Grant: Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
The Governing Council of the University of Toronto
$496,650.00
Artificial intelligence for battery material property-performance predictions and battery remaining useful lifetime
EF 2023-24 · 2024-03-26
Résumé
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.
Ministère
Programme
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Emplacement
Toronto|Toronto, Ontario, Canada
Type de bénéficiaire
Academia
Numéro d'entreprise
108162330
Type
Grant
Source
Proactive Disclosure - Grants and Contributions
Tel que publié
Agreement End Date
2027-03-31
Agreement Number
1015247
Agreement Start Date
2024-03-26
Agreement Title
Artificial intelligence for battery material property-performance predictions and battery remaining useful lifetime
Agreement Title (French)
Intelligence artificielle pour la prévision des propriétés et de la performance des matériaux de batterie et de la durée de vie utile restante de la batterie
Agreement Type
Grant
Agreement Value
$496,650.00
Amendment Number
0
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.
Description (French)
Intelligence artificielle pour la prévision des propriétés et de la performance des matériaux de batterie et de la durée de vie utile restante de la batterie
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.
Expected Results (French)
À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides.
Federal Riding
University–Rosedale
Federal Riding (French)
University–Rosedale
Federal Riding Number
35110
NAICS Code
541710 - R&D in the physical, engineering and life sciences
Organization Code
nrc-cnrc
Organization
National Research Council Canada | Conseil national de recherches Canada
Program Name
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Program Name (French)
Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D
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.
Program Purpose (French)
Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires.
Recipient Business Number
108162330
Recipient City
Toronto|Toronto
Recipient Country
CA
Recipient Legal Name
The Governing Council of the University of Toronto
Recipient Postal Code
M5G 1L5
Recipient Province or Territory
ON
Recipient Type
S
Reference Number
172-2023-2024-Q4-1015247
