Functional Optical Fiber Sensors Detecting Imperceptible Physical/Chemical Changes for Smart Batteries

doi:10.1007/s40820-024-01374-9

Abstract

Abstract:

The battery technology progress has been a contradictory process in which performance improvement and hidden risks coexist. Now the battery is still a “black box”, thus requiring a deep understanding of its internal state. The battery should “sense its internal physical/chemical conditions”, which puts strict requirements on embedded sensing parts. This paper summarizes the application of advanced optical fiber sensors in lithium-ion batteries and energy storage technologies that may be mass deployed, focuses on the insights of advanced optical fiber sensors into the processes of one-dimensional nano-micro-level battery material structural phase transition, electrolyte degradation, electrode-electrolyte interface dynamics to three-dimensional macro-safety evolution. The paper contributes to understanding how to use optical fiber sensors to achieve “real” and “embedded” monitoring. Through the inherent advantages of the advanced optical fiber sensor, it helps clarify the battery internal state and reaction mechanism, aiding in the establishment of more detailed models. These advancements can promote the development of smart batteries, with significant importance lying in essentially promoting the improvement of system consistency. Furthermore, with the help of smart batteries in the future, the importance of consistency can be weakened or even eliminated. The application of advanced optical fiber sensors helps comprehensively improve the battery quality, reliability, and life.

Key words: Smart battery, Advanced embedded optical fiber sensor, Battery internal physical/chemical state, Quality-reliability-life characteristic

Yiding Li, Li Wang, Youzhi Song, Wenwei Wang, Cheng Lin, Xiangming He. Functional Optical Fiber Sensors Detecting Imperceptible Physical/Chemical Changes for Smart Batteries[J]. Nano-Micro Letters, 2024, 16(1): 154-.

Yiding Li, Li Wang, Youzhi Song, Wenwei Wang, Cheng Lin, Xiangming He. [J]. Nano-Micro Letters, 2024, 16(1): 154-.

/ / Recommend / Download Citations

URL: https://www.qk.sjtu.edu.cn/nml/EN/10.1007/s40820-024-01374-9

https://www.qk.sjtu.edu.cn/nml/EN/Y2024/V16/I1/154

Figures/Tables 17

Fig. 1 Promising optical fiber sensors in battery applications

Fig. 2 Application of FBG sensors to measure the cell strain-temperature at the laboratory level and before industrialization. a FBG sensor structure and action mechanism. When the battery is loaded with temperature or strain, the FBG grid spacing changes, and the central wavelength of reflected light shifts. The central wavelength shift is linearly related to the temperature and strain. b The evolution of FBG in battery applications. From single point to multipoint, from external to internal, and from single battery to module even system. c Bidirectional strain measurement of an FBG sensor in a single cell. Reproduced with permission from Ref. [56]. Copyright 2018 MDPI. d Embedded FBG sensor core temperature measurement. Reproduced with permission from Ref. [35]. Copyright 2018 Elsevier. e Embedding of an FBG in a large pouch battery. Reproduced with permission from Ref. [55]. Copyright 2017 Elsevier. f Traction battery pack with an FBG embedded in the core. Reproduced with permission from Ref. [35]. Copyright 2017 Elsevier. g The process of embedding optical fibers in batteries involves the steps of implantation position determination, stacking fixation, encapsulation sealing, and testing in sequence. Reproduced with permission from Ref. [35]. Copyright 2017 Elsevier

Fig. 3 TFBG monitoring of the electrolyte environment. a Working mechanism and application scenario of the TFBG in batteries. When the incident light enters the fiber and reaches the grating, the light meeting the Bragg condition will undergo Bragg reflection, while the light meeting the conditions of a certain order cladding radiation mode will be coupled into the cladding and exchange energy with the external environment, realizing monitoring of the surrounding electrolyte environment. Reproduced with permission from Ref. [49]. Copyright 2021 Royal Society of Chemistry. b The evolution of electrolyte refractive index with battery health state in TFBG Wavelength. The suspended dead lithium, gas production, and degradation of the electrolyte’s performance caused by battery aging can all lead to significant changes in the comprehensive refractive index of the electrolyte detected by TFBG, which is reflected in the mode conversion of various modes in TFBG, including the center position of the mode peak and the power of the peak. Reproduced with permission from Ref. [49]. Copyright 2021 Royal Society of Chemistry

Fig. 4 FOEW sensor and SPR sensor for battery surface monitoring. a FOEW sensor: The evanescent wave effect occurs when a light wave is incident from a dense medium to a light sparse medium, and total reflection occurs. The evanescent wave is a surface wave. Its amplitude exponentially decreases with increasing depth perpendicular to the interface, and the phase changes with the tangential direction. Reproduced with permission from Ref. [70]. Copyright 2017 ACS Publications. Ref. [71]. Copyright 2022 ACS Publications. b The loss of reversible lithium leads to changes in the detectable optical properties of FOEW. The optical transmissivity of the evanescent wave is related to the lithium-ion concentration on the surface of the graphite particles, when the capacity of the lithium-ion battery declines after long cycling due to the reduction of recyclable graphite lithium content, the FOEW transmission amplitude decreases. Reproduced with permission from Ref. [42]. Copyright 2020 Elsevier. c SPR sensor: Total internal reflection of incident light occurs at the interface between an optical fiber and a metal film, and the evanescent wave causes regular oscillation of metal surface electrons at the interface between the metal film and medium, which excites a surface plasmon wave. When the incidence angle or wavelength is a certain value, the wave vectors of the surface plasmon wave and evanescent wave are equal along the interface between the metal film and medium such that the wave vectors match, and the two resonate. The incident light is coupled with the surface plasmon wave through the evanescent wave, and the energy is strongly absorbed by the metal surface electrons, which makes the reflected light energy sharply drop and generates the surface plasmon resonance phenomenon. Reproduced with permission from Ref. [44]. Copyright 2022 MDPI. d The changes in ion concentration and state on the electrode surface affect the SPR waveform. The SPR mode is closely related to the electrode surface polarity, and as the potential gradually becomes positive, the SPR wave becomes deeper. At the same time, the core mode is not related to the polarity, and research shows that it is only related to temperature and only changes with temperature. Reproduced with permission from Ref. [45]. Copyright 2018 Springer Nature

Fig. 5 Distributed optical fiber sensing system based on the Rayleigh scattering principle. a Measurement mechanisms for different distributed optical fibers. When the particle size is much smaller than the wavelength of the incident light (less than one tenth of the wavelength), the scattered light intensities in each direction are different, and the intensity is inversely proportional to the fourth power of the wavelength of the incident light. This phenomenon is called Rayleigh scattering. Reproduced with permission from Ref. [39]. Copyright 2021 IOP Publishing. Reproduced with permission from Ref. [51]. Copyright 2019 AIP Publishing. b Comparison between distributed temperature measurement based on Rayleigh scattering effect and FBG quasi-distributed temperature measurement. The spectrum of temperature measurement based on Rayleigh scattering effect in time and space is more intuitive than that of FBG measurement, and the identification of temperature hot spots is more accurate. Reproduced with permission from Ref. [39]. Copyright 2021 IOP Publishing

Table 1 Characteristics and application scenarios of different types of sensors

Working mechanism	Sensor	Characteristic	Application scenario	Note	Refs.
Optical structural component	FBG	Simple structure Good economy Easy identification of measurement parameters High linearity	Macro-temperature and strain measurement	Installed inside or outside the battery	[36, 62]
Optical structural component	TFBG	No cross-sensitivity of temperature to refractive index measurement	Measurement of composite refractive index of electrolyte	Embedded installation inside the battery	[49]
Optical functional Component	FOEW	The change of electrolyte salt concentration is not related to the observation of transmissivity index	Transmission measurement of electrode-optical fiber interface	Close to the electrode surface	[71]
	TFBG-SPR	Integrated with TFBG can realize multifunctional applications	Measurement of charge density and ion distribution concentration on electrode surface	Close to the electrode surface	[73]
	Rayleigh Scattering sensor	Distributed measurement	Distributed observation of cell temperature and strain on optical fiber path	Installed inside or outside the battery	[83, 85]

Fig. 6 Observation of the “breathing” effect of the battery by an advanced optical fiber sensor. The fiber has different wavelength response behaviors at different interface positions, and the bidirectional stress inside the electrode causes splitting of the fiber wave peak, indicating anisotropy of the electrode active material. Reproduced with permission from Ref. [99]. Copyright 2022 Springer Nature

Fig. 7 New understanding of the electrochemical reaction through advanced optical fiber sensors. a An improved microstructure optical fiber was used to understand the electrochemical reaction state of the battery by observing and calculating the heat release, chemical reaction at the electrode-electrolyte interface and reactivity of electrolyte additives. Reproduced with permission from Ref. [36]. Copyright 2020 Spring Nature. b Understanding the relationship between the redox of Fe in an LFP-based battery and the strength of the optical fiber output, and understanding the material reaction process. Reproduced with permission from Ref. [41]. Copyright 2021American Chemical Society

Fig. 8 An advanced optical fiber sensor provides insight into the internal environment of an electrolyte. a Factors that cause deterioration of the internal environment of the battery electrolyte during the entire life. Reproduced with permission from Ref. [52]. Copyright 2022 Springer Nature. b Effect of different electrolyte concentrations on the light intensity in the SPR mode and core mode. Reproduced with permission from Ref. [45]. Copyright 2018 Springer Nature. c Overall effect of the salt concentration on the light intensity. Reproduced with permission from Ref. [49]. Copyright 2021 Royal Society of Chemistry

Fig. 9 An advanced optical fiber sensor exhibits a timely response to dangerous battery thermal runaway. a Reaction sequence of components and materials in battery thermal runaway. b New multifunctional optical fiber sensor for monitoring the thermal runaway of commercial lithium-ion batteries. Reproduced with permission from Ref. [62]. Copyright 2022 Spring Nature. c SLIB equipped with an FBG sensor gives an early warning of extreme abuse conditions. Reproduced with permission from Ref. [114]. Copyright 2022 Elsevier

Table 2 Summary of the usage effects that different fiber optic sensors can achieve

Domain feature	Sensor	Monitoring Process	Principle	Function	Refs.
Element	FOEW	Observing the oxidation-reduction of elements	Change in light signal intensity	Understanding the redox process of elements and its accompanying processes	[41]
Interface	SPR	Observing the particle concentration at the interface	Changes in light intensity of SPR mode	Understanding interface electrical properties	[45]
Domain	TFBG	Observing the turbidity level of electrolyte	Changes in light intensity of guided cladding mode	Understanding the evolution of electrolyte domain performance	[49]
Macro: Point to Line (Adding a time dimension can transform single measurement point information into line information)	FBG	Observing the transverse and longitudinal bidirectional stress inside the battery; Observing oxidation-reduction temperature rise	FBG reflection peak splitting and displacement	Understanding the characteristics of charge and matter transport	[99]
Macro: Line to Surface (Adding a time dimension can transform the information along the optical fiber into surface information)	Rayleigh Scattering sensor	Observing global force and heat	Rayleigh scattering spectral shift	Understanding global force and thermal evolution	[85]

Fig. 10 Advanced optical fiber sensors have great application prospects in various types of batteries. a In the Li-S battery, the optical fiber sensor identifies the key phase change process of the electrode. Reproduced with permission from Ref. [38]. Copyright 2022 Royal Society of Chemistry. b The optical fiber sensor monitors the entire process of the Si-based electrode from complete structure to particle damage. Reproduced with permission from Ref. [99]. Copyright 2022 Springer Nature. c The oxygen partial pressure in the Li-air battery affects the light intensity, which can be used to monitor the oxygen concentration. Reproduced with permission from Ref. [138]. Copyright 2019 The Japan Society of Mechanical Engineers. d The application of advanced optical fiber sensor in Na-ion battery. When the electric signal is applied, the optical signal drops rapidly, indicating the occurrence of sodium precipitation. Reproduced with permission from Ref. [71]. Copyright 2022 American Chemical Society. e FBG sensors at different locations found “breathing” and spectral peak splitting effects caused by solid-state batteries under pressure. Reproduced with permission from Ref. [99]. Copyright 2022 Springer Nature

Table 3 Application value of fiber optic sensors in different battery research

Battery type	Embedding position	Function	Refs.
Coin battery	Symmetrically embedded in the middle of the cell	Traverse the battery, can be installed at any position; especially suitable for embedding at the most active electrochemical location	[99, 139]
Swagelok battery	Symmetrically embedded in the middle of the electrode	Achieves higher detection accuracy than pouch batteries after embedding	[68, 70, 99]
Three-electrode battery	Between electrode sheets, near the reference electrode	Cross-confirmation of optical and electrical signals; excellent binding performance	[71]
Pouch battery	Between electrode sheets	Can be embedded at any position, enabling measurement of battery non-uniformity; features a one-to-many characteristic with strong scalability	[35, 41]
Cylindrical battery	In the mandrel	Directly embedded in the battery core, avoids delayed judgment of internal temperature due to low radial thermal conductivity	[36, 39, 49]
Hard shell battery	Between electrode sheets		[140]
Solid state battery	Inside the active material	Able to detect the expansion issues of solid-state electrodes that conventional external sensors cannot detect	[99]
Wearable batter	Can be used as a battery matrix; can also be wound around the battery	Flexible mechanical properties and minimal radial size, can flex freely within flexible batteries, can be used as the base for supporting batteries	-

Fig. 11 Future application prospects of advanced PCF optical fiber sensors. a PCF structure, left: solid core, right: hollow core. b The future application of different types of PCF in energy storage devices such as batteries will achieve comprehensive monitoring of the mechanical, electrical, thermal, and pressure of energy devices. Upper left: The combination of the PCF structure and the SPR principle, a temperature sensor is constructed using surface plasmon resonance liquid crystal to measure the core temperature of the device; Lower left: The combination of PCF and FBG, a high birefringence microstructure is fabricated using FBG to form a pressure sensor, enabling the measurement of the device internal core pressure; Upper right: The PCF hollow is filled with magnetic fluid to form a magnetic field-temperature composite sensor. By utilizing the sensitivity of magnetic fluid to magnetic field and temperature, the magnetic field and temperature are decoupled through structural design to achieve monitoring of the internal magnetic field and temperature of the energy device; Lower right: The combination of PCF and the measured liquid forms a refractive index sensor. The measured liquid is filled in the PCF hollow through capillary effect, and sensitive spectral changes are utilized to monitor the refractive index of the measured object

Table 4 Summary of multipurpose sensing for PCF and advanced optical fiber sensor

Structure	Parameter	Sensitivity	Refs.
PCF + SMF Tip interferometer by PCF spliced with SMF	Temperature	10 pm/°C	[148]
PCF + FP + SMF PCF-based Fabry-Perot interferometer, which includes an inline micro-cavity and is spliced with SMF	Temperature	12 pm/°C	[149]
PCF + SPR Plasma resonance-based gold nanowire liquid crystal PCF	Temperature	10 nm/°C	[150]
PCF + Isopropanol Isopropanol filled PCF long period grating	Temperature	1.356 nm/°C	[151]
PCF + long period gratings The combination of periodically tapered long period grating and PCF	Pressure/Strain	11.2 pm/bar/ -7.6 pm/με	[152, 153]
PCF + FBG High birefringence microstructure fiber based on Bragg grating	Pressure	33 pm/Mpa	[154]
PCF + Fe₃O₄ Fe₃O₄ nanofluid filling	Electromagnetic field	242 pm/mT	[147]
PCF + FP + CdFe₂O₄ CdFe₂O₄ as a magnetic fluid	Electromagnetic field	33 pm/Oe	[155]
PCF + SMF + Water-based ferromagnetic fluid Water-based ferromagnetic fluid EMG507	Electromagnetic field	16.04 pm/G	[156]
PCF + SPR Gold layer filled SPR	Electromagnetic field	−	[157]
Multi core flat optical fiber based on SPR	Refractive index	23,000 nm/RIU	[158]
PCF + Long Period Grating Surface long-period gratings incorporated D-shaped PCF	Refractive index	585.3 nm/RIU	[159]
PCF + filled analyte under test	Refractive index	2 × 10⁻⁶ RIU	[160]

Fig. 12 An electrode was damaged due to improper mechanical behavior of the optical fiber inside the battery. a The electrode surface is scratched due to mechanical vibration. Reproduced with permission from Ref. [139]. Copyright 2017 MDPI. b Material damage is caused by mechanical displacement. Reproduced with permission from Ref.[139]. Copyright 2017 MDPI. c The change in the electrode structure due to the optical fiber sensor leads to obstruction of material diffusion and material deposition. Reproduced with permission from Ref. [44]. Copyright 2022 MDPI

Fig. 13 Advanced optical fiber sensors and microcontrollers in the future will help batteries be more powerful, safe, and intelligent. a The sensor and controller enable the battery to have self-sensing and self-control abilities. b In the future, it is possible to significantly promote system performance through embedded optical fiber sensor-smart batteries by enhancing the mechanical consistency. c Advanced smart batteries in the future will promote the progress of battery systems toward refined management. Reproduced with permission from Ref. [3]. Copyright 2020, published by EU

References 179

1.	C. Semeraro, M. Caggiano, A.-G. Olabi, M. Dassisti, Battery monitoring and prognostics optimization techniques: challenges and opportunities. Energy 255, 124538 (2022). https://doi.org/10.1016/j.energy.2022.124538
2.	K. Liu, Y.Y. Liu, D.C. Lin, A. Pei, Y. Cui, Materials for lithium-ion battery safety. Sci. Adv. 4, eaas9820 (2018). https://doi.org/10.1126/sciadv.aas9820
3.	K. Edstrom, R. Dominko, M. Fichtner, S. Perraud, J.M. Tarascon et al., Battery 2030+ Roadmap—Second Draft, (2020). https://doi.org/10.33063/diva2-1452023
4.	S. Dol, R. Bhinge, SMART motor for industry 4.0. IEEMA IEEE Engineer Infinite Conference. 1-6 (2018). https://ieeexplore.ieee.org/document/8385291 URL
5.	J. Jayakumar, B. Nagaraj, S. Chacko, P. Ajay, Conceptual implementation of artificial intelligent based E-mobility controller in smart city environment. Wirel. Commun. Mob. Comput. 2021, 5325116 (2021). https://doi.org/10.1155/2021/5325116
6.	S. Ci, N. Lin, D. Wu, Reconfigurable battery techniques and systems: a survey. IEEE Access 4, 1175-1189 (2016). https://doi.org/10.1109/ACCESS.2016.2545338
7.	L. Komsiyska, T. Buchberger, S. Diehl, M. Ehrensberger, C. Hanzl et al., Critical review of intelligent battery systems: challenges, implementation, and potential for electric vehicles. Energies 14, 5989 (2021). https://doi.org/10.3390/en14185989
8.	N. Piao, X. Gao, H. Yang, Z. Guo, G. Hu et al., Challenges and development of lithium-ion batteries for low temperature environments. eTransportation 11, 100145 (2022). https://doi.org/10.1016/j.etran.2021.100145
9.	A. Kushima, K.P. So, C. Su, P. Bai, N. Kuriyama et al., Liquid cell transmission electron microscopy observation of lithium metal growth and dissolution: root growth, dead lithium and lithium flotsams. Nano Energy 32, 271-279 (2017). https://doi.org/10.1016/j.nanoen.2016.12.001
10.	D. Hu, G. Chen, J. Tian, N. Li, L. Chen et al., Unrevealing the effects of low temperature on cycling life of 21700-type cylindrical Li-ion batteries. J. Energy Chem. 60, 104-110 (2021). https://doi.org/10.1016/j.jechem.2020.12.024
11.	X. Zhang, J. Zhu, E. Sahraei, Degradation of battery separators under charge-discharge cycles. RSC Adv. 7, 56099-56107 (2017). https://doi.org/10.1039/C7RA11585G
12.	C.R. Birkl, M.R. Roberts, E. McTurk, P.G. Bruce, D.A. Howey, Degradation diagnostics for lithium ion cells. J. Power. Sources 341, 373-386 (2017). https://doi.org/10.1016/j.jpowsour.2016.12.011
13.	X. Han, M. Ouyang, L. Lu, J. Li, Y. Zheng et al., A comparative study of commercial lithium ion battery cycle life in electrical vehicle: aging mechanism identification. J. Power. Sources 251, 38-54 (2014). https://doi.org/10.1016/j.jpowsour.2013.11.029
14.	Z.G. Wu, J.B. Zhang, Z. Li, B.Y. Liaw, Aging abuse boundary of lithium-ion cell above room temperature. J. Autom. Saf. Energy 9(1), 99-109 (2018). https://doi.org/10.3969/j.issn.1674-8484.2018.01.013
15.	J. Liu, Z. Huang, J. Sun, Q. Wang, Heat generation and thermal runaway of lithium-ion battery induced by slight overcharging cycling. J. Power. Sources 526, 231136 (2022). https://doi.org/10.1016/j.jpowsour.2022.231136
16.	L.-Y. Zhu, L.-X. Ou, L.-W. Mao, X.-Y. Wu, Y.-P. Liu et al., Advances in noble metal-decorated metal oxide nanomaterials for chemiresistive gas sensors: overview. Nano-Micro Lett. 15, 89 (2023). https://doi.org/10.1007/s40820-023-01047-z
17.	H. Zhao, W.-Y.A. Lam, L. Wang, H. Xu, W.A. Daoud et al., The significance of detecting imperceptible physical/chemical changes/reactions in lithium-ion batteries: a perspective. Energy Environ. Sci. 15, 2329-2355 (2022). https://doi.org/10.1039/D2EE01020H
18.	Y. Plotnikov, J. Karp, A. Knobloch, C. Kapusta, D. Lin-Eddy, Current sensor for in-situ monitoring of swelling of Li-ion prismatic cells. AIP Conference Proceedings. Boise, Idaho. AIP Publishing LLC 1650, 434-442 (2015) https://doi.org/10.1063/1.4914639
19.	W. Choi, Y. Seo, K. Yoo, T.J. Ko, J. Choi, Carbon nanotube-based strain sensor for excessive swelling detection of lithium-ion battery. in 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII), 2356-2359 (2019). https://doi.org/10.1109/TRANSDUCERS.2019.8808477
20.	A. Knobloch, C. Kapusta, J. Karp, Y. Plotnikov, J.B. Siegel et al., Fabrication of multimeasurand sensor for monitoring of a Li-ion battery. J. Electron. Packag. 140, 031002 (2018). https://doi.org/10.1115/1.4039861
21.	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
22.	S. Dey, Z.A. Biron, S. Tatipamula, N. Das, S. Mohon et al., Model-based real-time thermal fault diagnosis of Lithium-ion batteries. Contr. Eng. Pract. 56, 37-48 (2016). https://doi.org/10.1016/j.conengprac.2016.08.002
23.	J.P. Zheng, P. Andrei, L. Jin, J. Zheng, C. Zhang, Pre-lithiation strategies and energy density theory of lithium-ion and beyond lithium-ion batteries. J. Electrochem. Soc. 169, 040532 (2022). https://doi.org/10.1149/1945-7111/ac6540
24.	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
25.	W. Lee, S. Muhammad, C. Sergey, H. Lee, J. Yoon et al., Advances in the cathode materials for lithium rechargeable batteries. Angew. Chem. Int. Ed. 59, 2578-2605 (2020). https://doi.org/10.1002/anie.201902359
26.	Z. Deng, Z. Huang, Y. Shen, Y. Huang, H. Ding et al., Ultrasonic scanning to observe wetting and “unwetting” in Li-ion pouch cells. Joule 4, 2017-2029 (2020). https://doi.org/10.1016/j.joule.2020.07.014
27.	H. Cui, D. Ren, M. Yi, S. Hou, K. Yang et al., Operando monitoring of the open circuit voltage during electrolyte filling ensures high performance of lithium-ion batteries. Nano Energy 104, 107874 (2022). https://doi.org/10.1016/j.nanoen.2022.107874
28.	L. Yang, H.-S. Chen, W.-L. Song, D. Fang, Effect of defects on diffusion behaviors of lithium-ion battery electrodes: in situ optical observation and simulation. ACS Appl. Mater. Interfaces 10, 43623-43630 (2018). https://doi.org/10.1021/acsami.8b15260
29.	C. Modrzynski, V. Roscher, F. Rittweger, A. Ghannoum, P. Nieva, K.R. Riemschneider, Integrated optical fibers for simultaneous monitoring of the anode and the cathode in lithium ion batteries. 18th IEEE Sensors Conference. 19261568 (2019). https://doi.org/10.1109/SENSORS43011.2019.8956755
30.	V. Roscher, K.-R. Riemschneider, Method and measurement setup for battery state determination using optical effects in the electrode material. IEEE Trans. Instrum. Meas. 67, 735-744 (2018). https://doi.org/10.1109/TIM.2017.2782018
31.	E. Villemin, O. Raccurt, Optical lithium sensors. Coord. Chem. Rev. 435, 213801 (2021). https://doi.org/10.1016/j.ccr.2021.213801
32.	S. Zhu, J. Han, T.-S. Pan, Y.-M. Wei, W.-L. Song et al., A novel designed visualized Li-ion battery for in situ measuring the variation of internal temperature. Extreme Mech. Lett. 37, 100707 (2020). https://doi.org/10.1016/j.eml.2020.100707
33.	S. Rudolph, U. Schröder, I.M. Bayanov, K. Blenke, D. Hage, High resolution state of charge monitoring of vanadium electrolytes with IR optical sensor. J. Electroanal. Chem. 694, 17-22 (2013). https://doi.org/10.1016/j.jelechem.2013.01.042
34.	C. Zhu, R.E. Gerald, J. Huang, Progress toward sapphire optical fiber sensors for high-temperature applications. IEEE Trans. Instrum. Meas. 69, 8639-8655 (2020). https://doi.org/10.1109/TIM.2020.3024462
35.	A. Raghavan, P. Kiesel, L.W. Sommer, J. Schwartz, A. Lochbaum et al., Embedded fiber-optic sensing for accurate internal monitoring of cell state in advanced battery management systems part 1: cell embedding method and performance. J. Power. Sources 341, 466-473 (2017). https://doi.org/10.1016/j.jpowsour.2016.11.104
36.	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, 674-683 (2020). https://doi.org/10.1038/s41560-020-0665-y
37.	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 (Basel) 21, 1397 (2021). https://doi.org/10.3390/s21041397
38.	Z. Miao, Y. Li, X. Xiao, Q. Sun, B. He et al., Direct optical fiber monitor on stress evolution of the sulfur-based cathodes for lithium-sulfur batteries. Energy Environ. Sci. 15, 2029-2038 (2022). https://doi.org/10.1039/D2EE00007E
39.	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, 060520 (2021). https://doi.org/10.1149/1945-7111/ac03f0
40.	J. Albert, L.-Y. Shao, C. Caucheteur, Tilted fiber Bragg grating sensors. Laser Photonics Rev. 7, 83-108 (2013). https://doi.org/10.1002/lpor.201100039
41.	J. Hedman, F. Björefors, Fiber optic monitoring of composite lithium iron phosphate cathodes in pouch cell batteries. ACS Appl. Energy Mater. 5, 870-881 (2022). https://doi.org/10.1021/acsaem.1c03304
42.	A. Ghannoum, P. Nieva, Graphite lithiation and capacity fade monitoring of lithium ion batteries using optical fibers. J. Energy Storage 28, 101233 (2020). https://doi.org/10.1016/j.est.2020.101233
43.	J. Hedman, D. Nilebo, E. Larsson Langhammer, F. Björefors, Fibre optic sensor for characterisation of lithium-ion batteries. ChemSusChem 13, 5731-5739 (2020). https://doi.org/10.1002/cssc.202001709
44.	C. Gardner, E. Langhammer, W. Du, D.J.L. Brett, P.R. Shearing et al., In-situ Li-ion pouch cell diagnostics utilising plasmonic based optical fibre sensors. Sensors 22, 738 (2022). https://doi.org/10.3390/s22030738
45.	J. Lao, P. Sun, F. Liu, X. Zhang, C. Zhao et al., In situ plasmonic optical fiber detection of the state of charge of supercapacitors for renewable energy storage. Light. Sci. Appl. 7, 34 (2018). https://doi.org/10.1038/s41377-018-0040-y
46.	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
47.	M. Nascimento, S. Novais, M.S. Ding, M.S. Ferreira, S. Koch et al., Internal strain and temperature discrimination with optical fiber hybrid sensors in Li-ion batteries. J. Power. Sources 410-411, 1-9 (2019). https://doi.org/10.1016/j.jpowsour.2018.10.096
48.	M.S. Wahl, L. Spitthoff, H.I. Muri, A. Jinasena, O.S. Burheim et al., The importance of optical fibres for internal temperature sensing in lithium-ion batteries during operation. Energies 14, 3617 (2021). https://doi.org/10.3390/en14123617
49.	J. Huang, X. Han, F. Liu, C. Gervillié, L.A. Blanquer et al., Monitoring battery electrolyte chemistry via in-operando tilted fiber Bragg grating sensors. Energy Environ. Sci. 14, 6464-6475 (2021). https://doi.org/10.1039/D1EE02186A
50.	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
51.	P. Lu, N. Lalam, M. Badar, B. Liu, B.T. Chorpening et al., Distributed optical fiber sensing: review and perspective. Appl. Phys. Rev. 6, 041302 (2019). https://doi.org/10.1063/1.5113955
52.	J. Huang, S.T. Boles, J.-M. Tarascon, Sensing as the key to battery lifetime and sustainability. Nat. Sustain. 5, 194-204 (2022). https://doi.org/10.1038/s41893-022-00859-y
53.	A. Nedjalkov, J. Meyer, A. Gräfenstein, B. Schramm, M. Angelmahr et al., Refractive index measurement of lithium ion battery electrolyte with etched surface cladding waveguide Bragg gratings and cell electrode state monitoring by optical strain sensors. Batteries 5, 30 (2019). https://doi.org/10.3390/batteries5010030
54.	P. Desai, J. Huang, H. Hijazi, L. Zhang, S. Mariyappan et al., Deciphering interfacial reactions via optical sensing to tune the interphase chemistry for optimized Na-ion electrolyte formulation. Adv. Energy Mater. 11, 2101490 (2021). https://doi.org/10.1002/aenm.202101490
55.	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
56.	M. Nascimento, M. Ferreira, J. Pinto, Simultaneous sensing of temperature and Bi-directional strain in a prismatic Li-ion battery. Batteries 4, 23 (2018). https://doi.org/10.3390/batteries4020023
57.	A. Ganguli, B. Saha, A. Raghavan, P. Kiesel, K. Arakaki et al., Embedded fiber-optic sensing for accurate internal monitoring of cell state in advanced battery management systems part 2: internal cell signals and utility for state estimation. J. Power. Sources 341, 474-482 (2017). https://doi.org/10.1016/j.jpowsour.2016.11.103
58.	A.M. Cao Paz, J.M. Acevedo, C. Quintans-Grana, S. Fernandez-Gomez, Lifetime estimation for plastic optical fibers in harsh acid environments. IEEE Trans. Device Mater. Reliab. 12, 4-9 (2012). https://doi.org/10.1109/TDMR.2011.2170215
59.	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
60.	J. Peng, S. Jia, H. Yu, X. Kang, S. Yang et al., Design and experiment of FBG sensors for temperature monitoring on external electrode of lithium-ion batteries. IEEE Sens. J. 21, 4628-4634 (2021). https://doi.org/10.1109/JSEN.2020.3034257
61.	M. Maheshwari, Y. Yang, T. Chaturvedi, S.C. Tjin, Chirped fiber Bragg grating coupled with a light emitting diode as FBG interrogator. Opt. Lasers Eng. 122, 59-64 (2019). https://doi.org/10.1016/j.optlaseng.2019.05.025
62.	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, 5251 (2023). https://doi.org/10.1038/s41467-023-40995-3
63.	C. Jin, T. Liu, O. Sheng, M. Li, T. Liu et al., Rejuvenating dead lithium supply in lithium metal anodes by iodine redox. Nat. Energy 6, 378-387 (2021). https://doi.org/10.1038/s41560-021-00789-7
64.	Q. Jiang, D. Hu, M. Yang, Simultaneous measurement of liquid level and surrounding refractive index using tilted fiber Bragg grating. Sens. Actuat. A Phys. 170, 62-65 (2011). https://doi.org/10.1016/j.sna.2011.06.001
65.	T. Osuch, T. Jurek, K. Markowski, K. Jedrzejewski, Simultaneous measurement of liquid level and temperature using tilted fiber Bragg grating. IEEE Sens. J. 16, 1205-1209 (2016). https://doi.org/10.1109/JSEN.2015.2501381
66.	F. Liu, W. Lu, J. Huang, V. Pimenta, S. Boles et al., Detangling electrolyte chemical dynamics in lithium sulfur batteries by operando monitoring with optical resonance combs. Nat. Commun. 14, 7350 (2023). https://doi.org/10.1038/s41467-023-43110-8
67.	C.R. Taitt, G.P. Anderson, F.S. Ligler, Evanescent wave fluorescence biosensors: advances of the last decade. Biosens. Bioelectron. 76, 103-112 (2016). https://doi.org/10.1016/j.bios.2015.07.040
68.	A. Ghannoum, R.C. Norris, K. Iyer, L. Zdravkova, A. Yu et al., Optical characterization of commercial lithiated graphite battery electrodes and in situ fiber optic evanescent wave spectroscopy. ACS Appl. Mater. Interfaces 8, 18763-18769 (2016). https://doi.org/10.1021/acsami.6b03638
69.	A. Ghannoum, K. Iyer, P. Nieva, A. Khajepour, Fiber optic monitoring of lithium-ion batteries a novel tool to understand the lithiation of batteries. 15th IEEE Sensors Conference. 16597286 (2016). https://ieeexplore.ieee.org/document/7808695 URL
70.	A. Ghannoum, P. Nieva, A. Yu, A. Khajepour, Development of embedded fiber-optic evanescent wave sensors for optical characterization of graphite anodes in lithium-ion batteries. ACS Appl. Mater. Interfaces 9, 41284-41290 (2017). https://doi.org/10.1021/acsami.7b13464
71.	J. Hedman, R. Mogensen, R. Younesi, F. Björefors, Fiber optic sensors for detection of sodium plating in sodium-ion batteries. ACS Appl. Energy Mater. 5, 6219-6227 (2022). https://doi.org/10.1021/acsaem.2c00595
72.	V. Kapoor, N.K. Sharma, Effect of oxide layer on the performance of silver based fiber optic surface plasmon resonance sensor. Opt. Quantum Electron. 54, 475 (2022). https://doi.org/10.1007/s11082-022-03873-8
73.	R. Wang, H. Zhang, Q. Liu, F. Liu, X. Han et al., Operando monitoring of ion activities in aqueous batteries with plasmonic fiber-optic sensors. Nat. Commun. 13, 547 (2022). https://doi.org/10.1038/s41467-022-28267-y
74.	X. Liu, L. Yin, D. Ren, L. Wang, Y. Ren et al., In situ observation of thermal-driven degradation and safety concerns of lithiated graphite anode. Nat. Commun. 12, 4235 (2021). https://doi.org/10.1038/s41467-021-24404-1
75.	X. Wang, D. Ren, H. Liang, Y. Song, H. Huo et al., Ni crossover catalysis: truth of hydrogen evolution in Ni-rich cathode-based lithium-ion batteries. Energy Environ. Sci. 16, 1200-1209 (2023). https://doi.org/10.1039/D2EE04109J
76.	A.M. Cao-Paz, J. Marcos-Acevedo, A. del Río-Vázquez, C. Martínez-Peñalver, A. Lago-Ferreiro et al., A multi-point sensor based on optical fiber for the measurement of electrolyte density in lead-acid batteries. Sensors 10, 2587-2608 (2010). https://doi.org/10.3390/s100402587
77.	B. Liu, Z. Yu, Z. Tian, D. Homa, C. Hill et al., Temperature dependence of sapphire fiber Raman scattering. Opt. Lett. 40, 2041-2044 (2015). https://doi.org/10.1364/OL.40.002041
78.	J. Thapa, B. Liu, S.D. Woodruff, B.T. Chorpening, M.P. Buric, Raman scattering in single-crystal sapphire at elevated temperatures. Appl. Opt. 56, 8598-8606 (2017). https://doi.org/10.1364/AO.56.008598
79.	G.H. Watson Jr., W.B. Daniels, C.S. Wang, Measurements of Raman intensities and pressure dependence of phonon frequencies in sapphire. J. Appl. Phys. 52, 956-958 (1981). https://doi.org/10.1063/1.328785
80.	H.H. Kee, G.P. Lees, T.P. Newson, All-fiber system for simultaneous interrogation of distributed strain and temperature sensing by spontaneous brillouin scattering. Optics Lett. 25(10), 695-697 (2000). https://doi.org/10.1364/OL.25.000695
81.	S.-O. Yang, S. Lee, S.H. Song, J. Yoo, Development of a distributed optical thermometry technique for battery cells. Int. J. Heat Mass Transf. 194, 123020 (2022). https://doi.org/10.1016/j.ijheatmasstransfer.2022.123020
82.	H.L. Atchison, Z.R. Bailey, D.A. Wetz, M. Davis, J.M. Heinzel, Fiber optic based thermal and strain sensing of lithium-ion batteries at the individual cell level. J. Electrochem. Soc. 168, 040535 (2021). https://doi.org/10.1149/1945-7111/abf7e4
83.	Z. Wei, J. Hu, H. He, Y. Yu, J. Marco, Embedded distributed temperature sensing enabled multistate joint observation of smart lithium-ion battery. IEEE Trans. Ind. Electron. 70, 555-565 (2023). https://doi.org/10.1109/TIE.2022.3146503
84.	Z. Wei, P. Li, W. Cao, H. Chen, W. Wang et al., Machine learning-based hybrid thermal modeling and diagnostic for lithium-ion battery enabled by embedded sensing. Appl. Therm. Eng. 216, 119059 (2022). https://doi.org/10.1016/j.applthermaleng.2022.119059
85.	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
86.	C.-J. Bae, A. Manandhar, P. Kiesel, A. Raghavan, Monitoring the strain evolution of Lithium-Ion battery electrodes using an optical fiber Bragg grating sensor. Energy Technol. 4, 851-855 (2016). https://doi.org/10.1002/ente.201500514
87.	X. Wang, Y. Sone, G. Segami, H. Naito, C. Yamada et al., Understanding volume change in lithium-ion cells during charging and discharging using in situ measurements. J. Electrochem. Soc. 154, A14 (2007). https://doi.org/10.1149/1.2386933
88.	D. Clerici, F. Mocera, A. Somà, Electrochemical-mechanical multi-scale model and validation with thickness change measurements in prismatic lithium-ion batteries. J. Power. Sources 542, 231735 (2022). https://doi.org/10.1016/j.jpowsour.2022.231735
89.	M. Nascimento, C. Marques, J. Pinto, Tracking Li-ion batteries using fiber optic sensors. Smart Mobility-Recent Advances, New Perspectives and Applications, 1-28 (2022). https://doi.org/10.5772/intechopen.105548
90.	J. Meyer, A. Nedjalkov, A. Doering, M. Angelmahr, W. Schade, Fiber optical sensors for enhanced battery safety. SPIE Sensing Technology + Applications. in Proceedings of SPIE 9480, Fiber Optic Sensors and Applications XII Baltimore, MD, USA 9480, 190-201 (2015). https://doi.org/10.1117/12.2183325
91.	M. Nascimento, M.S. Ferreira, J.L. Pinto, Impact of different environmental conditions on lithium-ion batteries performance through the thermal monitoring with fiber sensors. in Proc SPIE 10453, Third International Conference on Applications of Optics and Photonics 10453, 673-677 (2017). https://doi.org/10.1117/12.2276331
92.	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
93.	L.W. Sommer, P. Kiesel, A. Ganguli, A. Lochbaum, B. Saha et al., Fast and slow ion diffusion processes in lithium ion pouch cells during cycling observed with fiber optic strain sensors. J. Power. Sources 296, 46-52 (2015). https://doi.org/10.1016/j.jpowsour.2015.07.025
94.	L.W. Sommer, A. Raghavan, P. Kiesel, B. Saha, J. Schwartz et al., Monitoring of intercalation stages in lithium-ion cells over charge-discharge cycles with fiber optic sensors. J. Electrochem. Soc. 162, A2664-A2669 (2015). https://doi.org/10.1149/2.0361514jes
95.	S. Kim, J. Wee, K. Peters, H.-Y.S. Huang, Multiphysics coupling in lithium-ion batteries with reconstructed porous microstructures. J. Phys. Chem. C 122(10), 5280-5290 (2018). https://doi.org/10.1021/acs.jpcc.7b12388
96.	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, 1453-1460 (2021). https://doi.org/10.1109/JSEN.2020.3016080
97.	C. Veth, D. Dragicevic, C. Merten, Thermal characterizations of a large-format lithium ion cell focused on high current discharges. J. Power. Sources 267, 760-769 (2014). https://doi.org/10.1016/j.jpowsour.2014.05.139
98.	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
99.	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, 1153 (2022). https://doi.org/10.1038/s41467-022-28792-w
100.	C.E. Hendricks, A.N. Mansour, D.A. Fuentevilla, G.H. Waller, J.K. Ko et al., Copper dissolution in overdischarged lithium-ion cells: X-ray photoelectron spectroscopy and X-ray absorption fine structure analysis. J. Electrochem. Soc. 167, 090501 (2020). https://doi.org/10.1149/1945-7111/ab697a
101.	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
102.	R. Srinivasan, P.A. Demirev, B.G. Carkhuff, Rapid monitoring of impedance phase shifts in lithium-ion batteries for hazard prevention. J. Power. Sources 405, 30-36 (2018). https://doi.org/10.1016/j.jpowsour.2018.10.014
103.	W. Wang, Y. Li, L. Cheng, F. Zuo, S. Yang, Safety performance and failure prediction model of cylindrical lithium-ion battery. J. Power. Sources 451, 227755 (2020). https://doi.org/10.1016/j.jpowsour.2020.227755
104.	J. Sun, B. Mao, Q. Wang, Progress on the research of fire behavior and fire protection of lithium ion battery. Fire Saf. J. 120, 103119 (2021). https://doi.org/10.1016/j.firesaf.2020.103119
105.	Y. Li, W. Wang, C. Lin, F. Zuo, High-efficiency multiphysics coupling framework for cylindrical lithium-ion battery under mechanical abuse. J. Clean. Prod. 286, 125451 (2021). https://doi.org/10.1016/j.jclepro.2020.125451
106.	Y. Li, W. Wang, C. Lin, X. Yang, F. Zuo, Multi-physics safety model based on structure damage for lithium-ion battery under mechanical abuse. J. Clean. Prod. 277, 124094 (2020). https://doi.org/10.1016/j.jclepro.2020.124094
107.	M. Ouyang, D. Ren, L. Lu, J. Li, X. Feng et al., Overcharge-induced capacity fading analysis for large format lithium-ion batteries with Li Ni_1/3Co_1/3Mn_1/3O²⁺ Li Mn₂O₄ composite cathode. J. Power. Sources 279, 626-635 (2015). https://doi.org/10.1016/j.jpowsour.2015.01.051
108.	G. Antonio, J. Monsalve-Serrano, R. Sari, S.D. Boggio, An optical investigation of thermal runway phenomenon under thermal abuse conditions. Energy Convers. Manag. 246, 114663 (2021). https://doi.org/10.1016/j.enconman.2021.114663
109.	Z. Liao, S. Zhang, K. Li, G. Zhang, T.G. Habetler, A survey of methods for monitoring and detecting thermal runaway of lithium-ion batteries. J. Power. Sources 436, 226879 (2019). https://doi.org/10.1016/j.jpowsour.2019.226879
110.	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, 1763 (2022). https://doi.org/10.3390/s22051763
111.	X. Feng, M. Fang, X. He, M. Ouyang, L. Lu et al., Thermal runaway features of large format prismatic lithium ion battery using extended volume accelerating rate calorimetry. J. Power. Sources 255, 294-301 (2014). https://doi.org/10.1016/j.jpowsour.2014.01.005
112.	S. Chen, Z. Gao, T. Sun, Safety challenges and safety measures of Li-ion batteries. Energy Sci. Eng. 9, 1647-1672 (2021). https://doi.org/10.1002/ese3.895
113.	P.A. Christensen, Z. Milojevic, M.S. Wise, M. Ahmeid, P.S. Attidekou et al., Thermal and mechanical abuse of electric vehicle pouch cell modules. Appl. Therm. Eng. 189, 116623 (2021). https://doi.org/10.1016/j.applthermaleng.2021.116623
114.	Y. Li, W. Wang, X.-G. Yang, F. Zuo, S. Liu et al., A smart Li-ion battery with self-sensing capabilities for enhanced life and safety. J. Power. Sources 546, 231705 (2022). https://doi.org/10.1016/j.jpowsour.2022.231705
115.	A. Raghavan, P. Kiesel, A. Lochbaum, B. Saha, L.W. Sommer, T. Staudt, Battery management based on internal optical sensing. US Patent, 9553465 B2 (2014). https://image-ppubs.uspto.gov/dirsearch-public/print/downloadPdf/9553465 URL
116.	H. Atchison, Z. Bailey, D. Wetz, M. Davis, J. Heinzel, Thermal monitoring of series and parallel connected lithium-ion battery modules using fiber optic sensors. ECS Sens. Plus 1, 025401 (2022). https://doi.org/10.1149/2754-2726/ac7abd
117.	A. Wang, L. Wang, Y. Wu, Y. He, D. Ren et al., Uncovering the effect of solid electrolyte interphase on ion desolvation for rational interface design in Li-ion batteries. Adv. Energy Mater. 13, 2300626 (2023). https://doi.org/10.1002/aenm.202300626
118.	A. Wang, L. Wang, H. Liang, Y. Song, Y. He et al., Lithium difluorophosphate as a widely applicable additive to boost lithium-ion batteries: a perspective. Adv. Funct. Mater. 33, 2370044 (2023). https://doi.org/10.1002/adfm.202370044
119.	Y. Song, L. Wang, H. Cui, H. Liang, Q. Hu et al., Boosting battery safety by mitigating thermal-induced crosstalk with a Bi-continuous separator. Adv. Energy Mater. 12, 2201964 (2022). https://doi.org/10.1002/aenm.202201964
120.	Y. Song, X. Liu, D. Ren, H. Liang, L. Wang et al., Simultaneously blocking chemical crosstalk and internal short circuit via gel-stretching derived nanoporous non-shrinkage separator for safe lithium-ion batteries. Adv. Mater. 34, e2106335 (2022). https://doi.org/10.1002/adma.202106335
121.	Z. Zhang, Y. Song, B. Zhang, L. Wang, X. He, Metallized plastic foils: a promising solution for high-energy lithium-ion battery current collectors. Adv. Energy Mater. 13, 2302134 (2023). https://doi.org/10.1002/aenm.202302134
122.	Q. Liu, L. Wang, X. He, Toward practical solid-state polymer lithium batteries by in situ polymerization process: a review. Adv. Energy Mater. 13, 2300798 (2023). https://doi.org/10.1002/aenm.202300798
123.	X. Feng, D. Ren, X. He, M. Ouyang, Mitigating thermal runaway of lithium-ion batteries. Joule 4, 743-770 (2020). https://doi.org/10.1016/j.joule.2020.02.010
124.	X. Feng, M. Ouyang, X. Liu, L. Lu, Y. Xia et al., Thermal runaway mechanism of lithium ion battery for electric vehicles: a review. Energy Storage Mater. 10, 246-267 (2018). https://doi.org/10.1016/j.ensm.2017.05.013
125.	J. Glenneberg, G. Kasiri, I. Bardenhagen, F. La Mantia, M. Busse et al., Investigations on morphological and electrochemical changes of all-solid-state thin film battery cells under dynamic mechanical stress conditions. Nano Energy 57, 549-557 (2019). https://doi.org/10.1016/j.nanoen.2018.12.070
126.	L. Xue, Y. Li, A. Hu, M. Zhou, W. Chen et al., In Situ/operando Raman techniques in lithium-sulfur batteries. Small Struct. 3, 2100170 (2022). https://doi.org/10.1002/sstr.202100170
127.	D. Ma, Z. Cao, A. Hu, Si-based anode materials for Li-ion batteries: a mini review. Nano-Micro Lett. 6, 347-358 (2014). https://doi.org/10.1007/s40820-014-0008-2
128.	H. Deng, F. Qiu, X. Li, H. Qin, S. Zhao et al., A Li-ion oxygen battery with Li-Si alloy anode prepared by a mechanical method. Electrochem. Commun. 78, 11-15 (2017). https://doi.org/10.1016/j.elecom.2017.03.010
129.	J. Zhu, U. Schwingenschlögl, Silicene for Na-ion battery applications. Mater. 3, 035012 (2016). https://doi.org/10.1088/2053-1583/3/3/035012
130.	Z. Ju, Q. Zhao, D. Chao, Y. Hou, H. Pan et al., Energetic aqueous batteries. Adv. Energy Mater. 12, 2201074 (2022). https://doi.org/10.1002/aenm.202201074
131.	S. Wei, H. Liu, R. Wei, L. Chen, Cathodes with MnO₂ catalysts for metal fuel battery. Front. Energy 13, 9-15 (2019). https://doi.org/10.1007/s11708-019-0611-5
132.	S. Qian, X. Chen, S. Jiang, Q. Sun, X. Chen et al., Plasmonic fiber-optic sensing system for in situ monitoring the capacitance and temperature of supercapacitors. Opt. Express 30, 27322-27332 (2022). https://doi.org/10.1364/OE.462189
133.	P. Listewnik, M. Bechelany, M. Szczerska, Microsphere structure application for supercapacitor in situ temperature monitoring. Smart Mater. Struct. 30, 10LT01 (2021). https://doi.org/10.1088/1361-665x/ac221b
134.	X. Cheng, M. Pecht, In situ stress measurement techniques on Li-ion battery electrodes: a review. Energies 10, 591 (2017). https://doi.org/10.3390/en10050591
135.	V.A. Sethuraman, M.J. Chon, M. Shimshak, V. Srinivasan, P.R. Guduru, In situ measurements of stress evolution in silicon thin films during electrochemical lithiation and delithiation. J. Power. Sources 195, 5062-5066 (2010). https://doi.org/10.1016/j.jpowsour.2010.02.013
136.	A.F. Bower, P.R. Guduru, V.A. Sethuraman, A finite strain model of stress, diffusion, plastic flow, and electrochemical reactions in a lithium-ion half-cell. J. Mech. Phys. Solids 59, 804-828 (2011). https://doi.org/10.1016/j.jmps.2011.01.003
137.	V.A. Sethuraman, V. Srinivasan, A.F. Bower, P.R. Guduru, In situ measurements of stress-potential coupling in lithiated silicon. J. Electrochem. Soc. 157, A1253 (2010). https://doi.org/10.1149/1.3489378
138.	S. Fujimoto, S. Uemura, N. Imanishi, S. Hirai, Oxygen concentration measurement in the porous cathode of a lithium-air battery using a fine optical fiber sensor. Mech. Eng. Lett. 5, 19-00095 (2019). https://doi.org/10.1299/mel.19-00095
139.	A. Fortier, M. Tsao, N. Williard, Y. Xing, M. Pecht, Preliminary study on integration of fiber optic Bragg grating sensors in Li-ion batteries and in situ strain and temperature monitoring of battery cells. Energies 10, 838 (2017). https://doi.org/10.3390/en10070838
140.	Y. Wu, X. Long, J. Lu, R. Zhou, L. Liu et al., Long-life in situ temperature field monitoring using Fiber Bragg grating sensors in electromagnetic launch high-rate hardcase lithium-ion battery. J. Energy Storage 57, 106207 (2023). https://doi.org/10.1016/j.est.2022.106207
141.	B. Witzigmann, Y. Arakawa, M. Osiński, K. Markiewicz, P. Mergo et al., A fiber optic temperature sensor based on multi-core microstructured fiber with coupled cores for high temperature environment. Physics and Simulation of Optoelectronic Devices XXVI. 105260X (2018). https://doi.org/10.1117/12.2290780
142.	B. Witzigmann, M. Osiński, Y. Arakawa, A. Ziolowicz, A. Kołakowska et al., Strain sensor based on sectional crosstalk change in dual-core fibers. Physics and Simulation of Optoelectronic Devices XXV. 100981N (2017). https://doi.org/10.1117/12.2252773
143.	J. Mathew, Y. Semenova, G. Rajan, G. Farrell, Humidity sensor based on photonic crystal fibre interferometer. Electron. Lett. 46, 1341 (2010). https://doi.org/10.1049/el.2010.2080
144.	K. Naeem, B.H. Kim, B. Kim, Y. Chung, High-sensitivity temperature sensor based on a selectively-polymer-filled two-core photonic crystal fiber in-line interferometer. IEEE Sens. J. 15, 3998-4003 (2015). https://doi.org/10.1109/JSEN.2015.2405911
145.	O. Frazao, C. Jesus, J.M. Baptista, J.L. Santos, P. Roy, Fiber-optic interferometric torsion sensor based on a two-LP-mode operation in birefringent fiber. IEEE Photonics Technol. Lett. 21, 1277-1279 (2009). https://doi.org/10.1109/LPT.2009.2025870
146.	H.P. Gong, C.C. Chan, P. Zu, L.H. Chen, X.Y. Dong, Curvature measurement by using low-birefringence photonic crystal fiber based Sagnac loop. Opt. Commun. 283, 3142-3144 (2010). https://doi.org/10.1016/j.optcom.2010.04.023
147.	H.V. Thakur, S.M. Nalawade, S. Gupta, R. Kitture, S.N. Kale, Photonic crystal fiber injected with Fe₃O₄ nanofluid for magnetic field detection. Appl. Phys. Lett. 99, 161101 (2011). https://doi.org/10.1063/1.3651490
148.	K. Mileńko, D.J. Hu, P.P. Shum, T. Zhang, J.L. Lim et al., Photonic crystal fiber tip interferometer for refractive index sensing. Opt. Lett. 37, 1373-1375 (2012). https://doi.org/10.1364/OL.37.001373
149.	J.N. Dash, R. Jha, Inline microcavity-based PCF interferometer for refractive index and temperature sensing. IEEE Photonics Technol. Lett. 27, 1325-1328 (2015). https://doi.org/10.1109/LPT.2015.2421308
150.	M.F.O. Hameed, M.Y. Azab, A.M. Heikal, S.M. El-Hefnawy, S.S.A. Obayya, Highly sensitive plasmonic photonic crystal temperature sensor filled with liquid crystal. IEEE Photonics Technol. Lett. 28, 59-62 (2016). https://doi.org/10.1109/LPT.2015.2480339
151.	C. Du, Q. Wang, Y. Zhao, J. Li, Highly sensitive temperature sensor based on an isopropanol-filled photonic crystal fiber long period grating. Opt. Fiber Technol. 34, 12-15 (2017). https://doi.org/10.1016/j.yofte.2016.11.013
152.	W.J. Bock, J. Chen, P. Mikulic, T. Eftimov, M. Korwin-Pawlowski, Pressure sensing using periodically tapered long-period gratings written in photonic crystal fibres. Meas. Sci. Technol. 18, 3098-3102 (2007). https://doi.org/10.1088/0957-0233/18/10/s08
153.	Y.-P. Wang, Highly sensitive long-period fiber-grating strain sensor with low temperature sensitivity. Optics Lett. 31(23), 3414-3416 (2006). https://doi.org/10.1364/OL.31.003414
154.	S. Sulejmani, C. Sonnenfeld, T. Geernaert, P. Mergo, M. Makara et al., Control over the pressure sensitivity of Bragg grating-based sensors in highly birefringent microstructured optical fibers. IEEE Photonics Technol. Lett. 24, 527-529 (2012). https://doi.org/10.1109/LPT.2012.2183120
155.	Y. Zhao, R.-Q. Lv, Y. Ying, Q. Wang, Hollow-core photonic crystal fiber Fabry-Perot sensor for magnetic field measurement based on magnetic fluid. Opt. Laser Technol. 44, 899-902 (2012). https://doi.org/10.1016/j.optlastec.2011.11.011
156.	W. Lin, H. Zhang, B. Song, B. Liu, Y. Lin et al., Magnetic field sensor based on fiber taper coupler coated with magnetic fluid. Proc. SPIE 9634, 1056-1059 (2015). https://doi.org/10.1117/12.2191929
157.	H. Liu, Y. Wang, C. Tan, C. Zhu, Y. Gao et al., Simultaneous measurement of temperature and magnetic field based on cascaded photonic crystal fibers with surface plasmon resonance. Optik 134, 257-263 (2017). https://doi.org/10.1016/j.ijleo.2017.01.022
158.	A.A. Rifat, G.A. Mahdiraji, Y.M. Sua, R. Ahmed, Y.G. Shee et al., Highly sensitive multi-core flat fiber surface plasmon resonance refractive index sensor. Opt. Express 24, 2485-2495 (2016). https://doi.org/10.1364/OE.24.002485
159.	H.-J. Kim, O.-J. Kown, S.B. Lee, Y.-G. Han, Measurement of temperature and refractive index based onsurface long-period gratings deposited onto a D-shaped photonic crystal fiber. Appl. Phys. B 102, 81-85 (2011). https://doi.org/10.1007/s00340-010-4146-z
160.	J. Sun, C.C. Chan, Photonic bandgap fiber for refractive index measurement. Sens. Actuat. B Chem. 128, 46-50 (2007). https://doi.org/10.1016/j.snb.2007.05.037
161.	M. Nascimento, M.S. Ferreira, J.L. Pinto, Temperature fiber sensing of Li-ion batteries under different environmental and operating conditions. Appl. Therm. Eng. 149, 1236-1243 (2019). https://doi.org/10.1016/j.applthermaleng.2018.12.135
162.	S.S. Patil, V.P. Labade, N.M. Kulkarni, A.D. Shaligram, Refractometric fiber optic sensor for in situ monitoring the state-of-charge of a lead acid battery. J. Opt. Technol. 81, 159-163 (2014). https://doi.org/10.1364/JOT.81.000159
163.	N.A. David, P.M. Wild, J. Jensen, T. Navessin, N. Djilali, Simultaneous in situ measurement of temperature and relative humidity in a PEMFC using optical fiber sensors. J. Electrochem. Soc. 157, B1173 (2010). https://doi.org/10.1149/1.3436652
164.	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, 1651 (2022). https://doi.org/10.1038/s41467-022-29330-4
165.	F. Rittweger, C. Modrzynski, P. Schiepel, K.-R. Riemschneider, Self-compensation of cross influences using spectral transmission ratios for optical fiber sensors in lithium-ion batteries. In 2021 IEEE Sensors Applications Symposium (SAS). Sundsvall, Sweden. IEEE, 1-6 (2021). https://doi.org/10.1109/SAS51076.2021.9530176
166.	J. Duan, X. Tang, H. Dai, Y. Yang, W. Wu et al., Building safe lithium-ion batteries for electric vehicles: a review. Electrochem. Energy Rev. 3, 1-42 (2020). https://doi.org/10.1007/s41918-019-00060-4
167.	M.F. Sgroi, M. Dotoli, M. Giuliano, G. Nicol, F. Parussa et al., Smart batteries: requirements of the automotive world. In 2021 IEEE International Workshop on Metrology for Automotive (MetroAutomotive). Bologna, Italy. IEEE, (2021), 42-47.
168.	H. Zhang, D. Ren, H. Ming, W. Zhang, G. Cao et al., Digital twin enables rational design of ultrahigh-power lithium-ion batteries. Adv. Energy Mater. 13(1), 2202660 (2022). https://doi.org/10.1002/aenm.202202660
169.	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, 2100362 (2021). https://doi.org/10.1002/aenm.202100362
170.	Y. Inoue, P. Kuad, Y. Okumura, Y. Takashima, H. Yamaguchi et al., Thermal and photochemical switching of conformation of poly(ethylene glycol)-substituted cyclodextrin with an azobenzene group at the chain end. J. Am. Chem. Soc. 129, 6396-6397 (2007). https://doi.org/10.1021/ja071717q
171.	M. Fichtner, K. Edström, E. Ayerbe, M. Berecibar, A. Bhowmik et al., Rechargeable batteries of the future: the state of the art from a BATTERY 2030+ perspective. Adv. Energy Mater. 12, 2102904 (2022). https://doi.org/10.1002/aenm.202102904
172.	J. Amici, P. Asinari, E. Ayerbe, P. Barboux, P. Bayle-Guillemaud et al., A roadmap for transforming research to invent the batteries of the future designed within the European large scale research initiative BATTERY 2030+. Adv. Energy Mater. 12, 2102785 (2022). https://doi.org/10.1002/aenm.202102785
173.	R. Narayan, C. Laberty-Robert, J. Pelta, J.-M. Tarascon, R. Dominko, Self-healing: an emerging technology for next-generation smart batteries. Adv. Energy Mater. 12, 2102652 (2022). https://doi.org/10.1002/aenm.202102652
174.	A. Benayad, D. Diddens, A. Heuer, A.N. Krishnamoorthy, M. Maiti et al., High-throughput experimentation and computational freeway lanes for accelerated battery electrolyte and interface development research. Adv. Energy Mater. 12, 2102678 (2022). https://doi.org/10.1002/aenm.202102678
175.	D. Atkins, E. Ayerbe, A. Benayad, F.G. Capone, E. Capria et al., Understanding battery interfaces by combined characterization and simulation approaches: challenges and perspectives. Adv. Energy Mater. 12, 2102687 (2022). https://doi.org/10.1002/aenm.202102687
176.	S. Clark, F.L. Bleken, S. Stier, E. Flores, C.W. Andersen et al., Toward a unified description of battery data. Adv. Energy Mater. 12, 2102702 (2022). https://doi.org/10.1002/aenm.202102702
177.	E. Ayerbe, M. Berecibar, S. Clark, A.A. Franco, J. Ruhland, Digitalization of battery manufacturing: current status, challenges, and opportunities. Adv. Energy Mater. 12, 2102696 (2022). https://doi.org/10.1002/aenm.202102696
178.	A. Bhowmik, M. Berecibar, M. Casas-Cabanas, G. Csanyi, R. Dominko et al., Implications of the BATTERY 2030+ AI-assisted toolkit on future low-TRL battery discoveries and chemistries. Adv. Energy Mater. 12, 2102698 (2022). https://doi.org/10.1002/aenm.202102698
179.	Ministry of Industry and Information Technology of China, Key Special Projects for Energy Storage and Smart Grid. (2021). https://www.miit.gov.cn/zwgk/jytafwgk/art/2021/art_1e9ad802670d4517affdd7ea85495e57.html URL

Please choose a citation manager

Content to export