Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Wireless transmission of internal hazard signals in Li-ion batteries

Nature volume 641, pages 639–645 (2025)Cite this article

Subjects

Abstract

High-capacity lithium-ion batteries (LIBs) play a critical role as power sources across diverse applications, including portable electronics, electric vehicles (EVs) and renewable-energy-storage systems1. However, there is growing concern about the safety of integrated LIB systems, with reports of up to 9,486 incidents between 2020 and 2024 (ref. 2). To ensure the safe application of commercial LIBs, it is essential to capture internal signals that enable early failure diagnosis and warning. Monitoring non-uniform temperature and strain distributions within the jelly-roll structures of the battery provides a promising approach to achieving this goal3,4. Here we propose a miniaturized and low-power-consumption system capable of accurate sensing and wireless transmission of internal temperature and strain signals inside LIBs, with negligible influence on its performance. The acquisition of internal temperature signals and the area ratio between initial internal-short-circuited regions and battery electrodes enables quantitative analysis of thermal fusing and thermal runaway phenomena, leading to the evaluation of the intensity of battery thermal runaway and recognition of thermal abuse behaviours. This work provides a foundation for designing next-generation smart LIBs with safety warning and failure positioning capabilities.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: An implantable sensing system for accurate sensing and wireless transmission of internal temperature and strain signals inside the LIBs.
Fig. 2: Operando measurement of internal strain and temperature.
Fig. 3: Localization of mechanical failure in cylindrical batteries.
Fig. 4: Detection of thermal failure in prismatic batteries.

Similar content being viewed by others

Data availability

The main data generated or analysed during this study are included in this article, the Extended Data and the Supplementary information. All other relevant data of this study are available from the corresponding author on request.

References

  1. Schmuch, R., Wagner, R., Hörpel, G., Placke, T. & Winter, M. Performance and cost of materials for lithium-based rechargeable automotive batteries. Nat. Energy 3, 267–278 (2018).

    Article  ADS  CAS  Google Scholar 

  2. UL Solutions. Lithium-ion battery incident reporting. UL Solutions https://www.ul.com/insights/lithium-ion-battery-incident-reporting (2024).

  3. Armand, M. & Tarascon, J.-M. Building better batteries. Nature 451, 652–657 (2008).

    Article  ADS  CAS  PubMed  Google Scholar 

  4. Wang, J. et al. Fire-extinguishing organic electrolytes for safe batteries. Nat. Energy 3, 22–29 (2017).

    Article  ADS  Google Scholar 

  5. Deng, J., Bae, C., Marcicki, J., Masias, A. & Miller, T. Safety modelling and testing of lithium-ion batteries in electrified vehicles. Nat. Energy 3, 261–266 (2018).

    Article  ADS  Google Scholar 

  6. Zhu, Y. et al. Fast lithium growth and short circuit induced by localized-temperature hotspots in lithium batteries. Nat. Commun. 10, 2067 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  7. Waldmann, T. et al. A mechanical aging mechanism in lithium-ion batteries. J. Electrochem. Soc. 161, A1742–A1747 (2014).

    Article  CAS  Google Scholar 

  8. Pfrang, A. et al. Geometrical inhomogeneities as cause of mechanical failure in commercial 18650 lithium ion cells. J. Electrochem. Soc. 166, A3745–A3752 (2019).

    Article  CAS  Google Scholar 

  9. Willenberg, L. et al. The development of jelly roll deformation in 18650 lithium-ion batteries at low state of charge. J. Electrochem. Soc. 167, 120502 (2020).

    Article  ADS  CAS  Google Scholar 

  10. Finegan, D. P. et al. Identifying the cause of rupture of Li-ion batteries during thermal runaway. Adv. Sci. 5, 1700369 (2018).

    Article  Google Scholar 

  11. Pfrang, A. et al. Deformation from formation until end of life: micro X-ray computed tomography of silicon alloy containing 18650 Li-ion cells. J. Electrochem. Soc. 170, 030548 (2023).

    Article  ADS  CAS  Google Scholar 

  12. Tranter, T. G., Timms, R., Shearing, P. R. & Brett, D. J. L. Communication—Prediction of thermal issues for larger format 4680 cylindrical cells and their mitigation with enhanced current collection. J. Electrochem. Soc. 167, 160544 (2020).

    Article  ADS  CAS  Google Scholar 

  13. Heenan, T. M. M. et al. Mapping internal temperatures during high-rate battery applications. Nature 617, 507–512 (2023).

    Article  ADS  CAS  PubMed  Google Scholar 

  14. Ziesche, R. F. et al. 4D imaging of lithium-batteries using correlative neutron and X-ray tomography with a virtual unrolling technique. Nat. Commun. 11, 777 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. Zhang, G. et al. In situ measurement of radial temperature distributions in cylindrical Li-ion cells. J. Electrochem. Soc. 161, A1499–A1507 (2014).

    Article  CAS  Google Scholar 

  16. Zhang, G. et al. Reaction temperature sensing (RTS)-based control for Li-ion battery safety. Sci. Rep. 5, 18237 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Albero Blanquer, L. et al. Optical sensors for operando stress monitoring in lithium-based batteries containing solid-state or liquid electrolytes. Nat. Commun. 13, 1153 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Miao, Z. et al. Direct optical fiber monitor on stress evolution of the sulfur-based cathodes for lithium–sulfur batteries. Energy Environ. Sci. 15, 2029–2038 (2022).

    Article  CAS  Google Scholar 

  19. Mei, W. et al. Operando monitoring of thermal runaway in commercial lithium-ion cells via advanced lab-on-fiber technologies. Nat. Commun. 14, 5251 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  20. Huang, J. et al. Operando decoding of chemical and thermal events in commercial Na(Li)-ion cells via optical sensors. Nat. Energy 5, 674–683 (2020).

    Article  ADS  CAS  Google Scholar 

  21. Wang, W. et al. Deciphering advanced sensors for life and safety monitoring of lithium batteries. Adv. Energy Mater. 14, 2304173 (2024).

    Article  CAS  Google Scholar 

  22. Zhu, S. et al. A novel embedded method for in-situ measuring internal multi-point temperatures of lithium ion batteries. J. Power Sources 456, 227981 (2020).

    Article  CAS  Google Scholar 

  23. Yang, L. et al. Internal field study of 21700 battery based on long-life embedded wireless temperature sensor. Acta Mech. Sin. 37, 895–901 (2021).

    Article  ADS  CAS  Google Scholar 

  24. Zhu, S. et al. In operando measuring circumferential internal strain of 18650 Li-ion batteries by thin film strain gauge sensors. J. Power Sources 516, 230669 (2021).

    Article  CAS  Google Scholar 

  25. Noelle, D. J., Wang, M. & Qiao, Y. Improved safety and mechanical characterizations of thick lithium-ion battery electrodes structured with porous metal current collectors. J. Power Sources 399, 125–132 (2018).

    Article  ADS  CAS  Google Scholar 

  26. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).

    Article  ADS  Google Scholar 

  27. Chatzakis, J., Kalaitzakis, K., Voulgaris, N. C. & Manias, S. N. Designing a new generalized battery management system. IEEE Trans. Ind. Electron. 50, 990–999 (2003).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key R&D Program of China (grant no. 2021YFB2401900).

Author information

Author notes

  1. These authors contributed equally: Jinbao Fan, Chenchen Liu, Na Li

Authors and Affiliations

  1. Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing, China

    Jinbao Fan, Chenchen Liu, Le Yang, Wei-Li Song, Hao-Sen Chen & Daining Fang

  2. State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing, China

    Na Li

  3. National Engineering Research Center of Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China

    Xiao-Guang Yang

  4. School of Information and Electronics, Beijing Institute of Technology, Beijing, China

    Bowen Dou & Shujuan Hou

  5. School of Vehicle and Mobility, Tsinghua University, Beijing, China

    Xuning Feng

  6. School of Engineering, Westlake University, Hangzhou, China

    Hanqing Jiang

  7. Institute of Physics, Chinese Academy of Sciences, Beijing, China

    Hong Li

  8. School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China

    Lei Sun

  9. Mechano-X Institute, Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University, Beijing, China

    Huajian Gao

Authors
  1. Jinbao Fan
    View author publications

    Search author on:PubMed Google Scholar

  2. Chenchen Liu
    View author publications

    Search author on:PubMed Google Scholar

  3. Na Li
    View author publications

    Search author on:PubMed Google Scholar

  4. Le Yang
    View author publications

    Search author on:PubMed Google Scholar

  5. Xiao-Guang Yang
    View author publications

    Search author on:PubMed Google Scholar

  6. Bowen Dou
    View author publications

    Search author on:PubMed Google Scholar

  7. Shujuan Hou
    View author publications

    Search author on:PubMed Google Scholar

  8. Xuning Feng
    View author publications

    Search author on:PubMed Google Scholar

  9. Hanqing Jiang
    View author publications

    Search author on:PubMed Google Scholar

  10. Hong Li
    View author publications

    Search author on:PubMed Google Scholar

  11. Wei-Li Song
    View author publications

    Search author on:PubMed Google Scholar

  12. Lei Sun
    View author publications

    Search author on:PubMed Google Scholar

  13. Hao-Sen Chen
    View author publications

    Search author on:PubMed Google Scholar

  14. Huajian Gao
    View author publications

    Search author on:PubMed Google Scholar

  15. Daining Fang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

J.F., C.L. and N.L. wrote the manuscript. L.S. designed and built the chips. J.F. and C.L. designed and built the batteries. L.Y. and X.-G.Y. designed the experiments. J.F. and C.L. carried out the experiments and performed 3D numerical simulations. B.D. and S.H. built the backpropagation neural network model. X.F. optimized the thermal runaway experiments. J.F., C.L. and N.L. analysed and interpreted the results. H.J., H.L., W.-L.S. and H.-S.C. assisted in results interpretation and feedback. W.-L.S. assisted with figure creation. H.-S.C. conceived the idea, supervised the project and analysed the data. H.G. and D.F. supervised the project. All authors contributed to the preparation of the manuscript.

Corresponding authors

Correspondence to Wei-Li Song, Lei Sun or Hao-Sen Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Jiangong Zhu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Communication principle and reliability of the implantable sensing system.

a, Circuit diagram of the implantable sensing system integrated with a strain sensor connected to a Wheatstone bridge. b, Principle of signal modulation. c, The ratio of power consumption of wireless transmission with different types of battery for mode 1. Battery 1, Battery 2 and Battery 3 are 26Ah-Tesla, 190Ah-BYD and 280Ah-CATL, respectively. The data are retrieved from the media releases of Tesla, BYD and CATL. d, The stability of temperature sensors in an electrolyte environment over 70 days. e,f, Optical pictures of temperature (e) and strain (f) sensors before and after immersing in electrolyte. g,h, Calibration results of the implantable sensing systems for the temperature (g) and strain sensors (h).

Extended Data Fig. 2 Integration process of implantable sensing systems.

a, The size of the strain sensor. b, Schematic illustration of the integrated sensor processes. c,d, Pictures of the integrated temperature sensor (c) and strain sensor (d) processes. e,f, Pictures of chip–sensor–battery integrated design in prismatic (e) and cylindrical (f) batteries. The sizes of the thin-film temperature and strain sensors are 60 × 8 × 0.05 mm3 and 70 × 5 × 0.05 mm3, respectively (50 μm: thickness of the dried slurry from active materials, binders and conductive agents on the current collectors).

Extended Data Fig. 3 The impact of implanting sensing systems on battery performance.

a–c, EIS (a), DRT (b) and resistance (c) of prismatic batteries with and without (w/ and w/o, respectively) the implantable sensing system. d–f, EIS (d), DRT (e) and resistance (f) of cylindrical batteries with and without the implantable sensing system. g,h, Charge—discharge curves (g) and rate performance (h) of prismatic batteries with and without the implantable sensing system. i,j, Charge—discharge curves (i) and rate performance (j) of cylindrical batteries with and without the implantable sensing system.

Extended Data Fig. 4 The cost analysis of implantable sensing systems (details in Supplementary information).

a, The costs of the sensors and chips. b, The costs for different types of battery. c,d, The cost ratios of implantable sensing systems for temperature (c) and strain (d) sensors in different types of battery.

Extended Data Fig. 5 Decoupling electrochemically induced and thermally induced strain for prismatic batteries.

a, Strain responses in different layers of the prismatic batteries to temperature in the thermal loading experiment. b,c, Internal temperature and strain of prismatic batteries at currents of 1.0 C (b) and 2.0 C (c). d–f, Decoupled strain and temperature for prismatic batteries at currents of 0.5 C (d), 1.0 C (e) and 2.0 C (f).

Extended Data Fig. 6 Decoupling electrochemically induced and thermally induced strain in cylindrical batteries.

a, Strain responses in different layers of cylindrical batteries to temperature during the thermal loading experiment. b,c, Internal temperature and strain of cylindrical batteries at currents of 1.0 C (b) and 2.0 C (c). d–f, Decoupled strain and temperature for cylindrical batteries at currents of 0.5 C (d), 1.0 C (e) and 2.0 C (f).

Extended Data Fig. 7 Internal inhomogeneous strain in different layers of cylindrical batteries.

a–f, Internal strain in different winding layers during cycles at the rate of 0.2 C: 4th and 15th layers (a); 5th and 15th layers (b); 9th and 15th layers (c); 11th and 15th layers (d); 13th layer (e); 14th layer (f). g, The spiral-wound structure geometric model of cylindrical batteries. h, Schematic diagram of load and constraints. i, Circumferential strain contour of Al current collector by simulation.

Extended Data Fig. 8 Electrode fracture in cylindrical batteries.

a, Picture of electrode fracture in a positive electrode. b, X-ray tomography image of the battery with electrode fracture. c,d, Evolution of internal strain in pristine battery (c) and battery with pre-set fracture (d). e, Circumferential strain contours of the battery with pre-set fracture. f, BP neural network training and testing loss in electrode fracture localization.

Extended Data Fig. 9 ISC in prismatic batteries.

a, Schematic illustration of the ISC device principle. b, Equivalent circuit of the ‘short-circuit electronic resistance’. c, Pictures of the ISC device within the battery. d, Pictures of ISC positions after ISC triggering in case 1. e–g, ISC simulation of case 1 (details in Supplementary information). h,i, Post-mortem pictures of the battery after global thermal runaway of case 2.

Extended Data Fig. 10 Voltage and temperature responses of prismatic batteries with winding jelly roll at different α values during ISC.

a, α = 0.00000776 (70 Ah). b, α = 0.00000776 (70 Ah). c, α = 0.00031 (50 Ah). d, α = 0.00037 (70 Ah). e, α = 0.00061 (0.9 Ah). f, α = 0.00067 (0.81 Ah). g,h, Post-mortem pictures at α = 0.00061 and α = 0.00067.

Supplementary information

Supplementary Information (download DOCX )

This file contains Supplementary Text, Supplementary Figs. 1–12, Supplementary Tables 1–8 and Supplementary References.

Supplementary Video 1 (download MP4 )

Wireless transmission of internal temperature and strain signals in lithium-ion batteries.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, J., Liu, C., Li, N. et al. Wireless transmission of internal hazard signals in Li-ion batteries. Nature 641, 639–645 (2025). https://doi.org/10.1038/s41586-025-08785-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41586-025-08785-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing