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Sensing as the key to battery lifetime and sustainability

Author

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  • Jiaqiang Huang

    (Collège de France, Chimie du Solide et de l’Energie—UMR 8260 CNRS
    Réseau sur le Stockage Electrochimique de l’Energie (RS2E)—FR, CNRS 3459)

  • Steven T. Boles

    (The Hong Kong Polytechnic University
    Faculty of Engineering, Norwegian University of Science and Technology (NTNU))

  • Jean-Marie Tarascon

    (Collège de France, Chimie du Solide et de l’Energie—UMR 8260 CNRS
    Réseau sur le Stockage Electrochimique de l’Energie (RS2E)—FR, CNRS 3459)

Abstract

Recent economic and productivity gains of rechargeable batteries have cemented their dominance in energy-intensive societies. With demand soaring, enhancing battery performance through continuous monitoring is essential to limiting their environmental footprint. Although some benefits of sensing have been known for a century, the convergence of fibre optic techniques with new battery platforms is poised to change the industry as a wealth of chemical, thermal and mechanical data will transform the utilization strategies for new and used lithium-ion devices alike. This Review highlights recent advances and associated benefits with a focus on optical sensors that could improve the sustainability of batteries.

Suggested Citation

  • Jiaqiang Huang & Steven T. Boles & Jean-Marie Tarascon, 2022. "Sensing as the key to battery lifetime and sustainability," Nature Sustainability, Nature, vol. 5(3), pages 194-204, March.
    • Handle: RePEc:nat:natsus:v:5:y:2022:i:3:d:10.1038_s41893-022-00859-y
    DOI: 10.1038/s41893-022-00859-y
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    Citations

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    Cited by:

    1. Fu Liu & Wenqing Lu & Jiaqiang Huang & Vanessa Pimenta & Steven Boles & Rezan Demir-Cakan & Jean-Marie Tarascon, 2023. "Detangling electrolyte chemical dynamics in lithium sulfur batteries by operando monitoring with optical resonance combs," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    2. Jiang, Bo & Tao, Siyi & Wang, Xueyuan & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique," Energy, Elsevier, vol. 278(PA).
    3. Wenxin Mei & Zhi Liu & Chengdong Wang & Chuang Wu & Yubin Liu & Pengjie Liu & Xudong Xia & Xiaobin Xue & Xile Han & Jinhua Sun & Gaozhi Xiao & Hwa-yaw Tam & Jacques Albert & Qingsong Wang & Tuan Guo, 2023. "Operando monitoring of thermal runaway in commercial lithium-ion cells via advanced lab-on-fiber technologies," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    4. Yuqiang Zeng & Fengyu Shen & Buyi Zhang & Jaeheon Lee & Divya Chalise & Qiye Zheng & Yanbao Fu & Sumanjeet Kaur & Sean D. Lubner & Vincent S. Battaglia & Bryan D. McCloskey & Michael C. Tucker & Ravi , 2023. "Nonintrusive thermal-wave sensor for operando quantification of degradation in commercial batteries," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

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