Knee Point

Battery Capacity versus number of cycles showing the Knee Point

The knee point describes a sudden change in the gradient of a cell cycling curve.

Also called [1]:

  • rollover failure
  • nonlinear aging
  • sudden death
  • saturation
  • second-stage degradation
  • two-phase degradation
  • capacity plunge
  • drop-off

Attia et al [1] also describe six mechanisms/pathways that can produce the “Knee Point”:

  1. Lithium plating – metallic lithium deposits on the surface of the negative electrode particles.
  2. Electrode saturation – the number of active sites in the electrode has decreased and can no longer accommodate the incoming lithium inventory.
  3. Resistance growth – high overpotentials lead to a rapid drop in available capacity.
  4. Electrolyte depletion – the local depletion of electrolyte leads to loss of active material, and additive depletion, in which the depletion of a critical electrolyte additive triggers a knee.
  5. Percolation-limited connectivity – a small change in ionic or electronic electrode connectivity leads to a large change in electrode active material.
  6. Mechanical deformation – microscale, mesoscale, or macroscale mechanical effects trigger an increasing rate of active material loss.

The impact of this nonlinear degradation in cell capacity depends to some extent on the architecture and application, but includes:

  • capacity fade
  • discharge rate reduction
  • charging curtailed
  • continuous balancing
  • increased heat
  • thermal runaway

This post has been built based on the support and sponsorship from: Eatron TechnologiesAbout:EnergyAVANT Future MobilityQuarto Technical ServicesTAE Power Solutions and The Limiting Factor. 

Based on this there are a lot of techniques being developed to try and predict the knee point early [2, 3]. An early indication of cell failure could allow the battery management system to take corrective action.

Also, it is important to be able to establish the knee point in cell testing [4]. This would allow the design to be adapted or for a change in cell selection or for a managed lifetime.

The knee point has been seen as a “cliff edge” unpredictable end to the life of a battery. The resultant failure mechanisms in these situations has pushed the management systems to shut down the operation of the battery pack. However, with cycling data, adaptive control and pack management it is possible to both manage the safety more robustly and to extend the lifetime of the battery pack.

References

  1. Peter M. Attia, Alexander Bills, Ferran Brosa Planella, Philipp Dechent, Gonçalo dos Reis, Matthieu Dubarry, Paul Gasper, Richard Gilchrist, Samuel Greenbank, David Howey, Ouyang Liu, Edwin Khoo, Yuliya Preger, Abhishek Soni, Shashank Sripad, Anna G. Stefanopoulou, and Valentin Sulzer, Review—“Knees” in Lithium-Ion Battery Aging Trajectories, Journal of The Electrochemical Society, 2022 169 060517
  2. Y. Ke, Y. Jiang, R. Zhu, W. Peng and X. Tan, “Early prediction of knee point and knee capacity for fast-charging Lithium-ion battery with uncertainty quantification and calibration,” in IEEE Transactions on Transportation Electrification
  3. Paula Fermín-Cueto, Euan McTurk, Michael Allerhand, Encarni Medina-Lopez, Miguel F. Anjos, Joel Sylvester, Gonçalo dos Reis, Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells, Energy and AI, Volume 1, 2020
  4. Diao, W.; Saxena, S.; Han, B.; Pecht, M. Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion CellsEnergies 201912, 2910

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