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Battery Capacity Knee-Onset Identification and Early Prediction Using Degradation Curvature

Huang Zhang, Faisal Altaf, Torsten Wik

TL;DR

This work introduces a curvature-based method to identify capacity knees and knee onset in lithium-ion batteries, formulating knee detection as an unsupervised three-state segmentation problem on degradation curvature. It leverages Savitzky–Golay smoothing, discrete curvature approximation, matrix-profile–based CAC analysis, and the Regime Extracting Algorithm to automatically locate knee-onset and knee points, without requiring a full fade curve. The method is validated on TRI LFP and SNL NMC datasets and a synthetic DFN-based dataset, consistently outperforming the state-of-the-art Double Bacon-Watts approach and showing strong correlations with end-of-life, enabling online knee-onset prediction. The knee-onset predictor can substantially reduce experimental time and costs (e.g., hundreds of cycles), facilitating online monitoring, battery grading, replacement planning, and second-life decision-making in industrial settings.

Abstract

Abrupt capacity fade can have a significant impact on performance and safety in battery applications. To address concerns arising from possible knee occurrence, this work aims for a better understanding of their cause by introducing a new definition of capacity knees and their onset. A curvature-based identification of a knee and its onset is proposed, which relies on the discovery of a distinctly fluctuating behavior in the transition between an initial and a final stable acceleration of the degradation. The method is validated on experimental degradation data of two different battery chemistries, synthetic degradation data, and is also benchmarked to the state-of-the-art knee identification method in the literature. The results demonstrate that our proposed method could successfully identify capacity knees when the state-of-the-art knee identification method failed. Furthermore, a significantly strong correlation is found between knee and end of life (EoL) and almost equally strong between knee onset and EoL. As the method does not require the full capacity fade curve, this opens up online knee-onset identification as well as knee and EoL prediction.

Battery Capacity Knee-Onset Identification and Early Prediction Using Degradation Curvature

TL;DR

This work introduces a curvature-based method to identify capacity knees and knee onset in lithium-ion batteries, formulating knee detection as an unsupervised three-state segmentation problem on degradation curvature. It leverages Savitzky–Golay smoothing, discrete curvature approximation, matrix-profile–based CAC analysis, and the Regime Extracting Algorithm to automatically locate knee-onset and knee points, without requiring a full fade curve. The method is validated on TRI LFP and SNL NMC datasets and a synthetic DFN-based dataset, consistently outperforming the state-of-the-art Double Bacon-Watts approach and showing strong correlations with end-of-life, enabling online knee-onset prediction. The knee-onset predictor can substantially reduce experimental time and costs (e.g., hundreds of cycles), facilitating online monitoring, battery grading, replacement planning, and second-life decision-making in industrial settings.

Abstract

Abrupt capacity fade can have a significant impact on performance and safety in battery applications. To address concerns arising from possible knee occurrence, this work aims for a better understanding of their cause by introducing a new definition of capacity knees and their onset. A curvature-based identification of a knee and its onset is proposed, which relies on the discovery of a distinctly fluctuating behavior in the transition between an initial and a final stable acceleration of the degradation. The method is validated on experimental degradation data of two different battery chemistries, synthetic degradation data, and is also benchmarked to the state-of-the-art knee identification method in the literature. The results demonstrate that our proposed method could successfully identify capacity knees when the state-of-the-art knee identification method failed. Furthermore, a significantly strong correlation is found between knee and end of life (EoL) and almost equally strong between knee onset and EoL. As the method does not require the full capacity fade curve, this opens up online knee-onset identification as well as knee and EoL prediction.
Paper Structure (34 sections, 2 equations, 9 figures, 5 tables, 2 algorithms)

This paper contains 34 sections, 2 equations, 9 figures, 5 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Double Bacon-Watts model
  4. Matrix profile
  5. Scalable time series anytime matrix profile
  6. Arc curve
  7. Fast low-cost unipotent semantic segmentation
  8. Regime extracting algorithm
  9. Methods
  10. Degradation curvature approximation
  11. Knee and knee-onset identification
  12. Experimental design
  13. Toyota research institute dataset
  14. Dataset description
  15. Lithium plating-induced knees
  16. ...and 19 more sections

Figures (9)

  • Figure 1: The curvature approximation results of a sample cell [b1c0] in the Toyota Research Institute dataset severson2019data.
  • Figure 2: The knee-onset and knee identification results of the sample cell [b1c0].
  • Figure 3: The relationship between knee-onset and end of life (left), knee and end of life (right) for 169 cells in the Toyota Research Institute dataset.
  • Figure 4: The relationship between knee-onset and end of life (left), and knee and end of life (right) for 22 cells in the Sandia National Lab dataset.
  • Figure 5: The knee-onset and knee identification results of a sample cell [No.10] in the Sandia National Lab dataset.
  • ...and 4 more figures

Theorems & Definitions (9)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5
  • Definition 6
  • Definition 7
  • Definition 8
  • Definition 9