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Argonne Impacts State by State

Argonne’s collaborations in Ohio and across the United States have led to groundbreaking discoveries and development of new technologies that help meet the nation’s needs for sustainable energy, economic prosperity, and security.

Ohio State takes honors in advanced vehicle competition managed by Argonne

The EcoCAR 3 Chevrolet Camaro. (Image by Argonne National Laboratory)

The Ohio State University (Columbus) was named the EcoCAR Mobility Challenge Year One champion in the four-year collegiate engineering competition managed by the U.S. Department of Energy’s (DOE) Argonne National Laboratory. EcoCAR, the latest DOE Advanced Vehicle Technology Competition, challenged 12 North American universities to apply advanced propulsion systems, electrification, Society of Automotive Engineers Level 2 automation and vehicle connectivity to improve the energy efficiency of a 2019 Chevrolet Blazer, while balancing emissions, safety and consumer acceptability.

Teams have four years (2018-2022) to transform their vehicles from design concept into reality, while building an energy-efficient, connected and semi-automated vehicle based on their engineered solutions. In addition to DOE, other EcoCAR sponsors included General Motors, MathWorks, NXP, National Science Foundation, Intel, American Axle & Manufacturing, Bosch, PACCAR, dSPACE, Siemens, Denso, Horiba, AVL, Delphi Technologies, California Air Resources Board, Tesa Tape, Vector, Electric Power Research Institute and Proterra.

BASF factory in Elyria to leverage Argonne battery technology

Argonne National Laboratory battery researchers (from left) Khalil Amine, Chris Johnson, Sun-Ho Kang and Mike Thackeray flank a continuously stirred tank reactor used to produce scaled-up quantities of cathode materials for lithium-ion batteries. (Image by Argonne National Laboratory)

Researchers at Argonne invented a game-changing structure for battery technology that led to multiple commercialization agreements and the building of two manufacturing plants in the Midwest, including one in Elyria, Ohio, by BASF Corp.

Argonne’s Nickel Manganese Cobalt (NMC) blended cathode structure was developed roughly 15 years ago. The development of NMC represented a major leap in lithium-ion battery technology from earlier cathode chemistries. It offers the longest lasting energy available in the smallest, lightest package. In 2009, BASF, the second largest producer of chemicals and related products in North America, licensed the NMC cathode technology and invested in further research and development as well as facilities to produce NMC-based products, which are used in electric and hybrid vehicles, personal electronics and power tools. In 2012, BASF invested $50 million to construct a 70,000-square-foot manufacturing plant in Elyria. Argonne and BASF also received a Deals of Distinction Award from the Licensing Executives Society Inc.

Ohio State, Argonne researchers earn award for paper on large-scale data visualizations

InSituNet uses deep learning to synthesize high-fidelity visualizations of science simulations, for example cosmological simulations that allow studies of the formation and evolution of galaxies. (Image by Vadim Sadovski/Shutterstock)

A team of researchers from Argonne and the Ohio State University (Columbus) won the Best Paper Award at the IEEE Scientific Visualization conference in Vancouver, Canada, in October 2019. The conference featured original research papers on scientific visualization, including theory, methods and applications ranging from mathematics and physical science to biosciences, economics and multimedia.

The paper, InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations,” presents a deep learning model for exploring parameter space for large-scale ensemble simulations in situ. The solution proposed by the Argonne-Ohio State team is a deep learning-based model, called InSituNet. Their approach works like this: Data is collected from an ensemble of simulations and visualized in situ using various visual mapping and view parameters. The model is then trained to learn the mapping from ensemble simulation parameters to visualizations of corresponding simulation outputs.