Issue

Vol. 17 (2025)

Sensors Innovations for Smart Lithium-Based Batteries: Advancements, Opportunities, and Potential Challenges

https://doi.org/10.1007/s40820-025-01786-1
Jamile Mohammadi Moradian
Institute for Advanced Materials, School of Materials Science and Engineering, Jiangsu University, Zhenjiang 212013, People’s Republic of China
Amjad Ali
College of Materials Science and Engineering, Nanjing Forestry University, Nanjing 210037, People’s Republic of China
Xuehua Yan
Institute for Advanced Materials, School of Materials Science and Engineering, Jiangsu University, Zhenjiang 212013, People’s Republic of China
Gang Pei
Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei 230027, People’s Republic of China
Shu Zhang
College of Materials Science and Engineering, Nanjing Forestry University, Nanjing 210037, People’s Republic of China
Ahmad Naveed
School of Materials Science and Engineering, Jiangsu University, Zhenjiang 212013, People’s Republic of China
Khurram Shehzad
Institute of Physics, Silesian University of Technology, Konarskiego 22B, 44‑100 Gliwice, Poland
Zohreh Shahnavaz
Institute for Advanced Materials, School of Materials Science and Engineering, Jiangsu University, Zhenjiang 212013, People’s Republic of China
Farooq Ahmad
School of Materials Science and Engineering, Jiangsu University, Zhenjiang 212013, People’s Republic of China
Balal Yousaf
Department of Technologies and Installations for Waste Management, Faculty of Energy and Environmental Engineering, Silesian University of Technology, 44‑100 Gliwice, Poland

Corresponding Author: Shu Zhang

s.zhang@njfu.edu.cn

Nano-Micro Letters, Vol. 17 (2025), Article Number: 279

Abstract

Lithium-based batteries (LiBs) are integral components in operating electric vehicles to renewable energy systems and portable electronic devices, thanks to their unparalleled energy density, minimal self-discharge rates, and favorable cycle life. However, the inherent safety risks and performance degradation of LiB over time impose continuous monitoring facilitated by sophisticated battery management systems (BMS). This review comprehensively analyzes the current state of sensor technologies for smart LiBs, focusing on their advancements, opportunities, and potential challenges. Sensors are classified into two primary groups based on their application: safety monitoring and performance optimization. Safety monitoring sensors, including temperature, pressure, strain, gas, acoustic, and magnetic sensors, focus on detecting conditions that could lead to hazardous situations. Performance optimization sensors, such as optical-based and electrochemical-based, monitor factors such as state of charge and state of health, emphasizing operational efficiency and lifespan. The review also highlights the importance of integrating these sensors with advanced algorithms and control approaches to optimize charging and discharge cycles. Potential advancements driven by nanotechnology, wireless sensor networks, miniaturization, and machine learning algorithms are also discussed. However, challenges related to sensor miniaturization, power consumption, cost efficiency, and compatibility with existing BMS need to be addressed to fully realize the potential of LiB sensor technologies. This comprehensive review provides valuable insights into the current landscape and future directions of sensor innovations in smart LiBs, guiding further research and development efforts to enhance battery performance, reliability, and safety.


Highlights:
1 Sensors for smart Lithium-based batteries (LiBs) are classified based on their application into safety monitoring (i.e., temperature, pressure, and strain) to detect hazardous conditions and performance optimization (i.e., optical and electrochemical sensors) for monitoring factors such as state of charge and state of health.
2 The potential for innovation in LiB sensor technology is driven by advancements in nanotechnology, miniaturization, machine learning algorithms, and wireless sensor networks, all of which contribute to enhanced sensor performance.
3 Key challenges faced in developing LiB sensors include miniaturization, power consumption, cost efficiency and scalability, and compatibility with existing battery management systems.

Keywords

Lithium-based batteries Sensors Battery management systems Thermal runaway State of charge State of health
Moradian, Jamile Mohammadi, Amjad Ali, Xuehua Yan, Gang Pei, Shu Zhang, Ahmad Naveed, Khurram Shehzad, Zohreh Shahnavaz, Farooq Ahmad, and Balal Yousaf. 2025. “Sensors Innovations for Smart Lithium-Based Batteries: Advancements, Opportunities, and Potential Challenges”. Nano-Micro Letters 17 (May):279. https://doi.org/10.1007/s40820-025-01786-1.
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. L. Albero Blanquer, F. Marchini, J.R. Seitz, N. Daher, F. Bétermier et al., Optical sensors for operando stress monitoring in lithium-based batteries containing solid-state or liquid electrolytes. Nat. Commun. 13(1), 1153 (2022). https://doi.org/10.1038/s41467-022-28792-w
  2. A. Jinasena, L. Spitthoff, M.S. Wahl, J.J. Lamb, P.R. Shearing et al., Online internal temperature sensors in lithium-ion batteries: state-of-the-art and future trends. Front. Chem. Eng. 4, 804704 (2022). https://doi.org/10.3389/fceng.2022.804704
  3. J. Xiao, F. Shi, T. Glossmann, C. Burnett, Z. Liu, From laboratory innovations to materials manufacturing for lithium-based batteries. Nat. Energy 8(4), 329–339 (2023). https://doi.org/10.1038/s41560-023-01221-y
  4. X.-B. Cheng, C.-Z. Zhao, Y.-X. Yao, H. Liu, Q. Zhang, Recent advances in energy chemistry between solid-state electrolyte and safe lithium-metal anodes. Chem 5(1), 74–96 (2019). https://doi.org/10.1016/j.chempr.2018.12.002
  5. P.K. Kausthubharam, S. Koorata, Panchal, Thermal management of large-sized LiFePO4 pouch cell using simplified mini-channel cold plates. Appl. Therm. Eng. 234, 121286 (2023). https://doi.org/10.1016/j.applthermaleng.2023.121286
  6. A. Bais, D. Subhedar, S. Panchal, Experimental investigation of longevity and temperature of a lithium-ion battery cell using phase change material based battery thermal management system. Mater. Today Proc. (2023). https://doi.org/10.1016/j.matpr.2023.08.103
  7. Q. Sun, Z. Gong, T. Zhang, J. Li, X. Zhu et al., Molecule-level multiscale design of nonflammable gel polymer electrolyte to build stable SEI/CEI for lithium metal battery. Nano-Micro Lett. 17(1), 18 (2024). https://doi.org/10.1007/s40820-024-01508-z
  8. J. Schnell, T. Günther, T. Knoche, C. Vieider, L. Köhler et al., All-solid-state lithium-ion and lithium metal batteries–paving the way to large-scale production. J. Power. Sources 382, 160–175 (2018). https://doi.org/10.1016/j.jpowsour.2018.02.062
  9. W. Liu, M.-S. Song, B. Kong, Y. Cui, Flexible and stretchable energy storage: recent advances and future perspectives. Adv. Mater. 29(1), 1603436 (2017). https://doi.org/10.1002/adma.201603436
  10. A.K. Joshi, P. Kakati, D. Dandotiya, P.S. Pandiyan, N.G. Patil et al., Computational analysis of preheating cylindrical lithium-ion batteries with fin-assisted phase change material. Int. J. Mod. Phys. C 35(4), 2450047 (2024). https://doi.org/10.1142/S0129183124500475
  11. D. Subhedar, K.V. Chauhan, S. Panchal, A. Bais, Numerical investigation of performance for liquid-cooled cylindrical electrical vehicle battery pack using Al2O3/EG-water nano coolant. Mater. Today Proc. (2023). https://doi.org/10.1016/j.matpr.2023.08.055
  12. R. Yang, Y. Xie, K. Li, M.-K. Tran, M. Fowler et al., Comparative study on the thermal characteristics of solid-state lithium-ion batteries. IEEE Trans. Transp. Electrif. 10(1), 1541–1557 (2024). https://doi.org/10.1109/TTE.2023.3289997
  13. B. Li, C.M. Jones, T.E. Adams, V. Tomar, Sensor based In-operando lithium-ion battery monitoring in dynamic service environment. J. Power. Sources 486, 229349 (2021). https://doi.org/10.1016/j.jpowsour.2020.229349
  14. X. Peng, J. Han, Q. Zhang, Y. Xiang, X. Hu, Real-time mechanical and thermal monitoring of lithium batteries with PVDF-TrFE thin films integrated within the battery. Sens. Actuat. A Phys. 338, 113484 (2022). https://doi.org/10.1016/j.sna.2022.113484
  15. J. Schmitt, B. Kraft, J.P. Schmidt, B. Meir, K. Elian et al., Measurement of gas pressure inside large-format prismatic lithium-ion cells during operation and cycle aging. J. Power. Sources 478, 228661 (2020). https://doi.org/10.1016/j.jpowsour.2020.228661
  16. P. Nazari, R. Bäuerle, J. Zimmermann, C. Melzer, C. Schwab et al., Piezoresistive free-standing microfiber strain sensor for high-resolution battery thickness monitoring. Adv. Mater. 35(21), 2212189 (2023). https://doi.org/10.1002/adma.202212189
  17. Y. Song, N. Lyu, S. Shi, X. Jiang, Y. Jin, Safety warning for lithium-ion batteries by module-space air-pressure variation under thermal runaway conditions. J. Energy Storage 56, 105911 (2022). https://doi.org/10.1016/j.est.2022.105911
  18. J. Peng, X. Zhou, S. Jia, Y. Jin, S. Xu et al., High precision strain monitoring for lithium ion batteries based on fiber Bragg grating sensors. J. Power. Sources 433, 226692 (2019). https://doi.org/10.1016/j.jpowsour.2019.226692
  19. R.-Q. Su, J.-J. Zhu, Q.-R. Kong, X. Yao, High-performance 0.75Li3V2(PO4)3·0.25Li3PO4/C composite cathode for lithium-ion batteries. Rare Met. 43(11), 6081–6087 (2024). https://doi.org/10.1007/s12598-024-02896-2
  20. K. Romanenko, P.W. Kuchel, A. Jerschow, Accurate visualization of operating commercial batteries using specialized magnetic resonance imaging with magnetic field sensing. Chem. Mater. 32(5), 2107–2113 (2020). https://doi.org/10.1021/acs.chemmater.9b05246
  21. Z. Wang, L. Zhu, J. Liu, J. Wang, W. Yan, Gas sensing technology for the detection and early warning of battery thermal runaway: a review. Energy Fuels 36(12), 6038–6057 (2022). https://doi.org/10.1021/acs.energyfuels.2c01121
  22. Z. Huang, Y. Zhou, Z. Deng, K. Huang, M. Xu et al., Precise state-of-charge mapping via deep learning on ultrasonic transmission signals for lithium-ion batteries. ACS Appl. Mater. Interfaces 15(6), 8217–8223 (2023). https://doi.org/10.1021/acsami.2c22210
  23. X. Ling, Q. Zhang, Y. Xiang, J.S. Chen, X. Peng et al., A Cu/Ni alloy thin-film sensor integrated with current collector for in situ monitoring of lithium-ion battery internal temperature by high-throughput selecting method. Int. J. Heat Mass Transf. 214, 124383 (2023). https://doi.org/10.1016/j.ijheatmasstransfer.2023.124383
  24. S. Zhu, L. Yang, J. Wen, X. Feng, P. Zhou 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). https://doi.org/10.1016/j.jpowsour.2021.230669
  25. Z. Yi, Z. Chen, K. Yin, L. Wang, K. Wang, Sensing as the key to the safety and sustainability of new energy storage devices. Prot. Control Mod. Power Syst. 8(2), 1–22 (2023). https://doi.org/10.1186/s41601-023-00300-2
  26. C.R. Michel, L. Meza-León, Development of a UV-visible-NIR sensor based on LiNiO2 prepared by the coprecipitation method. Sens. Actuat. A Phys. 321, 112429 (2021). https://doi.org/10.1016/j.sna.2020.112429
  27. Y. Han, Y. Zhao, A. Ming, Y. Fang, S. Fang et al., Application of an NDIR sensor system developed for early thermal runaway warning of automotive batteries. Energies 16(9), 3620 (2023). https://doi.org/10.3390/en16093620
  28. W. Gao, Z. Zhi, S. Fan, Z. Hua, H. Li et al., Amperometric hydrogen sensor based on solid polymer electrolyte and titanium foam electrode. ACS Omega 7(28), 24895–24902 (2022). https://doi.org/10.1021/acsomega.2c03610
  29. S.C. Kim, X. Kong, R.A. Vilá, W. Huang, Y. Chen et al., Potentiometric measurement to probe solvation energy and its correlation to lithium battery cyclability. J. Am. Chem. Soc. 143(27), 10301–10308 (2021). https://doi.org/10.1021/jacs.1c03868
  30. K.W. Knehr, T. Hodson, C. Bommier, G. Davies, A. Kim et al., Understanding full-cell evolution and non-chemical electrode crosstalk of Li-ion batteries. Joule 2(6), 1146–1159 (2018). https://doi.org/10.1016/j.joule.2018.03.016
  31. J. Zheng, H. Jiang, X. Xu, J. Zhao, X. Ma et al., In situ partial-cyclized polymerized acrylonitrile-coated NCM811 cathode for high-temperature ≥ 100 °C stable solid-state lithium metal batteries. Nano-Micro Lett. 17(1), 195 (2025). https://doi.org/10.1007/s40820-025-01683-7
  32. Z. Li, F. Cao, Y. Zhang, S. Zhang, B. Tang, Enhancing thermal protection in lithium batteries with power bank-inspired multi-network aerogel and thermally induced flexible composite phase change material. Nano-Micro Lett. 17(1), 166 (2025). https://doi.org/10.1007/s40820-024-01593-0
  33. X. Zhang, N. Zhao, H. Zhang, Y. Fan, F. Jin et al., Recent advances in wide-range temperature metal-CO2 batteries: a mini review. Nano-Micro Lett. 17(1), 99 (2024). https://doi.org/10.1007/s40820-024-01607-x
  34. W. Lv, C. Zhu, J. Chen, C. Ou, Q. Zhang et al., High performance of low-temperature electrolyte for lithium-ion batteries using mixed additives. Chem. Eng. J. 418, 129400 (2021). https://doi.org/10.1016/j.cej.2021.129400
  35. Y. Ji, Y. Zhang, C.-Y. Wang, Li-ion cell operation at low temperatures. J. Electrochem. Soc. 160(4), A636–A649 (2013). https://doi.org/10.1149/2.047304jes
  36. S. Wang, S.-Y. Liu, A. Khataee, K.-Z. Qi, One-step pore diffusion mechanism of Li+ in solid electrolyte interphase for fast-charging lithium-ion battery. Rare Met. 43(7), 3438–3440 (2024). https://doi.org/10.1007/s12598-024-02695-9
  37. Y. Chen, Q. He, Y. Zhao, W. Zhou, P. Xiao et al., Breaking solvation dominance of ethylene carbonate via molecular charge engineering enables lower temperature battery. Nat. Commun. 14(1), 8326 (2023). https://doi.org/10.1038/s41467-023-43163-9
  38. K.-F. Ren, H. Liu, J.-X. Guo, X. Sun, C. Guo et al., Pulse charge suppressing dendrite growth at low temperature by rapidly replenishing lithium ion on anode surface. Chemsuschem 18(2), e202401401 (2025). https://doi.org/10.1002/cssc.202401401
  39. Y. Xiao, Model-based virtual thermal sensors for lithium-ion battery in EV applications. IEEE Trans. Ind. Electron. 62(5), 3112–3122 (2015). https://doi.org/10.1109/TIE.2014.2386793
  40. B. Gulsoy, T.A. Vincent, J.E.H. Sansom, J. Marco, In-situ temperature monitoring of a lithium-ion battery using an embedded thermocouple for smart battery applications. J. Energy Storage 54, 105260 (2022). https://doi.org/10.1016/j.est.2022.105260
  41. C.-Y. Lee, S.-J. Lee, M.-S. Tang, P.-C. Chen, In situ monitoring of temperature inside lithium-ion batteries by flexible micro temperature sensors. Sensors 11(10), 9942–9950 (2011). https://doi.org/10.3390/s111009942
  42. M.S.K. Mutyala, J. Zhao, J. Li, H. Pan, C. Yuan et al., In-situ temperature measurement in lithium ion battery by transferable flexible thin film thermocouples. J. Power. Sources 260, 43–49 (2014). https://doi.org/10.1016/j.jpowsour.2014.03.004
  43. D. Kong, H. Lv, P. Ping, G. Wang, A review of early warning methods of thermal runaway of lithium ion batteries. J. Energy Storage 64, 107073 (2023). https://doi.org/10.1016/j.est.2023.107073
  44. S. Goutam, J.-M. Timmermans, N. Omar, P. Van den Bossche, J. Van Mierlo, Comparative study of surface temperature behavior of commercial Li-ion pouch cells of different chemistries and capacities by infrared thermography. Energies 8(8), 8175–8192 (2015). https://doi.org/10.3390/en8088175
  45. L. Zhao, C. Wu, X. Zhang, Y. Zhang, C. Zhang et al., Integrated arrays of micro resistance temperature detectors for monitoring of the short-circuit point in lithium metal batteries. Batteries 8(12), 264 (2022). https://doi.org/10.3390/batteries8120264
  46. Y. Shen, S. Wang, H. Li, K. Wang, K. Jiang, An overview on in situ/operando battery sensing methodology through thermal and stress measurements. J. Energy Storage 64, 107164 (2023). https://doi.org/10.1016/j.est.2023.107164
  47. C. Xu, X. Feng, W. Huang, Y. Duan, T. Chen et al., Internal temperature detection of thermal runaway in lithium-ion cells tested by extended-volume accelerating rate calorimetry. J. Energy Storage 31, 101670 (2020). https://doi.org/10.1016/j.est.2020.101670
  48. R.R. Richardson, P.T. Ireland, D.A. Howey, Battery internal temperature estimation by combined impedance and surface temperature measurement. J. Power. Sources 265, 254–261 (2014). https://doi.org/10.1016/j.jpowsour.2014.04.129
  49. D. Anthony, D. Wong, D. Wetz, A. Jain, Non-invasive measurement of internal temperature of a cylindrical Li-ion cell during high-rate discharge. Int. J. Heat Mass Transf. 111, 223–231 (2017). https://doi.org/10.1016/j.ijheatmasstransfer.2017.03.095
  50. M. Nascimento, M.S. Ferreira, J.L. Pinto, Real time thermal monitoring of lithium batteries with fiber sensors and thermocouples: a comparative study. Measurement 111, 260–263 (2017). https://doi.org/10.1016/j.measurement.2017.07.049
  51. M. Parhizi, M.B. Ahmed, A. Jain, Determination of the core temperature of a Li-ion cell during thermal runaway. J. Power. Sources 370, 27–35 (2017). https://doi.org/10.1016/j.jpowsour.2017.09.086
  52. T. Waldmann, G. Bisle, B.-I. Hogg, S. Stumpp, M.A. Danzer et al., Influence of cell design on temperatures and temperature gradients in lithium-ion cells: an in operando study. J. Electrochem. Soc. 162(6), A921–A927 (2015). https://doi.org/10.1149/2.0561506jes
  53. T. Shan, Z. Wang, X. Zhu, H. Wang, Y. Zhou et al., Explosion behavior investigation and safety assessment of large-format lithium-ion pouch cells. J. Energy Chem. 72, 241–257 (2022). https://doi.org/10.1016/j.jechem.2022.04.018
  54. M. Yang, M. Rong, Y. Ye, A. Yang, J. Chu et al., Comprehensive analysis of gas production for commercial LiFePO4 batteries during overcharge-thermal runaway. J. Energy Storage 72, 108323 (2023). https://doi.org/10.1016/j.est.2023.108323
  55. M. Debert, G. Colin, G. Bloch, Y. Chamaillard, An observer looks at the cell temperature in automotive battery packs. Control. Eng. Pract. 21(8), 1035–1042 (2013). https://doi.org/10.1016/j.conengprac.2013.03.001
  56. T.A. Vincent, B. Gulsoy, J.E.H. Sansom, J. Marco, Development of an in-vehicle power line communication network with in situ instrumented smart cells. Transp. Eng. 6, 100098 (2021). https://doi.org/10.1016/j.treng.2021.100098
  57. J. Fleming, T. Amietszajew, J. Charmet, A.J. Roberts, D. Greenwood et al., The design and impact of in situ and operando thermal sensing for smart energy storage. J. Energy Storage 22, 36–43 (2019). https://doi.org/10.1016/j.est.2019.01.026
  58. J. Christensen, D. Cook, P. Albertus, An efficient parallelizable 3D thermoelectrochemical model of a Li-ion cell. J. Electrochem. Soc. 160(11), A2258–A2267 (2013). https://doi.org/10.1149/2.086311jes
  59. D. Chalise, K. Shah, T. Halama, L. Komsiyska, A. Jain, An experimentally validated method for temperature prediction during cyclic operation of a Li-ion cell. Int. J. Heat Mass Transf. 112, 89–96 (2017). https://doi.org/10.1016/j.ijheatmasstransfer.2017.04.115
  60. P. Wang, X. Zhang, L. Yang, X. Zhang, M. Yang et al., Real-time monitoring of internal temperature evolution of the lithium-ion coin cell battery during the charge and discharge process. Extreme Mech. Lett. 9, 459–466 (2016). https://doi.org/10.1016/j.eml.2016.03.013
  61. W. Wang, Y. Zhang, B. Xie, L. Huang, S. Dong et al., Deciphering advanced sensors for life and safety monitoring of lithium batteries. Adv. Energy Mater. 14(24), 2304173 (2024). https://doi.org/10.1002/aenm.202304173
  62. L.H.J. Raijmakers, D.L. Danilov, R.A. Eichel, P.H.L. Notten, A review on various temperature-indication methods for Li-ion batteries. Appl. Energy 240, 918–945 (2019). https://doi.org/10.1016/j.apenergy.2019.02.078
  63. T. Amietszajew, J. Fleming, A.J. Roberts, W.D. Widanage, D. Greenwood et al., Hybrid thermo-electrochemical in situ instrumentation for lithium-ion energy storage. Batter. Supercaps 2(11), 934–940 (2019). https://doi.org/10.1002/batt.201900109
  64. B. Lu, W. Bao, W. Yao, J.-M. Doux, C. Fang et al., Editors’ choice: methods: pressure control apparatus for lithium metal batteries. J. Electrochem. Soc. 169(7), 070537 (2022). https://doi.org/10.1149/1945-7111/ac834c
  65. Z. Chen, J. Lin, C. Zhu, Q. Zhuang, Q. Chen et al., Detection of jelly roll pressure evolution in large-format Li-ion batteries via in situ thin film flexible pressure sensors. J. Power. Sources 566, 232960 (2023). https://doi.org/10.1016/j.jpowsour.2023.232960
  66. A.J. Louli, L.D. Ellis, J.R. Dahn, operando pressure measurements reveal solid electrolyte interphase growth to rank Li-ion cell performance. Joule 3(3), 745–761 (2019). https://doi.org/10.1016/j.joule.2018.12.009
  67. L.K. Willenberg, P. Dechent, G. Fuchs, D.U. Sauer, E. Figgemeier, High-precision monitoring of volume change of commercial lithium-ion batteries by using strain gauges. Sustainability 12(2), 557 (2020). https://doi.org/10.3390/su12020557
  68. L. Wang, X. Duan, B. Liu, Q.M. Li, S. Yin et al., Deformation and failure behaviors of anode in lithium-ion batteries: Model and mechanism. J. Power. Sources 448, 227468 (2020). https://doi.org/10.1016/j.jpowsour.2019.227468
  69. A.M. Boyce, E. Martínez-Pañeda, A. Wade, Y.S. Zhang, J.J. Bailey et al., Cracking predictions of lithium-ion battery electrodes by X-ray computed tomography and modelling. J. Power. Sources 526, 231119 (2022). https://doi.org/10.1016/j.jpowsour.2022.231119
  70. H. Zappen, G. Fuchs, A. Gitis, D.U. Sauer, In-operando impedance spectroscopy and ultrasonic measurements during high-temperature abuse experiments on lithium-ion batteries. Batteries 6(2), 25 (2020). https://doi.org/10.3390/batteries6020025
  71. W. Ren, T. Zheng, C. Piao, D.E. Benson, X. Wang et al., Characterization of commercial 18, 650 Li-ion batteries using strain gauges. J. Mater. Sci. 57(28), 13560–13569 (2022). https://doi.org/10.1007/s10853-022-07490-4
  72. P. Mohtat, S. Lee, J.B. Siegel, A.G. Stefanopoulou, Reversible and irreversible expansion of lithium-ion batteries under a wide range of stress factors. J. Electrochem. Soc. 168(10), 100520 (2021). https://doi.org/10.1149/1945-7111/ac2d3e
  73. A.W. Golubkov, S. Scheikl, R. Planteu, G. Voitic, H. Wiltsche et al., Thermal runaway of commercial 18650 Li-ion batteries with LFP and NCA cathodes–impact of state of charge and overcharge. RSC Adv. 5(70), 57171–57186 (2015). https://doi.org/10.1039/C5RA05897J
  74. Z. Teng, C. Lv, Detection toward early-stage thermal runaway gases of Li-ion battery by semiconductor sensor. Front. Chem. 13, 1586903 (2025). https://doi.org/10.3389/fchem.2025.1586903
  75. L. Torres-Castro, A.M. Bates, N.B. Johnson, G. Quintana, L. Gray, Early detection of Li-ion battery thermal runaway using commercial diagnostic technologies. J. Electrochem. Soc. 171(2), 020520 (2024). https://doi.org/10.1149/1945-7111/ad2440
  76. T. Cai, P. Valecha, V. Tran, B. Engle, A. Stefanopoulou et al., Detection of Li-ion battery failure and venting with carbon dioxide sensors. eTransportation 7, 100100 (2021). https://doi.org/10.1016/j.etran.2020.100100
  77. X.-X. Wang, Q.-T. Li, X.-Y. Zhou, Y.-M. Hu, X. Guo, Monitoring thermal runaway of lithium-ion batteries by means of gas sensors. Sens. Actuat. B Chem. 411, 135703 (2024). https://doi.org/10.1016/j.snb.2024.135703
  78. P.J. Bugryniec, E.G. Resendiz, S.M. Nwophoke, S. Khanna, C. James et al., Review of gas emissions from lithium-ion battery thermal runaway failure: considering toxic and flammable compounds. J. Energy Storage 87, 111288 (2024). https://doi.org/10.1016/j.est.2024.111288
  79. Z. Liao, J. Zhang, Z. Gan, Y. Wang, J. Zhao et al., Thermal runaway warning of lithium-ion batteries based on photoacoustic spectroscopy gas sensing technology. Int. J. Energy Res. 46(15), 21694–21702 (2022). https://doi.org/10.1002/er.8632
  80. J.P. Vivek, N. Garcia-Araez, Differences in interfacial reactivity of graphite and lithium metal battery electrodes investigated via operando gas analysis. J. Phys. Chem. C Nanomater. Interfaces 128(32), 13395–13401 (2024). https://doi.org/10.1021/acs.jpcc.4c03656
  81. C. Essl, L. Seifert, M. Rabe, A. Fuchs, Early detection of failing automotive batteries using gas sensors. Batteries 7(2), 25 (2021). https://doi.org/10.3390/batteries7020025
  82. O. Lupan, H. Krüger, L. Siebert, N. Ababii, N. Kohlmann et al., Additive manufacturing as a means of gas sensor development for battery health monitoring. Chemosensors 9(9), 252 (2021). https://doi.org/10.3390/chemosensors9090252
  83. Q. Chen, Y. Zhang, M. Tang, Z. Wang, D. Zhang, A fast response hydrogen sensor based on the heterojunction of MXene and SnO2 nanosheets for lithium-ion battery failure detection. Sens. Actuat. B Chem. 405, 135229 (2024). https://doi.org/10.1016/j.snb.2023.135229
  84. Y. Jin, Z. Zheng, D. Wei, X. Jiang, H. Lu et al., Detection of micro-scale Li dendrite via H2 gas capture for early safety warning. Joule 4(8), 1714–1729 (2020). https://doi.org/10.1016/j.joule.2020.05.016
  85. Y. Fernandes, A. Bry, S. de Persis, Identification and quantification of gases emitted during abuse tests by overcharge of a commercial Li-ion battery. J. Power. Sources 389, 106–119 (2018). https://doi.org/10.1016/j.jpowsour.2018.03.034
  86. L. Luo, J. Chen, A.G. Hui, R. Liu, Y. Zhou et al., Highly sensitive non-dispersive infrared gas sensor with innovative application for monitoring carbon dioxide emissions from lithium-ion battery thermal runaway. Micromachines 16(1), 36 (2024). https://doi.org/10.3390/mi16010036
  87. M. Xu, Y. Xu, J. Tao, L. Wen, C. Zheng et al., Development of a compact NDIR CO2 gas sensor for harsh environments. Infrared Phys. Technol. 136, 105035 (2024). https://doi.org/10.1016/j.infrared.2023.105035
  88. J.O. Majasan, J.B. Robinson, R.E. Owen, M. Maier, A.N.P. Radhakrishnan et al., Recent advances in acoustic diagnostics for electrochemical power systems. J. Phys. Energy 3(3), 032011 (2021). https://doi.org/10.1088/2515-7655/abfb4a
  89. K. Zhang, J. Yin, Y. He, Acoustic emission detection and analysis method for health status of lithium ion batteries. Sensors 21(3), 712 (2021). https://doi.org/10.3390/s21030712
  90. Z. Wang, X. Zhao, H. Zhang, D. Zhen, F. Gu et al., Active acoustic emission sensing for fast co-estimation of state of charge and state of health of the lithium-ion battery. J. Energy Storage 64, 107192 (2023). https://doi.org/10.1016/j.est.2023.107192
  91. S. Schweidler, M. Bianchini, P. Hartmann, T. Brezesinski, J. Janek, The sound of batteries: an operando acoustic emission study of the LiNiO2 cathode in Li–ion cells. Batter. Supercaps 3(10), 1021–1027 (2020). https://doi.org/10.1002/batt.202000099
  92. S. Komagata, N. Kuwata, R. Baskaran, J. Kawamura, K. Sato et al., Detection of degradation of lithium-ion batteries with acoustic emission technique. ECS Trans. 25(33), 163–167 (2010). https://doi.org/10.1149/1.3334804
  93. J.B. Robinson, M. Maier, G. Alster, T. Compton, D.J.L. Brett et al., Spatially resolved ultrasound diagnostics of Li-ion battery electrodes. Phys. Chem. Chem. Phys. 21(12), 6354–6361 (2019). https://doi.org/10.1039/C8CP07098A
  94. H. Sun, N. Muralidharan, R. Amin, V. Rathod, P. Ramuhalli et al., Ultrasonic nondestructive diagnosis of lithium-ion batteries with multiple frequencies. J. Power. Sources 549, 232091 (2022). https://doi.org/10.1016/j.jpowsour.2022.232091
  95. Y. Wu, Y. Wang, W.K.C. Yung, M. Pecht, Ultrasonic health monitoring of lithium-ion batteries. Electronics 8(7), 751 (2019). https://doi.org/10.3390/electronics8070751
  96. A.G. Hsieh, S. Bhadra, B.J. Hertzberg, P.J. Gjeltema, A. Goy et al., Electrochemical-acoustic time of flight: in operando correlation of physical dynamics with battery charge and health. Energy Environ. Sci. 8(5), 1569–1577 (2015). https://doi.org/10.1039/C5EE00111K
  97. Z. Zhou, W. Hua, S. Peng, Y. Tian, J. Tian et al., Fast and smart state characterization of large-format lithium-ion batteries via phased-array ultrasonic sensing technology. Sensors 24(21), 7061 (2024). https://doi.org/10.3390/s24217061
  98. F. Brauchle, F. Grimsmann, O. von Kessel, K.P. Birke, Direct measurement of current distribution in lithium-ion cells by magnetic field imaging. J. Power. Sources 507, 230292 (2021). https://doi.org/10.1016/j.jpowsour.2021.230292
  99. K. Shen, X. Xu, Y. Tang, Recent progress of magnetic field application in lithium-based batteries. Nano Energy 92, 106703 (2022). https://doi.org/10.1016/j.nanoen.2021.106703
  100. G. Ruan, J. Hua, X. Hu, C. Yu, Study on the influence of magnetic field on the performance of lithium-ion batteries. Energy Rep. 8, 1294–1304 (2022). https://doi.org/10.1016/j.egyr.2022.02.095
  101. C.M. Costa, K.J. Merazzo, R. Gonçalves, C. Amos, S. Lanceros-Méndez, Magnetically active lithium-ion batteries towards battery performance improvement. iScience 24(6), 102691 (2021). https://doi.org/10.1016/j.isci.2021.102691
  102. R. Chen, J. Jiao, Z. Chen, Y. Wang, T. Deng et al., Power batteries health monitoring: a magnetic imaging method based on magnetoelectric sensors. Materials 15(5), 1980 (2022). https://doi.org/10.3390/ma15051980
  103. A.J. Ilott, M. Mohammadi, C.M. Schauerman, M.J. Ganter, A. Jerschow, Rechargeable lithium-ion cell state of charge and defect detection by in situ inside-out magnetic resonance imaging. Nat. Commun. 9(1), 1776 (2018). https://doi.org/10.1038/s41467-018-04192-x
  104. D. Zou, M. Li, D. Wang, N. Li, R. Su et al., Temperature estimation of lithium-ion battery based on an improved magnetic nanop thermometer. IEEE Access 8, 135491–135498 (2020). https://doi.org/10.1109/ACCESS.2020.3007932
  105. J. Gao, J. Wang, L. Zhang, Q. Yu, Y. Huang et al., Magnetic signature analysis for smart security system based on TMR magnetic sensor array. IEEE Sens. J. 19(8), 3149–3155 (2019). https://doi.org/10.1109/JSEN.2019.2891082
  106. Y. Zhang, Q. Tang, Y. Zhang, J. Wang, U. Stimming et al., Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning. Nat. Commun. 11(1), 1706 (2020). https://doi.org/10.1038/s41467-020-15235-7
  107. B.G. Carkhuff, P.A. Demirev, R. Srinivasan, Impedance-based battery management system for safety monitoring of lithium-ion batteries. IEEE Trans. Ind. Electron. 65(8), 6497–6504 (2018). https://doi.org/10.1109/TIE.2017.2786199
  108. G. Han, J. Yan, Z. Guo, D. Greenwood, J. Marco et al., A review on various optical fibre sensing methods for batteries. Renew. Sustain. Energy Rev. 150, 111514 (2021). https://doi.org/10.1016/j.rser.2021.111514
  109. D. Chen, Q. Zhao, Y. Zheng, Y. Xu, Y. Chen et al., Recent progress in lithium-ion battery safety monitoring based on fiber Bragg grating sensors. Sensors 23(12), 5609 (2023). https://doi.org/10.3390/s23125609
  110. J. Huang, S.T. Boles, J.-M. Tarascon, Sensing as the key to battery lifetime and sustainability. Nat. Sustain. 5(3), 194–204 (2022). https://doi.org/10.1038/s41893-022-00859-y
  111. A.J. Merryweather, C. Schnedermann, Q. Jacquet, C.P. Grey, A. Rao, operando optical tracking of single-p ion dynamics in batteries. Nature 594(7864), 522–528 (2021). https://doi.org/10.1038/s41586-021-03584-2
  112. J. Huang, L.A. Blanquer, C. Gervillié, J.-M. Tarascon, Distributed fiber optic sensing to assess in-live temperature imaging inside batteries: Rayleigh and FBGs. J. Electrochem. Soc. 168(6), 060520 (2021). https://doi.org/10.1149/1945-7111/ac03f0
  113. Y. Yu, E. Vergori, D. Worwood, Y. Tripathy, Y. Guo et al., Distributed thermal monitoring of lithium ion batteries with optical fibre sensors. J. Energy Storage 39, 102560 (2021). https://doi.org/10.1016/j.est.2021.102560
  114. J. Hedman, D. Nilebo, E. Larsson Langhammer, F. Björefors, Fibre optic sensor for characterisation of lithium-ion batteries. ChemSusChem 13(21), 5731–5739 (2020). https://doi.org/10.1002/cssc.202001709
  115. Y. Yu, E. Vergori, F. Maddar, Y. Guo, D. Greenwood et al., Real-time monitoring of internal structural deformation and thermal events in lithium-ion cell via embedded distributed optical fibre. J. Power. Sources 521, 230957 (2022). https://doi.org/10.1016/j.jpowsour.2021.230957
  116. K.M. Alcock, Á. González-Vila, M. Beg, F. Vedreño-Santos, Z. Cai et al., Individual cell-level temperature monitoring of a lithium-ion battery pack. Sensors 23(9), 4306 (2023). https://doi.org/10.3390/s23094306
  117. E. McTurk, T. Amietszajew, J. Fleming, R. Bhagat, Thermo-electrochemical instrumentation of cylindrical Li-ion cells. J. Power. Sources 379, 309–316 (2018). https://doi.org/10.1016/j.jpowsour.2018.01.060
  118. T. Amietszajew, E. McTurk, J. Fleming, R. Bhagat, Understanding the limits of rapid charging using instrumented commercial 18650 high-energy Li-ion cells. Electrochim. Acta 263, 346–352 (2018). https://doi.org/10.1016/j.electacta.2018.01.076
  119. J. Fleming, T. Amietszajew, E. McTurk, D.P. Towers, D. Greenwood et al., Development and evaluation of in situ instrumentation for cylindrical Li-ion cells using fibre optic sensors. HardwareX 3, 100–109 (2018). https://doi.org/10.1016/j.ohx.2018.04.001
  120. S. Novais, M. Nascimento, L. Grande, M.F. Domingues, P. Antunes et al., Internal and external temperature monitoring of a Li-ion battery with fiber Bragg grating sensors. Sensors 16(9), 1394 (2016). https://doi.org/10.3390/s16091394
  121. Y.-J. Ee, K.-S. Tey, K.-S. Lim, P. Shrivastava, S.B.R.S. Adnan et al., Lithium-ion battery state of charge (SoC) estimation with non-electrical parameter using uniform fiber Bragg grating (FBG). J. Energy Storage 40, 102704 (2021). https://doi.org/10.1016/j.est.2021.102704
  122. X. Han, H. Zhong, K. Li, X. Xue, W. Wu et al., operando monitoring of dendrite formation in lithium metal batteries via ultrasensitive tilted fiber Bragg grating sensors. Light Sci. Appl. 13(1), 24 (2024). https://doi.org/10.1038/s41377-023-01346-5
  123. J. Bonefacino, S. Ghashghaie, T. Zheng, C.-P. Lin, W. Zheng et al., High-fidelity strain and temperature measurements of Li-ion batteries using polymer optical fiber sensors. J. Electrochem. Soc. 169(10), 100508 (2022). https://doi.org/10.1149/1945-7111/ac957e
  124. L. Giammichele, V. D’Alessandro, M. Falone, R. Ricci, Thermal behaviour assessment and electrical characterisation of a cylindrical Lithium-ion battery using infrared thermography. Appl. Therm. Eng. 205, 117974 (2022). https://doi.org/10.1016/j.applthermaleng.2021.117974
  125. N. Saqib, C.M. Ganim, A.E. Shelton, J.M. Porter, On the decomposition of carbonate-based lithium-ion battery electrolytes studied using operando infrared spectroscopy. J. Electrochem. Soc. 165(16), A4051–A4057 (2018). https://doi.org/10.1149/2.1051816jes
  126. Y. Qiao, Z. Zhou, Z. Chen, S. Du, Q. Cheng et al., Visualizing ion diffusion in battery systems by fluorescence microscopy: a case study on the dissolution of LiMn2O4. Nano Energy 45, 68–74 (2018). https://doi.org/10.1016/j.nanoen.2017.12.036
  127. X. Cheng, F. Xian, Z. Hu, C. Wang, X. Du et al., Fluorescence probing of active lithium distribution in lithium metal anodes. Angew. Chem. Int. Ed. 58(18), 5936–5940 (2019). https://doi.org/10.1002/anie.201900105
  128. G. Zhou, X. Sun, Q.-H. Li, X. Wang, J.-N. Zhang et al., Mn ion dissolution mechanism for lithium-ion battery with LiMn2O4 cathode: In situ ultraviolet-visible spectroscopy and Ab initio molecular dynamics simulations. J. Phys. Chem. Lett. 11(8), 3051–3057 (2020). https://doi.org/10.1021/acs.jpclett.0c00936
  129. L. Zhao, E. Chénard, Ö.Ö. Çapraz, N.R. Sottos, S.R. White, Direct detection of manganese ions in organic electrolyte by UV-vis spectroscopy. J. Electrochem. Soc. 165(2), 345–348 (2018). https://doi.org/10.1149/2.1111802jes
  130. T. Gross, C. Hess, Raman diagnostics of LiCoO2 electrodes for lithium-ion batteries. J. Power. Sources 256, 220–225 (2014). https://doi.org/10.1016/j.jpowsour.2014.01.084
  131. M.A. Cabañero, M. Hagen, E. Quiroga-González, In-operando Raman study of lithium plating on graphite electrodes of lithium ion batteries. Electrochim. Acta 374, 137487 (2021). https://doi.org/10.1016/j.electacta.2020.137487
  132. Q. Zhang, T. Liu, C. Hao, Y. Qu, J. Niu et al., In situ Raman investigation on gas components and explosion risk of thermal runaway emission from lithium-ion battery. J. Energy Storage 56, 105905 (2022). https://doi.org/10.1016/j.est.2022.105905
  133. E. Miele, W.M. Dose, I. Manyakin, M.H. Frosz, Z. Ruff et al., Hollow-core optical fibre sensors for operando Raman spectroscopy investigation of Li-ion battery liquid electrolytes. Nat. Commun. 13(1), 1651 (2022). https://doi.org/10.1038/s41467-022-29330-4
  134. S. Fang, M. Yan, R.J. Hamers, Cell design and image analysis for in situ Raman mapping of inhomogeneous state-of-charge profiles in lithium-ion batteries. J. Power. Sources 352, 18–25 (2017). https://doi.org/10.1016/j.jpowsour.2017.03.055
  135. Y.D. Su, Y. Preger, H. Burroughs, C. Sun, P.R. Ohodnicki, Fiber optic sensing technologies for battery management systems and energy storage applications. Sensors 21(4), 1397 (2021). https://doi.org/10.3390/s21041397
  136. K.M. Alcock, M. Grammel, Á. González-Vila, L. Binetti, K. Goh et al., An accessible method of embedding fibre optic sensors on lithium-ion battery surface for in situ thermal monitoring. Sens. Actuat. A Phys. 332, 113061 (2021). https://doi.org/10.1016/j.sna.2021.113061
  137. Z. Liu, Y. Lu, X. Ma, Y. He, M. Fu et al., Advanced functional optical fiber sensors for smart battery monitoring. Energy Mater. Adv. 5, 0142 (2024). https://doi.org/10.34133/energymatadv.0142
  138. W. Jeong, S.-O. Kim, H. Lim, K. Lee, High-resolution thermal monitoring of lithium-ion batteries using Brillouin scattering based fiber optic sensor with flexible spatial arrangement of sensing points. J. Energy Storage 104, 114558 (2024). https://doi.org/10.1016/j.est.2024.114558
  139. Y. Zhang, Y. Li, Z. Guo, J. Li, X. Ge et al., Health monitoring by optical fiber sensing technology for rechargeable batteries. eScience 4(1), 100174 (2024). https://doi.org/10.1016/j.esci.2023.100174
  140. G. Yan, T. Wang, L. Zhu, F. Meng, W. Zhuang, A novel strain-decoupled sensitized FBG temperature sensor and its applications to aircraft thermal management. Opt. Laser Technol. 140, 106597 (2021). https://doi.org/10.1016/j.optlastec.2020.106597
  141. Z. Liang, X. Wang, Y. Ma, J. Yan, W. Di et al., Dual-FBG arrays hybrid measurement technology for mechanical strain, temperature, and thermal strain on composite materials. Phys. Scr. 98(11), 115515 (2023). https://doi.org/10.1088/1402-4896/acfeb6
  142. B. Rente, M. Fabian, M. Vidakovic, X. Liu, X. Li et al., Lithium-ion battery state-of-charge estimator based on FBG-based strain sensor and employing machine learning. IEEE Sens. J. 21(2), 1453–1460 (2021). https://doi.org/10.1109/JSEN.2020.3016080
  143. J. Peng, S. Jia, S. Yang, X. Kang, H. Yu et al., State estimation of lithium-ion batteries based on strain parameter monitored by fiber Bragg grating sensors. J. Energy Storage 52, 104950 (2022). https://doi.org/10.1016/j.est.2022.104950
  144. J. Huang, L. Albero Blanquer, J. Bonefacino, E.R. Logan, D. Alves Dalla Corte et al., operando decoding of chemical and thermal events in commercial Na(Li)-ion cells via optical sensors. Nat. Energy 5(9), 674–683 (2020). https://doi.org/10.1038/s41560-020-0665-y
  145. W. Mei, Z. Liu, C. Wang, C. Wu, Y. Liu et al., operando monitoring of thermal runaway in commercial lithium-ion cells via advanced lab-on-fiber technologies. Nat. Commun. 14(1), 5251 (2023). https://doi.org/10.1038/s41467-023-40995-3
  146. W. Zhang, W. Wan, W. Wu, Z. Zhang, X. Qi, Internal temperature prediction model of the cylindrical lithium-ion battery under different cooling modes. Appl. Therm. Eng. 212, 118562 (2022). https://doi.org/10.1016/j.applthermaleng.2022.118562
  147. Y. Liu, Z. Liu, W. Mei, X. Han, P. Liu et al., operando monitoring Lithium-ion battery temperature via implanting femtosecond-laser-inscribed optical fiber sensors. Measurement 203, 111961 (2022). https://doi.org/10.1016/j.measurement.2022.111961
  148. T. Vegge, J.-M. Tarascon, K. Edström, Toward better and smarter batteries by combining AI with multisensory and self-healing approaches. Adv. Energy Mater. 11(23), 2100362 (2021). https://doi.org/10.1002/aenm.202100362
  149. J. Albert, L.-Y. Shao, C. Caucheteur, Tilted fiber Bragg grating sensors. Laser Photonics Rev. 7(1), 83–108 (2013). https://doi.org/10.1002/lpor.201100039
  150. H.-C. Li, J. Liu, X.-D. He, J. Yuan, Q. Wu et al., Long-period fiber grating based on side-polished optical fiber and its sensing application. IEEE Trans. Instrum. Meas. 72, 7001109 (2023). https://doi.org/10.1109/TIM.2023.3234094
  151. S.O. Obare, C.J. Murphy, A two-color fluorescent lithium ion sensor. Inorg. Chem. 40(23), 6080–6082 (2001). https://doi.org/10.1021/ic010271q
  152. N.A. Padilla, M.T. Rea, M. Foy, S.P. Upadhyay, K.A. Desrochers et al., Tracking lithium ions via widefield fluorescence microscopy for battery diagnostics. ACS Sens. 2(7), 903–908 (2017). https://doi.org/10.1021/acssensors.7b00087
  153. W. Qin, S.O. Obare, C.J. Murphy, S.M. Angel, A fiber-optic fluorescence sensor for lithium ion in acetonitrile. Anal. Chem. 74(18), 4757–4762 (2002). https://doi.org/10.1021/ac020365x
  154. A. Van der Ven, Lithium diffusion in layered LixCoO2. Electrochem. Solid-State Lett. 3(7), 301 (1999). https://doi.org/10.1149/1.1391130
  155. Z. Geng, Y.-C. Chien, M.J. Lacey, T. Thiringer, D. Brandell, Validity of solid-state Li + diffusion coefficient estimation by electrochemical approaches for lithium-ion batteries. Electrochim. Acta 404, 139727 (2022). https://doi.org/10.1016/j.electacta.2021.139727
  156. M. Wang, Y. Song, W. Wei, H. Liang, Y. Yi et al., First fluorescent probe for graphite anodes of lithium-ion battery. Matter 6(3), 873–886 (2023). https://doi.org/10.1016/j.matt.2022.12.014
  157. M.S. Wahl, J. Lamb, E. Sundby, P.J. Thomas, D.R. Hjelme et al., Towards in situ state of health monitoring of lithium-ion batteries using internal fiber-optic sensors. Meet. Abstr. MA2022-01(52), 2166 (2022). https://doi.org/10.1149/ma2022-01522166mtgabs
  158. F. Javanbakht, H. Najafi, K. Jalili, M. Salami-Kalajahi, A review on photochemical sensors for lithium ion detection: relationship between the structure and performance. J. Mater. Chem. A 11(48), 26371–26392 (2023). https://doi.org/10.1039/D3TA06113B
  159. P.U. Nzereogu, A.D. Omah, F.I. Ezema, E.I. Iwuoha, A.C. Nwanya, Anode materials for lithium-ion batteries: a review. Appl. Surf. Sci. Adv. 9, 100233 (2022). https://doi.org/10.1016/j.apsadv.2022.100233
  160. M.S. Kim, B.H. Lee, J.H. Park, H.S. Lee, W. Hooch Antink et al., operando identification of the chemical and structural origin of Li-ion battery aging at near-ambient temperature. J. Am. Chem. Soc. 142(31), 13406–13414 (2020). https://doi.org/10.1021/jacs.0c02203
  161. S. Fang, D. Bresser, S. Passerini, Transition metal oxide anodes for electrochemical energy storage in lithium- and sodium-ion batteries. Adv. Energy Mater. 10(1), 1902485 (2020). https://doi.org/10.1002/aenm.201902485
  162. L. Meyer, N. Saqib, J. Porter, Review: operando optical spectroscopy studies of batteries. J. Electrochem. Soc. 168(9), 090561 (2021). https://doi.org/10.1149/1945-7111/ac2088
  163. T. Aoshima, K. Okahara, C. Kiyohara, K. Shizuka, Mechanisms of manganese spinels dissolution and capacity fade at high temperature. J. Power. Sources 97, 377–380 (2001). https://doi.org/10.1016/S0378-7753(01)00551-1
  164. D. Tang, Y. Sun, Z. Yang, L. Ben, L. Gu et al., Surface structure evolution of LiMn2O4 cathode material upon charge/discharge. Chem. Mater. 26(11), 3535–3543 (2014). https://doi.org/10.1021/cm501125e
  165. Y. Terada, Y. Nishiwaki, I. Nakai, F. Nishikawa, Study of Mn dissolution from LiMn2O4 spinel electrodes using in situ total reflection X-ray fluorescence analysis and fluorescence XAFS technique. J. Power. Sources 97, 420–422 (2001). https://doi.org/10.1016/S0378-7753(01)00741-8
  166. L. Cabo-Fernandez, D. Bresser, F. Braga, S. Passerini, L.J. Hardwick, In-situ electrochemical SHINERS investigation of SEI composition on carbon-coated Zn0.9Fe0.1O anode for lithium-ion batteries. Batter. Supercaps 2(2), 168–177 (2019). https://doi.org/10.1002/batt.201800063
  167. L.J. Hardwick, M. Hahn, P. Ruch, M. Holzapfel, W. Scheifele et al., An in situ Raman study of the intercalation of supercapacitor-type electrolyte into microcrystalline graphite. Electrochim. Acta 52(2), 675–680 (2006). https://doi.org/10.1016/j.electacta.2006.05.053
  168. C. Sole, N.E. Drewett, L.J. Hardwick, In situ Raman study of lithium-ion intercalation into microcrystalline graphite. Faraday Discuss. 172, 223–237 (2014). https://doi.org/10.1039/C4FD00079J
  169. R. Baddour-Hadjean, J.-P. Pereira-Ramos, Raman microspectrometry applied to the study of electrode materials for lithium batteries. Chem. Rev. 110(3), 1278–1319 (2010). https://doi.org/10.1021/cr800344k
  170. T. Nonaka, H. Kawaura, Y. Makimura, Y.F. Nishimura, K. Dohmae, In situ X-ray Raman scattering spectroscopy of a graphite electrode for lithium-ion batteries. J. Power. Sources 419, 203–207 (2019). https://doi.org/10.1016/j.jpowsour.2019.02.064
  171. A.R. Neale, D.C. Milan, F. Braga, I.V. Sazanovich, L.J. Hardwick, Lithium insertion into graphitic carbon observed via operando Kerr-gated Raman spectroscopy enables high state of charge diagnostics. ACS Energy Lett. 7(8), 2611–2618 (2022). https://doi.org/10.1021/acsenergylett.2c01120
  172. D.V. Pelegov, A.A. Koshkina, B.N. Slautin, V.S. Gorshkov, Statistical Raman spectroscopy characterization of carbon additive in low-C composites: Toward industrial quality control. J. Raman Spectrosc. 50(7), 1015–1026 (2019). https://doi.org/10.1002/jrs.5604
  173. Y. Zhu, J. Xie, A. Pei, B. Liu, Y. Wu et al., Fast lithium growth and short circuit induced by localized-temperature hotspots in lithium batteries. Nat. Commun. 10(1), 2067 (2019). https://doi.org/10.1038/s41467-019-09924-1
  174. A. Vizintin, J. Bitenc, A. Kopač Lautar, K. Pirnat, J. Grdadolnik et al., Probing electrochemical reactions in organic cathode materials via in operando infrared spectroscopy. Nat. Commun. 9(1), 661 (2018). https://doi.org/10.1038/s41467-018-03114-1
  175. D.M. Seo, S. Reininger, M. Kutcher, K. Redmond, W.B. Euler et al., Role of mixed solvation and ion pairing in the solution structure of lithium ion battery electrolytes. J. Phys. Chem. C 119(25), 14038–14046 (2015). https://doi.org/10.1021/acs.jpcc.5b03694
  176. G. Yang, I.N. Ivanov, R.E. Ruther, R.L. Sacci, V. Subjakova et al., Electrolyte solvation structure at solid-liquid interface probed by nanogap surface-enhanced Raman spectroscopy. ACS Nano 12(10), 10159–10170 (2018). https://doi.org/10.1021/acsnano.8b05038
  177. M.M. Amaral, C.G. Real, V.Y. Yukuhiro, G. Doubek, P.S. Fernandez et al., In situ and operando infrared spectroscopy of battery systems: Progress and opportunities. J. Energy Chem. 81, 472–491 (2023). https://doi.org/10.1016/j.jechem.2023.02.036
  178. J. Lim, K.-K. Lee, C. Liang, K.-H. Park, M. Kim et al., Two-dimensional infrared spectroscopy and molecular dynamics simulation studies of nonaqueous lithium ion battery electrolytes. J. Phys. Chem. B 123(31), 6651–6663 (2019). https://doi.org/10.1021/acs.jpcb.9b02026
  179. F. Geifes, C. Bolsinger, P. Mielcarek, K.P. Birke, Determination of the entropic heat coefficient in a simple electro-thermal lithium-ion cell model with pulse relaxation measurements and least squares algorithm. J. Power. Sources 419, 148–154 (2019). https://doi.org/10.1016/j.jpowsour.2019.02.072
  180. L. Meyer, D. Curran, R. Brow, S. Santhanagopalan, J. Porter, operando measurements of electrolyte Li-ion concentration during fast charging with FTIR/ATR. J. Electrochem. Soc. 168(9), 090502 (2021). https://doi.org/10.1149/1945-7111/ac1d7a
  181. J.-H. Tian, T. Jiang, M. Wang, Z. Hu, X. Zhu et al., In situ/operando spectroscopic characterizations guide the compositional and structural design of lithium–sulfur batteries. Small Meth. 4(6), 1900467 (2020). https://doi.org/10.1002/smtd.201900467
  182. X. Shan, Y. Zhong, L. Zhang, Y. Zhang, X. Xia et al., A brief review on solid electrolyte interphase composition characterization technology for lithium metal batteries: challenges and perspectives. J. Phys. Chem. C 125(35), 19060–19080 (2021). https://doi.org/10.1021/acs.jpcc.1c06277
  183. K.-K. Lee, K. Park, H. Lee, Y. Noh, D. Kossowska et al., Ultrafast fluxional exchange dynamics in electrolyte solvation sheath of lithium ion battery. Nat. Commun. 8, 14658 (2017). https://doi.org/10.1038/ncomms14658
  184. Q. He, B. Yu, Z. Li, Y. Zhao, Density functional theory for battery materials. Energy Environ. Mater. 2(4), 264–279 (2019). https://doi.org/10.1002/eem2.12056
  185. Y. Gao, K. Liu, C. Zhu, X. Zhang, D. Zhang, Co-estimation of state-of-charge and state-of- health for lithium-ion batteries using an enhanced electrochemical model. IEEE Trans. Ind. Electron. 69(3), 2684–2696 (2022). https://doi.org/10.1109/TIE.2021.3066946
  186. M. Dotoli, R. Rocca, M. Giuliano, G. Nicol, F. Parussa et al., A review of mechanical and chemical sensors for automotive Li-ion battery systems. Sensors 22(5), 1763 (2022). https://doi.org/10.3390/s22051763
  187. Y. Lu, S. Zhang, S. Dai, D. Liu, X. Wang et al., Ultrasensitive detection of electrolyte leakage from lithium-ion batteries by ionically conductive metal-organic frameworks. Matter 3(3), 904–919 (2020). https://doi.org/10.1016/j.matt.2020.05.021
  188. J. Wan, C. Liu, X. Wang, H. Wang, L. Tang et al., Conductometric sensor for ppb-level lithium-ion battery electrolyte leakage based on Co/Pd-doped SnO2. Sens. Actuat. B Chem. 393, 134326 (2023). https://doi.org/10.1016/j.snb.2023.134326
  189. X.-F. Zhang, Y. Zhao, H.-Y. Liu, T. Zhang, W.-M. Liu et al., Degradation of thin-film lithium batteries characterised by improved potentiometric measurement of entropy change. Phys. Chem. Chem. Phys. 20(16), 11378–11385 (2018). https://doi.org/10.1039/C7CP08588E
  190. X.-F. Zhang, Y. Zhao, Y. Patel, T. Zhang, W.-M. Liu et al., Potentiometric measurement of entropy change for lithium batteries. Phys. Chem. Chem. Phys. 19(15), 9833–9842 (2017). https://doi.org/10.1039/C6CP08505A
  191. Z. Lin, D. Wu, C. Du, Z. Ren, An improved potentiometric method for the measurement of entropy coefficient of lithium-ion battery based on positive adjustment. Energy Rep. 8, 54–63 (2022). https://doi.org/10.1016/j.egyr.2022.10.109
  192. J. Li, Y. Zhang, R. Shang, C. Cheng, Y. Cheng et al., Recent advances in lithium-ion battery separators with reversible/irreversible thermal shutdown capability. Energy Storage Mater. 43, 143–157 (2021). https://doi.org/10.1016/j.ensm.2021.08.046
  193. C. Thirstrup, L. Deleebeeck, Review on electrolytic conductivity sensors. IEEE Trans. Instrum. Meas. 70, 1008222 (2021). https://doi.org/10.1109/TIM.2021.3083562
  194. M. Xiao, S.-Y. Choe, Impedance model of lithium ion polymer battery considering temperature effects based on electrochemical principle: Part I for high frequency. J. Power. Sources 277, 403–415 (2015). https://doi.org/10.1016/j.jpowsour.2014.10.157
  195. U. Westerhoff, K. Kurbach, F. Lienesch, M. Kurrat, Analysis of lithium-ion battery models based on electrochemical impedance spectroscopy. Energy Technol. 4(12), 1620–1630 (2016). https://doi.org/10.1002/ente.201600154
  196. M. Gaberšček, Understanding Li-based battery materials via electrochemical impedance spectroscopy. Nat. Commun. 12(1), 6513 (2021). https://doi.org/10.1038/s41467-021-26894-5
  197. C. Rabissi, A. Innocenti, G. Sordi, A. Casalegno, A comprehensive physical-based sensitivity analysis of the electrochemical impedance response of lithium-ion batteries. Energy Technol. 9(3), 2000986 (2021). https://doi.org/10.1002/ente.202000986
  198. C.-Y. Lee, H.-C. Peng, S.-J. Lee, I.-M. Hung, C.-T. Hsieh et al., A flexible three-in-one microsensor for real-time monitoring of internal temperature, voltage and current of lithium batteries. Sensors 15(5), 11485–11498 (2015). https://doi.org/10.3390/s150511485
  199. Z. Wei, J. Zhao, H. He, G. Ding, H. Cui et al., Future smart battery and management: advanced sensing from external to embedded multi-dimensional measurement. J. Power. Sources 489, 229462 (2021). https://doi.org/10.1016/j.jpowsour.2021.229462
  200. X. Du, B. Yang, Y. Lu, X. Guo, G. Zu et al., Detection of electrolyte leakage from lithium-ion batteries using a miniaturized sensor based on functionalized double-walled carbon nanotubes. J. Mater. Chem. C 9(21), 6760–6765 (2021). https://doi.org/10.1039/d1tc01069g
  201. L. Li, Y. Chen, T. Sun, J. Zhang, C. Xiong et al., Ionic gel chemical sensors with damage tolerance for monitoring lithium-ion battery electrolyte leakage and battery safety. Adv. Funct. Mater. 35(1), 2407702 (2025). https://doi.org/10.1002/adfm.202407702
  202. G. Manfredini, A. Ria, P. Bruschi, L. Gerevini, M. Vitelli et al., An ASIC-based miniaturized system for online multi-measurand monitoring of lithium-ion batteries. Batteries 7(3), 45 (2021). https://doi.org/10.3390/batteries7030045
  203. S. Giazitzis, M. Sakwa, S. Leva, E. Ogliari, S. Badha et al., A case study of a tiny machine learning application for battery state-of-charge estimation. Electronics 13(10), 1964 (2024). https://doi.org/10.3390/electronics13101964
  204. T. Paljk, V. Bracamonte, T. Syrový, S.D. Talian, S. Hočevar et al., Integrated sensor printed on the separator enabling the detection of dissolved manganese ions in battery cell. Energy Storage Mater. 55, 55–63 (2023). https://doi.org/10.1016/j.ensm.2022.11.039
  205. J. Zikulnig, S. Chang, J. Bito, L. Rauter, A. Roshanghias et al., Printed electronics technologies for additive manufacturing of hybrid electronic sensor systems. Adv. Sens. Res. 2(7), 2200073 (2023). https://doi.org/10.1002/adsr.202200073
  206. M. Shikida, Y. Hasegawa, M.S. Al Farisi, M. Matsushima, T. Kawabe, Advancements in MEMS technology for medical applications: microneedles and miniaturized sensors. Jpn. J. Appl. Phys. 61, SA0803 (2022). https://doi.org/10.35848/1347-4065/ac305d
  207. J.M. Quero, F. Perdigones, C. Aracil, Microfabrication technologies used for creating smart devices for industrial applications. in Smart sensors and MEMs. (Elsevier, 2018). pp. 291–311. https://doi.org/10.1016/b978-0-08-102055-5.00011-5
  208. E. Pomerantseva, H. Jung, M. Gnerlich, S. Baron, K. Gerasopoulos et al., A MEMS platform for in situ, real-time monitoring of electrochemically induced mechanical changes in lithium-ion battery electrodes. J. Micromech. Microeng. 23(11), 114018 (2013). https://doi.org/10.1088/0960-1317/23/11/114018
  209. C.-Y. Lee, S.-J. Lee, Y.-M. Hung, C.-T. Hsieh, Y.-M. Chang et al., Integrated microsensor for real-time microscopic monitoring of local temperature, voltage and current inside lithium ion battery. Sens. Actuat. A Phys. 253, 59–68 (2017). https://doi.org/10.1016/j.sna.2016.10.011
  210. K. Tan, W. Li, Z. Lin, X. Han, X. Dai et al., operando monitoring of internal gas pressure in commercial lithium-ion batteries via a MEMS-assisted fiber-optic interferometer. J. Power. Sources 580, 233471 (2023). https://doi.org/10.1016/j.jpowsour.2023.233471
  211. O. Lupan, N. Magariu, D. Santos-Carballal, N. Ababii, J. Offermann et al., Development of 2-in-1 sensors for the safety assessment of lithium-ion batteries via early detection of vapors produced by electrolyte solvents. ACS Appl. Mater. Interfaces 15(22), 27340–27356 (2023). https://doi.org/10.1021/acsami.3c03564
  212. H. Zhu, Y. Li, W. Liu, L. Zhang, D. Sun et al., Amorphous bimetallic oxide CuSnO3 modified In2O3 for highly sensitive detection of thermal runaway marker 1, 2-dimethoxyethane in lithium batteries. Ceram. Int. 51(8), 9978–9986 (2025). https://doi.org/10.1016/j.ceramint.2024.12.430
  213. C. Lupan, A.K. Mishra, N. Wolff, J. Drewes, H. Krüger et al., Nanosensors based on a single ZnO: Eu nanowire for hydrogen gas sensing. ACS Appl. Mater. Interfaces 14(36), 41196–41207 (2022). https://doi.org/10.1021/acsami.2c10975
  214. M. Zhang, Z. He, W. Cheng, X. Li, X. Zan et al., A room-temperature MEMS hydrogen sensor for lithium ion battery gas detecting based on Pt-modified Nb doped TiO2 nanosheets. Int. J. Hydrog. Energy 74, 307–315 (2024). https://doi.org/10.1016/j.ijhydene.2024.05.388
  215. I. Pandey, J.D. Tiwari, Advance sensor for monitoring electrolyte leakage in lithium-ion batteries for electric vehicles. In 2022 IEEE 9th Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON). December 2–4, 2022, Prayagraj, India. (IEEE, 2022), pp. 1–5
  216. S. Zhang, Y. Lu, L. Li, X. Wang, D. Liu et al., Sensitive sensors based on bilayer organic field-effect transistors for detecting lithium-ion battery electrolyte leakage. Sci. China Mater. 65(5), 1187–1194 (2022). https://doi.org/10.1007/s40843-021-1903-5
  217. Y. Liu, Q. Zhou, G. Cui, Machine learning boosting the development of advanced lithium batteries. Small Meth. 5(8), 2100442 (2021). https://doi.org/10.1002/smtd.202100442
  218. O. Surucu, S.A. Gadsden, J. Yawney, Condition monitoring using machine learning: a review of theory, applications, and recent advances. Expert Syst. Appl. 221, 119738 (2023). https://doi.org/10.1016/j.eswa.2023.119738
  219. J. Schmidt, M.R.G. Marques, S. Botti, M.A.L. Marques, Recent advances and applications of machine learning in solid-state materials science. NPJ Comput. Mater. 5, 83 (2019). https://doi.org/10.1038/s41524-019-0221-0
  220. A. ZhuParris, A.A. de Goede, I.E. Yocarini, W. Kraaij, G.J. Groeneveld et al., Machine learning techniques for developing remotely monitored central nervous system biomarkers using wearable sensors: a narrative literature review. Sensors 23(11), 5243 (2023). https://doi.org/10.3390/s23115243
  221. M. Ma, X. Li, W. Gao, J. Sun, Q. Wang et al., Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA. Appl. Energy 324, 119678 (2022). https://doi.org/10.1016/j.apenergy.2022.119678
  222. S. Jin, X. Sui, X. Huang, S. Wang, R. Teodorescu et al., Overview of machine learning methods for lithium-ion battery remaining useful lifetime prediction. Electronics 10(24), 3126 (2021). https://doi.org/10.3390/electronics10243126
  223. Y. Cai, W. Li, T. Zahid, C. Zheng, Q. Zhang et al., Early prediction of remaining useful life for lithium-ion batteries based on CEEMDAN-transformer-DNN hybrid model. Heliyon 9(7), e17754 (2023). https://doi.org/10.1016/j.heliyon.2023.e17754
  224. Y. Han, C. Li, L. Zheng, G. Lei, L. Li, Remaining useful life prediction of lithium-ion batteries by using a denoising transformer-based neural network. Energies 16(17), 6328 (2023). https://doi.org/10.3390/en16176328
  225. C. Lv, X. Zhou, L. Zhong, C. Yan, M. Srinivasan et al., Machine learning: an advanced platform for materials development and state prediction in lithium-ion batteries. Adv. Mater. 34(25), 2101474 (2022). https://doi.org/10.1002/adma.202101474
  226. Y. Khawaja, N. Shankar, I. Qiqieh, J. Alzubi, O. Alzubi et al., Battery management solutions for li-ion batteries based on artificial intelligence. Ain Shams Eng. J. 14(12), 102213 (2023). https://doi.org/10.1016/j.asej.2023.102213
  227. M.A. Hannan, D.N.T. How, M.S.H. Lipu, M. Mansor, P.J. Ker et al., Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model. Sci. Rep. 11(1), 19541 (2021). https://doi.org/10.1038/s41598-021-98915-8
  228. R. Navega Vieira, J.M. Mauricio Villanueva, T.K. Sales Flores, E.C. Tavares de Macêdo, State of charge estimation of battery based on neural networks and adaptive strategies with correntropy. Sensors 22(3), 1179 (2022). https://doi.org/10.3390/s22031179
  229. F. Wang, Z. Zhai, Z. Zhao, Y. Di, X. Chen, Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nat. Commun. 15(1), 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z
  230. L. Yao, Z. Fang, Y. Xiao, J. Hou, Z. Fu, An intelligent fault diagnosis method for lithium battery systems based on grid search support vector machine. Energy 214, 118866 (2021). https://doi.org/10.1016/j.energy.2020.118866
  231. A. Samanta, S. Chowdhuri, S.S. Williamson, Machine learning-based data-driven fault detection/diagnosis of lithium-ion battery: a critical review. Electronics 10(11), 1309 (2021). https://doi.org/10.3390/electronics10111309
  232. S. Khaleghi, M.S. Hosen, D. Karimi, H. Behi, S.H. Beheshti et al., Developing an online data-driven approach for prognostics and health management of lithium-ion batteries. Appl. Energy 308, 118348 (2022). https://doi.org/10.1016/j.apenergy.2021.118348
  233. K. Park, Y. Choi, W.J. Choi, H.-Y. Ryu, H. Kim, LSTM-based battery remaining useful life prediction with multi-channel charging profiles. IEEE Access 8, 20786–20798 (2020). https://doi.org/10.1109/ACCESS.2020.2968939
  234. H. Li, J. Huang, W. Ji, Z. He, J. Cheng et al., Predicting capacity fading behaviors of lithium ion batteries: an electrochemical protocol-integrated digital-twin solution. J. Electrochem. Soc. 169(10), 100504 (2022). https://doi.org/10.1149/1945-7111/ac95d2
  235. K.A. Severson, P.M. Attia, N. Jin, N. Perkins, B. Jiang et al., Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4(5), 383–391 (2019). https://doi.org/10.1038/s41560-019-0356-8
  236. L. Su, M. Wu, Z. Li, J. Zhang, Cycle life prediction of lithium-ion batteries based on data-driven methods. eTransportation 10, 100137 (2021). https://doi.org/10.1016/j.etran.2021.100137
  237. J. Tian, R. Xiong, W. Shen, J. Lu, Data-driven battery degradation prediction: forecasting voltage-capacity curves using one-cycle data. EcoMat 4(5), e12213 (2022). https://doi.org/10.1002/eom2.12213
  238. H. Zhang, Y. Su, F. Altaf, T. Wik, S. Gros, Interpretable battery cycle life range prediction using early cell degradation data. IEEE Trans. Transp. Electrif. 9(2), 2669–2682 (2023). https://doi.org/10.1109/TTE.2022.3226683
  239. M. Iftikhar, M. Shoaib, A. Altaf, F. Iqbal, S.G. Villar et al., A deep learning approach to optimize remaining useful life prediction for Li-ion batteries. Sci. Rep. 14(1), 25838 (2024). https://doi.org/10.1038/s41598-024-77427-1
  240. S. Saxena, L. Ward, J. Kubal, W. Lu, S. Babinec et al., A convolutional neural network model for battery capacity fade curve prediction using early life data. J. Power. Sources 542, 231736 (2022). https://doi.org/10.1016/j.jpowsour.2022.231736
  241. H. Zhang, Y. Li, S. Zheng, Z. Lu, X. Gui et al., Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning. Nat. Mach. Intell. 7(2), 270–277 (2025). https://doi.org/10.1038/s42256-024-00972-x
  242. S. Buchanan, C. Crawford, Probabilistic lithium-ion battery state-of-health prediction using convolutional neural networks and Gaussian process regression. J. Energy Storage 76, 109799 (2024). https://doi.org/10.1016/j.est.2023.109799
  243. S. Mao, X. Han, Y. Lu, D. Wang, A. Su et al., Multi sensor fusion methods for state of charge estimation of smart lithium-ion batteries. J. Energy Storage 72, 108736 (2023). https://doi.org/10.1016/j.est.2023.108736
  244. Y. Zheng, Z. Zhang, S. Zhou, X. Zhou, Q. Li et al., Innovative fault diagnosis and early warning method based on multifeature fusion model for electric vehicles. J. Energy Storage 78, 109681 (2024). https://doi.org/10.1016/j.est.2023.109681
  245. Z. Zhang, R. Cao, Y. Jin, J. Lin, Y. Zheng et al., Battery leakage fault diagnosis based on multi-modality multi-classifier fusion decision algorithm. J. Energy Storage 72, 108741 (2023). https://doi.org/10.1016/j.est.2023.108741
  246. Z. Xie, Y. Zhang, H. Wang, P. Li, J. Shi et al., The multi-parameter fusion early warning method for lithium battery thermal runaway based on cloud model and dempster–shafer evidence theory. Batteries 10(9), 325 (2024). https://doi.org/10.3390/batteries10090325
  247. V. Sulzer, P. Mohtat, A. Aitio, S. Lee, Y.T. Yeh et al., The challenge and opportunity of battery lifetime prediction from field data. Joule 5(8), 1934–1955 (2021). https://doi.org/10.1016/j.joule.2021.06.005
  248. S.K. Mulpuri, B. Sah, P. Kumar, An intelligent battery management system (BMS) with end-edge-cloud connectivity–a perspective. Sustain. Energy Fuels 9(5), 1142–1159 (2025). https://doi.org/10.1039/D4SE01238K
  249. M. Cavus, D. Dissanayake, M. Bell, Next generation of electric vehicles: AI-driven approaches for predictive maintenance and battery management. Energies 18(5), 1041 (2025). https://doi.org/10.3390/en18051041
  250. D.N.T. How, M.A. Hannan, M.S. HossainLipu, P.J. Ker, State of charge estimation for lithium-ion batteries using model-based and data-driven methods: a review. IEEE Access 7, 136116–136136 (2019). https://doi.org/10.1109/ACCESS.2019.2942213
  251. Y. Li, C. Zou, M. Berecibar, E. Nanini-Maury, J.C.W. Chan et al., Random forest regression for online capacity estimation of lithium-ion batteries. Appl. Energy 232, 197–210 (2018). https://doi.org/10.1016/j.apenergy.2018.09.182
  252. T. Lombardo, M. Duquesnoy, H. El-Bouysidy, F. Årén, A. Gallo-Bueno et al., Artificial intelligence applied to battery research: hype or reality? Chem. Rev. 122(12), 10899–10969 (2022). https://doi.org/10.1021/acs.chemrev.1c00108
  253. Y. Liu, B. Guo, X. Zou, Y. Li, S. Shi, Machine learning assisted materials design and discovery for rechargeable batteries. Energy Storage Mater. 31, 434–450 (2020). https://doi.org/10.1016/j.ensm.2020.06.033
  254. R. Lajara, J.J. Pérez-Solano, J. Pelegrí-Sebastiá, Predicting the batteries’ state of health in wireless sensor networks applications. IEEE Trans. Ind. Electron. 65(11), 8936–8945 (2018). https://doi.org/10.1109/TIE.2018.2808925
  255. A.R. Pinto, C. Montez, G. Araújo, F. Vasques, P. Portugal, An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms. Inf. Fusion 15, 90–101 (2014). https://doi.org/10.1016/j.inffus.2013.05.003
  256. G. Krishna, R. Singh, A. Gehlot, S.V. Akram, N. Priyadarshi et al., Digital technology implementation in battery-management systems for sustainable energy storage: review, challenges, and recommendations. Electronics 11(17), 2695 (2022). https://doi.org/10.3390/electronics11172695
  257. M. Schneider, S. Ilgin, N. Jegenhorst, R. Kube, S. Püttjer et al., Automotive battery monitoring by wireless cell sensors. in 2012 IEEE international instrumentation and measurement technology conference proceedings. May 13-16, 2012, Graz, Austria (IEEE, 2012), pp 816–820
  258. B.C. Florea, D.D. Taralunga, Blockchain IoT for smart electric vehicles battery management. Sustainability 12(10), 3984 (2020). https://doi.org/10.3390/su12103984
  259. J. Yan, M. Zhou, Z. Ding, Recent advances in energy-efficient routing protocols for wireless sensor networks: a review. IEEE Access 4, 5673–5686 (2016). https://doi.org/10.1109/ACCESS.2016.2598719
  260. O.O. Ogundile, A.S. Alfa, A survey on an energy-efficient and energy-balanced routing protocol for wireless sensor networks. Sensors 17(5), 1084 (2017). https://doi.org/10.3390/s17051084
  261. J. Chen, M. Manivanan, J. Duque, P. Kollmeyer, S. Panchal et al., A convolutional neural network for estimation of lithium-ion battery state-of-health during constant current operation. in 2023 IEEE transportation electrification conference and expo (ITEC). June 21-23, 2023, Detroit, MI, USA (IEEE, 2023), pp. 1–6
  262. H. Mostafaei, Energy-efficient algorithm for reliable routing of wireless sensor networks. IEEE Trans. Ind. Electron. 66(7), 5567–5575 (2019). https://doi.org/10.1109/TIE.2018.2869345
  263. D. Kandris, E. Anastasiadis, Advanced wireless sensor networks: applications, challenges and research trends. Electronics 13(12), 2268 (2024). https://doi.org/10.3390/electronics13122268
  264. H. Hu, X. Fan, C. Wang, Energy efficient clustering and routing protocol based on quantum p swarm optimization and fuzzy logic for wireless sensor networks. Sci. Rep. 14(1), 18595 (2024). https://doi.org/10.1038/s41598-024-69360-0
  265. O. Ali, M.K. Ishak, A.B. Ahmed, M.F.M. Salleh, C.A. Ooi et al., On-line WSN SoC estimation using Gaussian process regression: an adaptive machine learning approach. Alex. Eng. J. 61(12), 9831–9848 (2022). https://doi.org/10.1016/j.aej.2022.02.067
  266. P. Nagrale, Global battery monitoring systems market: size, growth report- global forecast till 2030, market research future (2020), https://www.marketresearchfuture.com/reports/battery-monitoring-system-market-6985 (June, 2023)
  267. Y. Hua, A. Cordoba-Arenas, N. Warner, G. Rizzoni, A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control. J. Power. Sources 280, 293–312 (2015). https://doi.org/10.1016/j.jpowsour.2015.01.112
  268. M.A. Hannan, M.S.H. Lipu, A. Hussain, A. Mohamed, A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations. Renew. Sustain. Energy Rev. 78, 834–854 (2017). https://doi.org/10.1016/j.rser.2017.05.001
  269. J.H. Kim, Grand challenges and opportunities in batteries and electrochemistry. Front. Batter. Electrochem. 1, 1066276 (2022). https://doi.org/10.3389/fbael.2022.1066276
  270. J. Mao, J. Miao, Y. Lu, Z. Tong, Machine learning of materials design and state prediction for lithium ion batteries. Chin. J. Chem. Eng. 37, 1–11 (2021). https://doi.org/10.1016/j.cjche.2021.04.009
  271. C. An, K. Zheng, S. Wang, T. Wang, H. Liu et al., Advances in sensing technologies for monitoring states of lithium-ion batteries. J. Power. Sources 625, 235633 (2025). https://doi.org/10.1016/j.jpowsour.2024.235633
  272. K. Gulati, R.S. Kumar Boddu, D. Kapila, S.L. Bangare, N. Chandnani et al., A review paper on wireless sensor network techniques in internet of things (IoT). Mater. Today Proc. 51, 161–165 (2022). https://doi.org/10.1016/j.matpr.2021.05.067
  273. I. González, A.J. Calderón, F.J. Folgado, IoT real time system for monitoring lithium-ion battery long-term operation in microgrids. J. Energy Storage 51, 104596 (2022). https://doi.org/10.1016/j.est.2022.104596
  274. C. Li, S. Cong, Z. Tian, Y. Song, L. Yu et al., Flexible perovskite solar cell-driven photo-rechargeable lithium-ion capacitor for self-powered wearable strain sensors. Nano Energy 60, 247–256 (2019). https://doi.org/10.1016/j.nanoen.2019.03.061
  275. G. Bree, H. Hao, Z. Stoeva, C.T. JohnLow, Monitoring state of charge and volume expansion in lithium-ion batteries: an approach using surface mounted thin-film graphene sensors. RSC Adv. 13(10), 7045–7054 (2023). https://doi.org/10.1039/D2RA07572E
  276. I.V. Zaporotskova, N.P. Boroznina, Y.N. Parkhomenko, L.V. Kozhitov, Carbon nanotubes: sensor properties. A review. Mod. Electron. Mater. 2(4), 95–105 (2016). https://doi.org/10.1016/j.moem.2017.02.002
  277. N. Goel, R. Kumar, Physics of 2D materials for developing smart devices. Nano-Micro Lett. 17(1), 197 (2025). https://doi.org/10.1007/s40820-024-01635-7
  278. Z. Li, F. Sun, L. Rose, G. Nagesh, N.K. Shekar et al., Multilayered single-walled carbon nanotube-based flexible temperature
Read More
Author biographies are not available.
Download this PDF file
PDF
Statistic
Read Counter : 795 Download : 602