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Non-Invasive Detection of Lithium-Metal Battery Degradation

Author

Listed:
  • Pietro Iurilli

    (Sustainable Energy Center, CSEM, 2002 Neuchâtel, Switzerland)

  • Luigi Luppi

    (Sustainable Energy Center, CSEM, 2002 Neuchâtel, Switzerland
    Department of Energy, Politecnico di Milano, 20156 Milano, Italy)

  • Claudio Brivio

    (Department of Energy, Politecnico di Milano, 20156 Milano, Italy)

Abstract

The application of Lithium Metal Batteries (LMBs) as secondary cells is still limited due to dendrite degradation mechanisms arising with cycling and responsible for safety risk and early cell failure. Studies to prevent and suppress dendritic growth using state-of-the-art materials are in continuous development. Specific detection techniques can be applied to verify the internal condition of new LMB chemistries through cycling tests. In this work, six non-invasive and BMS-triggerable detection techniques are investigated to anticipate LMB failures and to lay the basis for innovative self-healing mechanisms. The novel methodology is based on: (i) defining detection parameters to track the evolution of cell aging, (ii) defining a detection algorithm and applying it to cycling data, and (iii) validating the algorithm in its capability to detect failure. The proposed methodology is applied to Li||NMC pouch cells. The main outcomes of the work include the characterization results of the tested LMBs under different cycling conditions, the detection techniques performance evaluation, and a sensitivity analysis to identify the most performing parameter and its activation threshold.

Suggested Citation

  • Pietro Iurilli & Luigi Luppi & Claudio Brivio, 2022. "Non-Invasive Detection of Lithium-Metal Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:6904-:d:920566
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    References listed on IDEAS

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    1. Peter M. Attia & Aditya Grover & Norman Jin & Kristen A. Severson & Todor M. Markov & Yang-Hung Liao & Michael H. Chen & Bryan Cheong & Nicholas Perkins & Zi Yang & Patrick K. Herring & Muratahan Ayko, 2020. "Closed-loop optimization of fast-charging protocols for batteries with machine learning," Nature, Nature, vol. 578(7795), pages 397-402, February.
    2. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
    3. Jie Xiao & Qiuyan Li & Yujing Bi & Mei Cai & Bruce Dunn & Tobias Glossmann & Jun Liu & Tetsuya Osaka & Ryuta Sugiura & Bingbin Wu & Jihui Yang & Ji-Guang Zhang & M. Stanley Whittingham, 2020. "Understanding and applying coulombic efficiency in lithium metal batteries," Nature Energy, Nature, vol. 5(8), pages 561-568, August.
    4. Chaojiang Niu & Hongkyung Lee & Shuru Chen & Qiuyan Li & Jason Du & Wu Xu & Ji-Guang Zhang & M. Stanley Whittingham & Jie Xiao & Jun Liu, 2019. "High-energy lithium metal pouch cells with limited anode swelling and long stable cycles," Nature Energy, Nature, vol. 4(7), pages 551-559, July.
    5. Julian Estaller & Anton Kersten & Manuel Kuder & Torbjörn Thiringer & Richard Eckerle & Thomas Weyh, 2022. "Overview of Battery Impedance Modeling Including Detailed State-of-the-Art Cylindrical 18650 Lithium-Ion Battery Cell Comparisons," Energies, MDPI, vol. 15(10), pages 1-21, May.
    6. Yang, Fangfang & Wang, Dong & Zhao, Yang & Tsui, Kwok-Leung & Bae, Suk Joo, 2018. "A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries," Energy, Elsevier, vol. 145(C), pages 486-495.
    7. Chengcheng Fang & Jinxing Li & Minghao Zhang & Yihui Zhang & Fan Yang & Jungwoo Z. Lee & Min-Han Lee & Judith Alvarado & Marshall A. Schroeder & Yangyuchen Yang & Bingyu Lu & Nicholas Williams & Migue, 2019. "Quantifying inactive lithium in lithium metal batteries," Nature, Nature, vol. 572(7770), pages 511-515, August.
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