Degradation and Modeling of Large-Format Commercial Lithium-Ion Cells as a Function of Chemistry, Design, and Aging Conditions

Paul Gasper, Aron Saxon, Ying Shi, Elizabeth Endler, Kandler Smith, Foram Thakkar

Research output: Contribution to journalArticlepeer-review

2 Scopus Citations

Abstract

Demand for large-format (>10 Ah) lithium-ion batteries has increased substantially in recent years, due to the growth of both electric vehicle and stationary energy storage markets. The economics of these applications is sensitive to the lifetime of the batteries, and end-of-life can either be due to energy or power limitations. Despite this, there is little information from cell manufacturers on the sensitivity of cell degradation to environmental conditions or battery use. This work reports accelerated aging test data from four commercial large-format lithium-ion batteries from three manufacturers, with varying design (thickness, casings, …), chemistry (lithium‑iron-phosphate (LFP) or lithium‑nickel‑manganese‑cobalt-oxide positive electrodes (NMC), with graphite (Gr) negative electrodes), and capacity (50 to 250 Amp∙hours). The tested LFP|Gr cell is found to be relatively insensitive to cycling conditions like temperature or voltage window, while NMC|Gr cells have varying sensitivity. Degradation trends are further investigated by training predictive models: simple polynomial trend lines, a semi-empirical reduced-order model, and an empirical reduced-order model identified using machine-learning based on symbolic regression. Calendar and cycle life are simulated over a variety of conditions to directly compare the various batteries. Cell size and thickness are found to substantially impact sensitivity to temperature during cycle aging, while electrode chemistry impacts depth-of-discharge sensitivity. Real-world battery lifetime is evaluated by simulating residential energy storage and commercial frequency containment reserve systems in several U.S. climate regions. Predicted lifetime across cell types varies from 7 years to 20+ years, though all cells are predicted to have at least 10 year life in certain conditions.

Original languageAmerican English
Article number109042
Number of pages20
JournalJournal of Energy Storage
Volume73
Issue numberPart C
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5700-83317

Keywords

  • Calendar life
  • Cycle life
  • Degradation
  • Lithium-ion battery
  • Machine-learning
  • Stationary energy storage

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