Journal of Hydrogen and New Energy (한국수소및신에너지학회논문집)

The Korean Hydrogen and New Energy Society (한국수소및신에너지학회)
권호 표지
DOI QR코드

DOI QR Code

Application of Regularized Linear Regression Models Using Public Domain data for Cycle Life Prediction of Commercial Lithium-Ion Batteries

상업용 리튬 배터리의 수명 예측을 위한 고속대량충방전 데이터 정규화 선형회귀모델의 적용

  • KIM, JANG-GOON (School of Materials Science and Engineering, Chonnam National University) ;
  • LEE, JONG-SOOK (School of Materials Science and Engineering, Chonnam National University)
  • 김장군 (전남대학교 신소재공학부) ;
  • 이종숙 (전남대학교 신소재공학부)
  • Received : 2021.10.14
  • Accepted : 2021.12.14
  • Published : 2021.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this study a rarely available high-throughput cycling data set of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles including in-cycle temperature and per-cycle IR measurements. We worked out own Python codes which reproduced the various data plots and machine learning approaches for cycle life prediction using early cycles and more details not presented in the article and the supplementary information. Particularly, we applied regularized ridge, lasso and elastic net linear regression models using features extracted from capacity fade curves, discharge voltage curves, and other data such as internal resistance and cell can temperature. We found that due to the limitation in the quantity and quality of the data from costly and lengthy battery testing a careful hyperparameter tuning may be required and that model features need to be extracted based on the domain knowledge.

Keywords

Acknowledgement

이 성과는 2018년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구입니다(NRF-2018R1A5A1025224).

References

  1. H. D. Lee and O. T. Lim, "Policy suggestions for forstering the industry of using end of life ev batteries", Trans Korean Hydrogen New Energy Soc, Vol. 32, No. 4, 2021, pp. 263-270, doi: https://doi.org/10.7316/KHNES.2021.32.4.263.
  2. G. Reis, C. Strange, M. Yadav, and S. Li, "Lithium-ion battery data and where to find it", Energy and AI, Vol. 5, 2021, pp. 10081, doi: https://doi.org/10.1016/j.egyai.2021.100081.
  3. K. A. Severson, Pe. M. Attia, N. Jin, N. Perkins, B. Jiang, Z. Yang, M. H. Chen, M. Aykol, P. K. Herring, D. Fraggedakis, M. Z. Bazant, S. J. Harris, W. C. Chueh, and R. D. Braatz "Data-driven prediction of battery cycle life before capacity degradation", Nat Energy, Vol. 4, 2019, pp. 383-391, doi: https://doi.org/10.1038/s41560-019-0356-8.
  4. C. H. Sim and H. S. Kim, "Basic Investigation into the Validity of Thermal Analysis of 18650 Li-ion Battery Pack Using CFD Simulation", Trans Korean Hydrogen New Energy Soc, Vol. 31, No. 5, 2020, pp. 489-497, doi: https://doi.org/10.7316/KHNES.2020.31.5.489.
  5. M. Lewerenz, J. Munnix, J. Schmalstieg, S. Kabitz, M. Knips, and D. U. Sauer, "Systematic aging of commercial LiFePO4 | Graphite cylindrical cells including a theory explaining rise of capacity during aging", J. Power Sources, Vol. 345, 2017, pp. 254-263, doi: https://doi.org/10.1016/j.jpowsour.2017.01.133.
  6. R. Tibshirani, "Regression shrinkage and selection via the lasso." JSTOR, Vol. 58, No. 1, 1996, pp. 267-288, Retrieved from https://www.jstor.org/stable/2346178.
  7. H. Zou and T. Hastie, "Regularization and variable selection via the elastic net", Journal of the Royal Statistical Society: Series B, Vol. 67, No. 2, 2005, pp.301-320, doi: https://doi.org/10.1111/j.1467-9868.2005.00503.x.