Tutorial: Machine Learning and Artificial Intelligence in Batteries
Conference
·
OSTI ID:1781618
Machine learning (ML) promises to compress the time needed to characterize battery performance, lifetime and safety. By coupling ML with physical models and metrics, that learning can bridge across materials, chemistries and cell designs. This tutorial will discuss the most popular ML techniques and resources and review recent work in the electrochemical literature. Applications include materials discovery, image recognition for quantitative microscopy analysis, fast charge algorithm development and life prediction.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1781618
- Report Number(s):
- NREL/PR-5700-78367; MainId:32284; UUID:a7961ce3-c81d-492c-a0c9-f83aea9fd309; MainAdminID:22340
- Country of Publication:
- United States
- Language:
- English
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