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Towards a BMS2 Design Framework: Adaptive Data-driven State-of-health Estimation for Second-Life Batteries with BIBO Stability Guarantees (2401.04734v2)

Published 8 Jan 2024 in eess.SY and cs.SY

Abstract: A key challenge that is currently hindering the widespread use of retired electric vehicle (EV) batteries for second-life (SL) applications is the ability to accurately estimate and monitor their state of health (SOH). Second-life battery systems can be sourced from different battery packs with lack of knowledge of their historical usage. To tackle the in-the-field use of SL batteries, this paper introduces an online adaptive health estimation approach with guaranteed bounded-input-bounded-output (BIBO) stability. This method relies exclusively on operational data that can be accessed in real time from SL batteries. The effectiveness of the proposed approach is shown on a laboratory aged experimental data set of retired EV batteries. The estimator gains are dynamically adapted to accommodate the distinct characteristics of each individual cell, making it a promising candidate for future SL battery management systems (BMS2).

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References (28)
  1. N. Lutsey, M. Grant, S. Wappelhorst, and H. Zhou, “Power play: How governments are spurring the electric vehicle industry.”   ICCT Washington, DC, USA, 2018.
  2. J. Eddy, A. Pfeiffer, and J. van de Staaij, “Recharging economies: The ev-battery manufacturing outlook for europe,” McKinsey & Company, 2019.
  3. P. Pavlínek, “Transition of the automotive industry towards electric vehicle production in the east european integrated periphery,” Empirica, vol. 50, no. 1, pp. 35–73, 2023.
  4. B. Jones, V. Nguyen-Tien, and R. J. Elliott, “The electric vehicle revolution: Critical material supply chains, trade and development,” The World Economy, vol. 46, no. 1, pp. 2–26, 2023.
  5. Q. Dong, S. Liang, J. Li, H. C. Kim, W. Shen, and T. J. Wallington, “Cost, energy, and carbon footprint benefits of second-life electric vehicle battery use,” iScience, 2023.
  6. J. Lu, R. Xiong, J. Tian, C. Wang, C.-W. Hsu, N.-T. Tsou, F. Sun, and J. Li, “Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning,” Energy Storage Materials, vol. 50, pp. 139–151, 2022.
  7. A. Weng, E. Dufek, and A. Stefanopoulou, “Battery passports for promoting electric vehicle resale and repurposing,” Joule, pp. 837––842, 2023.
  8. X. Hu, X. Deng, F. Wang, Z. Deng, X. Lin, R. Teodorescu, and M. G. Pecht, “A Review of Second-Life Lithium-Ion Batteries for Stationary Energy Storage Applications,” Proceedings of the IEEE, vol. 110, no. 6, pp. 735–753, jun 2022.
  9. G. Pozzato, S. B. Lee, and S. Onori, “Modeling degradation of Lithium-ion batteries for second-life applications: preliminary results,” CCTA 2021 - 5th IEEE Conference on Control Technology and Applications, pp. 826–831, 2021.
  10. Y. Jiang, J. Jiang, C. Zhang, W. Zhang, Y. Gao, and N. Li, “State of health estimation of second-life lifepo4 batteries for energy storage applications,” Journal of Cleaner Production, vol. 205, pp. 754–762, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0959652618328725
  11. J. Wei, G. Dong, and Z. Chen, “Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression,” IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5634–5643, 2018.
  12. A. Takahashi, A. Allam, and S. Onori, “Evaluating the feasibility of batteries for second-life applications using machine learning,” iScience, vol. 26, no. 4, p. 106547, 2023. [Online]. Available: https://doi.org/10.1016/j.isci.2023.106547
  13. C. Zhang, J. Jiang, W. Zhang, Y. Wang, S. M. Sharkh, and R. Xiong, “A novel data-driven fast capacity estimation of spent electric vehicle lithium-ion batteries,” Energies, vol. 7, no. 12, pp. 8076–8094, 2014. [Online]. Available: https://www.mdpi.com/1996-1073/7/12/8076
  14. A. Bhatt, W. Ongsakul, N. Madhu, and J. G. Singh, “Machine learning-based approach for useful capacity prediction of second-life batteries employing appropriate input selection,” International Journal of Energy Research, vol. 45, no. 15, pp. 21 023–21 049, dec 2021.
  15. X. Li, Y. Dai, Y. Ge, J. Liu, Y. Shan, and L.-Y. Duan, “Uncertainty modeling for out-of-distribution generalization,” arXiv preprint arXiv:2202.03958, 2022.
  16. Y. Zhang, T. Wik, J. Bergström, M. Pecht, and C. Zou, “A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data,” Journal of Power Sources, vol. 526, p. 231110, 2022.
  17. C. She, Y. Li, C. Zou, T. Wik, Z. Wang, and F. Sun, “Offline and online blended machine learning for lithium-ion battery health state estimation,” IEEE Transactions on Transportation Electrification, vol. 8, no. 2, pp. 1604–1618, 2021.
  18. J. Zhou, D. Liu, Y. Peng, and X. Peng, “An optimized relevance vector machine with incremental learning strategy for lithium-ion battery remaining useful life estimation,” in 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).   IEEE, 2013, pp. 561–565.
  19. Y. Xing, E. W. Ma, K.-L. Tsui, and M. Pecht, “An ensemble model for predicting the remaining useful performance of lithium-ion batteries,” Microelectronics Reliability, vol. 53, no. 6, pp. 811–820, 2013. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0026271412005227
  20. Y. Zhang, T. Wik, J. Bergström, M. Pecht, and C. Zou, “A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data,” Journal of Power Sources, vol. 526, apr 2022.
  21. K. Liu, Q. Peng, Y. Che, Y. Zheng, K. Li, R. Teodorescu, D. Widanage, and A. Barai, “Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects,” Advances in Applied Energy, p. 100117, 2022.
  22. J. Lu, R. Xiong, J. Tian, C. Wang, and F. Sun, “Deep learning to estimate lithium-ion battery state of health without additional degradation experiments,” Nature Communications, vol. 14, no. 1, p. 2760, 2023.
  23. F. Von Bülow and T. Meisen, “State of health forecasting of heterogeneous lithium-ion battery types and operation enabled by transfer learning,” in PHM Society European Conference, vol. 7, no. 1, 2022, pp. 490–508.
  24. S. Zhang, H. Zhu, J. Wu, and Z. Chen, “Voltage relaxation-based state-of-health estimation of lithium-ion batteries using convolutional neural networks and transfer learning,” Journal of Energy Storage, vol. 73, p. 108579, 2023.
  25. K. A. Severson, P. M. Attia, N. Jin, N. Perkins, B. Jiang, Z. Yang, M. H. Chen, M. Aykol, P. K. Herring, D. Fraggedakis et al., “Data-driven prediction of battery cycle life before capacity degradation,” Nature Energy, vol. 4, no. 5, pp. 383–391, 2019.
  26. C. P. Aiken, E. R. Logan, A. Eldesoky, H. Hebecker, J. Oxner, J. Harlow, M. Metzger, and J. Dahn, “Li[\chNi_0.50.50.50.5Mn_0.30.30.30.3Co_0.20.20.20.2] \ceO2 as a superior alternative to \ceLiFePO4 for long-lived low voltage li-ion cells,” Journal of The Electrochemical Society, vol. 169, no. 5, p. 050512, 2022.
  27. X. Cui, M. A. Khan, G. Pozzato, R. Sharma, S. Singh, and S. Onori, “From exhausted to empowered: Experiments, data analysis, and health estimation for second-life batteries,” Cell Reports Physical Science (under review).
  28. P. M. Attia, A. Bills, F. B. Planella, P. Dechent, G. Dos Reis, M. Dubarry, P. Gasper, R. Gilchrist, S. Greenbank, D. Howey et al., ““knees” in lithium-ion battery aging trajectories,” Journal of The Electrochemical Society, vol. 169, no. 6, p. 060517, 2022.

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