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Title: Multivariate prognosis of battery advanced state of health via transformers

Journal Article · · Cell Reports Physical Science
ORCiD logo [1];  [1];  [1]
  1. Argonne National Laboratory (ANL), Argonne, IL (United States)
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De-risking energy storage investments necessary to meet CO2 reduction targets requires a deep understanding of the connections between battery health, design, and use. The historical definition of the battery state of health (SOH) as the percentage of current versus initial capacity is inadequate for this purpose, motivating an expanded SOH consisting of an interrelated set of descriptors including capacity, energy, ionic and electronic impedances, open-circuit voltages, and microstructure metrics. In this work, we introduce deep transformer networks for the simultaneous prognosis of 28 battery SOH descriptors using two cycling datasets representing six lithium-ion cathode chemistries, multiple electrolyte/anode compositions, and different charge-discharge scenarios. The accuracy of these predictions for battery life (with an unprecedented mean absolute error of 19 cycles in predicting end of life for a lithium-iron-phosphate fast-charging dataset) illustrates the promise of deep learning toward providing enhanced understanding and control of battery health.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Electricity (OE); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
2475469
Alternate ID(s):
OSTI ID: 2338213
Journal Information:
Cell Reports Physical Science, Journal Name: Cell Reports Physical Science Journal Issue: 5 Vol. 5; ISSN 2666-3864
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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25 ENERGY STORAGE
lithium ion
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degradation prediction
state of health
deep learning
transformer model