ML-ACCEPT: Machine-Learning-enhanced Automated Circuit Configuration and Evaluation of Power Converters

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Program:
DIFFERENTIATE
Award:
$1,923,957
Location:
Dearborn, Michigan
Status:
ALUMNI
Project Term:
06/17/2020 - 12/31/2022
Website:

Technology Description:

The University of Michigan-Dearborn will develop a machine learning-enhanced design tool for the automated architectural configuration and performance evaluation of electrical power converters. This tool will help engineers consider a wider range of innovative concepts when developing new converters than would be possible via traditional approaches. This tool is expected to leverage a number of ML techniques—including decision trees, supervised learning and reinforcement learning—and is expected to reduce the cost and time required to develop new ultra-efficient power-converter designs.

Potential Impact:

DIFFERENTIATE aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. If successful, DIFFERENTIATE will yield the following benefits in ARPA-E mission areas:

Security:

Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.

Environment:

Use these tools to solve our most challenging energy and environmental problems by facilitating an economically-attractive transition to lower carbon-footprint energy sources and systems.

Economy:

Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.

Contact

ARPA-E Program Director:
Dr. David Tew
Project Contact:
Dr. Yi Murphey
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.gov
Project Contact Email:
yilu@umich.edu

Partners

Oak Ridge National Laboratory
Lawrence Livermore National Laboratory

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Release Date:
04/05/2019