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Accelerated battery life predictions through synergistic combination of physics-based models and machine learning

Journal Article · · Cell Reports Physical Science

Not Available

Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Grant/Contract Number:
AC07-05ID14517
OSTI ID:
1889500
Alternate ID(s):
OSTI ID: 1924794
Journal Information:
Cell Reports Physical Science, Journal Name: Cell Reports Physical Science Journal Issue: 9 Vol. 3; ISSN 2666-3864
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Nano Li4Ti5O12–LiMn2O4 batteries with high power capability and improved cycle-life journal January 2009
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Li plating as unwanted side reaction in commercial Li-ion cells – A review journal April 2018
Impacts of lean electrolyte on cycle life for rechargeable Li metal batteries journal December 2018
Model-based lithium deposition detection method using differential voltage analysis journal November 2021
Heterogeneous Behavior of Lithium Plating during Extreme Fast Charging journal July 2020
A machine learning framework for early detection of lithium plating combining multiple physics-based electrochemical signatures journal March 2021
Fast-Charging Aging Considerations: Incorporation and Alignment of Cell Design and Material Degradation Pathways journal September 2021
Random Forests journal January 2001
Deep learning journal May 2015
Data-driven prediction of battery cycle life before capacity degradation journal March 2019
Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python journal February 2020
Model-Instructed Design of Novel Charging Protocols for the Extreme Fast Charging of Lithium-Ion Batteries Without Lithium Plating journal May 2020
Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning journal February 2021

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