Grants and Contributions:
Title:
Artificial intelligence for battery material property-performance predictions and battery remaining useful lifetime
Agreement Number:
1015247
Agreement Value:
$496,650.00
Agreement Date:
Mar 26, 2024 - Mar 31, 2027
Description:
This project will develop a physics informed machine learning approach to predicting battery durability to be implemented into a self-driving laboratory being developed by the NRC. It leverages University of Toronto AI-assisted tools for fitting electrochemical impedance spectroscopy (EIS), a central tool in measuring electrochemical systems, and generating statistically significant and unbiased models of physical processes. The Recipient will refine and apply this tool to support the NRC’s development of novel battery cathodes by developing (1) an automated sensitivity analysis and out of distribution detection algorithm to enable model updating during active learning studies, (2) a robust modeling framework for generating physical insights into battery performance and degradation, which will permit scientifically informed adjustments to battery formulations, and (3) an active learning tool that combines these tools to predict battery longevity without long term cycling studies. All data and code generated will be released publicly to benefit all Canadians.
Organization:
National Research Council Canada
Expected Results:
In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.
Location:
Toronto, Ontario, CA M5G 1L5
Reference Number:
172-2023-2024-Q4-1015247
Agreement Type:
Grant
Report Type:
Grants and Contributions
Recipient Business Number:
108162330
Recipient Type:
Academia
Recipient's Legal Name:
The Governing Council of the University of Toronto
Federal Riding Name:
University–Rosedale
Federal Riding Number:
35110
Program:
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Program Purpose:
Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.
NAICS Code:
541710 - R&D in the physical, engineering and life sciences