Model-free reconstruction of capacity degradation trajectory of lithium-ion batteries using early cycle data

Early degradation prediction of lithium-ion batteries is important to guarantee safe operations and avoid unexpected failure in manufacturing and diagnosis processes. However, long-term capacity trajectory prediction involves several extrapolation predictions, which can result in failure due to cumulative errors and noises. To reduce the efforts for qualification testing of batteries in the industry, this study proposes a data-centric method to predict the capacity degradation trajectory of lithium-ion cells using early single-cycle data. A few knots at specific retention levels are predicted using a deep learning-based model and interpolated to reconstruct the trajectory. The retention levels of two to four knots are identified using two approaches: uniformly divide the retention up to the end of life and find the optimal locations using Bayesian optimization. The proposed model is validated using the experimental data of 169 cells with five-fold cross-validation. The simulation results show that the mean absolute percentage errors in trajectory prediction are less than 1.60% for all cases of knots. The proposed model can directly estimate the overall capacity degradation trajectory by predicting only the cycle numbers of at least two knots based on the early single-cycle charge and discharge data. Further experiments on the impact of the number of input cycles suggest the use of three-cycle input data to obtain robust and efficient predictions even when there is noise in the input data. Finally, the proposed method is applied to predict various shapes of capacity degradation patterns of additional experimental data of 82 cells. The authors demonstrate that regardless of the shapes of the trajectories, it is sufficient to collect only the cycle information of a few knots while training the prediction model and a few early cycle information for predicting the capacity degradation, which can help establish appropriate warranties or replacement cycles in the battery manufacturing and diagnosis processes.

• Record URL:
  https://doi.org/10.1016/j.etran.2023.100243
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2590116823000188
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/25901168
• Supplemental Notes:
  • © 2023 Elsevier B.V. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Kim, Seongyoon
  • Jung, Hangsoon
  • Lee, Minho
  • Choi, Yun Young
  • Choi, Jung-Il
  • 0000-0002-4948-1250
• Publication Date: 2023-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 100243
• Serial:
  • eTransportation
  • Volume: 17
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2590-1168
  • Serial URL: https://www.sciencedirect.com/journal/etransportation

Subject/Index Terms

• TRT Terms: Degradation (Thermodynamics); Electric vehicles; Lithium batteries; Predictive models; Trajectory
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01893514
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 19 2023 9:27AM