ML-ACCEPT: Machine-Learning-enhanced Automated Circuit Configuration and Evaluation of Power Converters
Project Innovation + Advantages:
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.
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:
Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.
Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.