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A review on various optical fibre sensing methods for batteries

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  • Han, Gaoce
  • Yan, Jize
  • Guo, Zhen
  • Greenwood, David
  • Marco, James
  • Yu, Yifei

Abstract

Batteries have rapidly evolved and are widely applied in both stationary and transport applications. The safe and reliable operation is of vital importance to all types of batteries, herein an effective battery sensing system with high performance and easy implementation is critically needed. This also requires the sensing system to monitor the states of batteries in real time. Among the available methods, optical fibre sensors have shown a significant advantage due to their advanced capabilities of which include the fast measurement of multiple parameters with high sensitivity, working without interfering the battery performance, being able to be composited in multiplexed configurations and being robust to various harsh environment conditions. This paper mainly discusses the current optical fibre sensing methods for batteries in terms of the working principles and critical reviews the sensing performance corresponding to different sensing parameters. Moreover, the challenges and outlooks for future research on battery sensing are derived.

Suggested Citation

  • Han, Gaoce & Yan, Jize & Guo, Zhen & Greenwood, David & Marco, James & Yu, Yifei, 2021. "A review on various optical fibre sensing methods for batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:rensus:v:150:y:2021:i:c:s1364032121007930
    DOI: 10.1016/j.rser.2021.111514
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    References listed on IDEAS

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    1. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    2. Ximing Cheng & Michael Pecht, 2017. "In Situ Stress Measurement Techniques on Li-ion Battery Electrodes: A Review," Energies, MDPI, vol. 10(5), pages 1-19, April.
    3. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    4. Raijmakers, L.H.J. & Danilov, D.L. & Eichel, R.-A. & Notten, P.H.L., 2019. "A review on various temperature-indication methods for Li-ion batteries," Applied Energy, Elsevier, vol. 240(C), pages 918-945.
    5. Alotto, Piergiorgio & Guarnieri, Massimo & Moro, Federico, 2014. "Redox flow batteries for the storage of renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 325-335.
    6. Aleksandra Fortier & Max Tsao & Nick D. Williard & Yinjiao Xing & Michael G. Pecht, 2017. "Preliminary Study on Integration of Fiber Optic Bragg Grating Sensors in Li-Ion Batteries and In Situ Strain and Temperature Monitoring of Battery Cells," Energies, MDPI, vol. 10(7), pages 1-11, June.
    7. Jiaqiang Huang & Laura Albero Blanquer & Julien Bonefacino & E. R. Logan & Daniel Alves Dalla Corte & Charles Delacourt & Betar M. Gallant & Steven T. Boles & J. R. Dahn & Hwa-Yaw Tam & Jean-Marie Tar, 2020. "Operando decoding of chemical and thermal events in commercial Na(Li)-ion cells via optical sensors," Nature Energy, Nature, vol. 5(9), pages 674-683, September.
    8. Shovon Goutam & Jean-Marc Timmermans & Noshin Omar & Peter Van den Bossche & Joeri Van Mierlo, 2015. "Comparative Study of Surface Temperature Behavior of Commercial Li-Ion Pouch Cells of Different Chemistries and Capacities by Infrared Thermography," Energies, MDPI, vol. 8(8), pages 1-18, August.
    9. Deng, Zhongwei & Hu, Xiaosong & Lin, Xianke & Che, Yunhong & Xu, Le & Guo, Wenchao, 2020. "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," Energy, Elsevier, vol. 205(C).
    10. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    11. Chao-Tsung Ma, 2020. "A Novel State of Charge Estimating Scheme Based on an Air-Gap Fiber Interferometer Sensor for the Vanadium Redox Flow Battery," Energies, MDPI, vol. 13(2), pages 1-13, January.
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