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State-of-the-art characterization techniques for advanced lithium-ion batteries

Author

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  • Jun Lu

    (Argonne National Laboratory)

  • Tianpin Wu

    (Advanced Photon Sources (APS), Argonne National Laboratory)

  • Khalil Amine

    (Argonne National Laboratory)

Abstract

To meet future needs for industries from personal devices to automobiles, state-of-the-art rechargeable lithium-ion batteries will require both improved durability and lowered costs. To enhance battery performance and lifetime, understanding electrode degradation mechanisms is of critical importance. Various advanced in situ and operando characterization tools developed during the past few years have proven indispensable for optimizing battery materials, understanding cell degradation mechanisms, and ultimately improving the overall battery performance. Here we review recent progress in the development and application of advanced characterization techniques such as in situ transmission electron microscopy for high-performance lithium-ion batteries. Using three representative electrode systems—layered metal oxides, Li-rich layered oxides and Si-based or Sn-based alloys—we discuss how these tools help researchers understand the battery process and design better battery systems. We also summarize the application of the characterization techniques to lithium–sulfur and lithium–air batteries and highlight the importance of those techniques in the development of next-generation batteries.

Suggested Citation

  • Jun Lu & Tianpin Wu & Khalil Amine, 2017. "State-of-the-art characterization techniques for advanced lithium-ion batteries," Nature Energy, Nature, vol. 2(3), pages 1-13, March.
    • Handle: RePEc:nat:natene:v:2:y:2017:i:3:d:10.1038_nenergy.2017.11
    DOI: 10.1038/nenergy.2017.11
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    Citations

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    Cited by:

    1. David Beck & Philipp Dechent & Mark Junker & Dirk Uwe Sauer & Matthieu Dubarry, 2021. "Inhomogeneities and Cell-to-Cell Variations in Lithium-Ion Batteries, a Review," Energies, MDPI, vol. 14(11), pages 1-25, June.
    2. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2021. "Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data," Energy, Elsevier, vol. 225(C).
    3. He, Guannan & Ciez, Rebecca & Moutis, Panayiotis & Kar, Soummya & Whitacre, Jay F., 2020. "The economic end of life of electrochemical energy storage," Applied Energy, Elsevier, vol. 273(C).
    4. Nan Zhou & Xiulong Cui & Changhao Han & Zhou Yang, 2022. "Analysis of Acoustic Characteristics under Battery External Short Circuit Based on Acoustic Emission," Energies, MDPI, vol. 15(5), pages 1-16, February.
    5. Jamila Hemdani & Laid Degaa & Moez Soltani & Nassim Rizoug & Achraf Jabeur Telmoudi & Abdelkader Chaari, 2022. "Battery Lifetime Prediction via Neural Networks with Discharge Capacity and State of Health," Energies, MDPI, vol. 15(22), pages 1-17, November.
    6. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    7. Chao-Yu Li & Ming Chen & Shuai Liu & Xinyao Lu & Jinhui Meng & Jiawei Yan & Héctor D. Abruña & Guang Feng & Tianquan Lian, 2022. "Unconventional interfacial water structure of highly concentrated aqueous electrolytes at negative electrode polarizations," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    8. Ashleigh Townsend & Rupert Gouws, 2022. "A Comparative Review of Lead-Acid, Lithium-Ion and Ultra-Capacitor Technologies and Their Degradation Mechanisms," Energies, MDPI, vol. 15(13), pages 1-29, July.

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