers available, including those accessible to outside users at the Oak Ridge Leadership Computing Facility.
Oak Ridge National Laboratory geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.