Sensors Innovations for Smart Lithium-Based Batteries: Advancements, Opportunities, and Potential Challenges
Corresponding Author: Shu Zhang
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
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- A. Van der Ven, Lithium diffusion in layered LixCoO2. Electrochem. Solid-State Lett. 3(7), 301 (1999). https://doi.org/10.1149/1.1391130
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- C. Thirstrup, L. Deleebeeck, Review on electrolytic conductivity sensors. IEEE Trans. Instrum. Meas. 70, 1008222 (2021). https://doi.org/10.1109/TIM.2021.3083562
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- D. Kandris, E. Anastasiadis, Advanced wireless sensor networks: applications, challenges and research trends. Electronics 13(12), 2268 (2024). https://doi.org/10.3390/electronics13122268
- 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
- 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
- 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)
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Z. Li, F. Sun, L. Rose, G. Nagesh, N.K. Shekar et al., Multilayered single-walled carbon nanotube-based flexible temperature
References
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
A. Van der Ven, Lithium diffusion in layered LixCoO2. Electrochem. Solid-State Lett. 3(7), 301 (1999). https://doi.org/10.1149/1.1391130
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
C. Thirstrup, L. Deleebeeck, Review on electrolytic conductivity sensors. IEEE Trans. Instrum. Meas. 70, 1008222 (2021). https://doi.org/10.1109/TIM.2021.3083562
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
D. Kandris, E. Anastasiadis, Advanced wireless sensor networks: applications, challenges and research trends. Electronics 13(12), 2268 (2024). https://doi.org/10.3390/electronics13122268
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
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
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)
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
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
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
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
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
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
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
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
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
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
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
Z. Li, F. Sun, L. Rose, G. Nagesh, N.K. Shekar et al., Multilayered single-walled carbon nanotube-based flexible temperature