As a technology that can adjust energy, time, and space, batteries are one of the best ways to optimize energy applications and improve the comprehensive efficiency of energy use. With the increasing demand for energy storage and the wide application of large-scale energy storage systems, batteries with high-energy density and long-cycle life have become a current research focus. However, with the improved battery performance, its safety has become increasingly a concern. Battery accidents are often closely related to the flammable and explosive properties of organic electrolytes, heat accumulation induced by high current charging and discharging, battery monomer structure, and the thermoelectric loop control technology of modules. Nondestructive characterization means are used to accurately analyze the battery performance evolution, such as thermal runaway and life attenuation. These techniques can considerably avoid external interference and conduct insitu detection and analysis of the battery under the real environment and operating conditions to express and monitor the battery service behavior clearly and accurately. Thus, critical information, such as the reaction principle and the health state of the battery, can be derived. Furthermore, preparing electrode materials and designing and grouping battery structures improve the safety and reliability of the battery. Herein, the recently reported battery nondestructive testing, monitoring, and characterization methods are reviewed, including sensor, magnetic resonance, X-ray, neutron scattering, ultrasonic detection, and Raman scattering. These methods help to describe their principles, application methods, and characteristics of information acquisition, making a comprehensive comparison of each characterization technology, especially the mutually supportive relationship of battery data. It provides methods and technical means to deeply explore the relationship between the internal microstructure evolution, electrical performance, and battery safety under different working conditions, which gradually improve the battery efficiency and support the establishment of battery accident warning and life warning mechanisms.
Fig. 3
Schematic of the smart battery design and fabrication. (a) Dendrite formation in a traditional lithium battery where complete penetration of the separator by a lithium dendrite is only detected when the battery fails due to an internal short circuit and VLi-Li drops to zero; (b) In comparison, a lithium battery with a bifunctional separator where the overgrown lithium dendrite penetrates into the separator and makes contact with the conducting copper layer, giving rise to a drop in VCu-Li as warning of impending failure due to an internal short circuit. As well as, the full battery remains safely operational with non-zero potential[11] (NatureCommunications has image copyright)
Fig. 4
(a) Schematic of customized RTD embedded LIB coin cell; (b) RTD embedded polylactic acid spacer and CR2032 cell with internal RTD. Dimension of the RTD embedded spacer was comparable to ordinary CR2032 coin cell spacer, allowing for reliable sensor-electrode contact and cell sealing after assembly[22](ScientificReports has image copyright)
Fig. 6
Schematic diagram and image of FBG sensor attached (a) or implanted (b) on anode electrode of lithium ion battery respectively (c) and (d)[47] (EnergyTechnology has image copyright)
Fig. 8
Schematic of the cell embedded with FBG and corresponding electrochemical performance. (a) The schematic illustration of the structure of FBG and the Li-S pouch cell embedded with FBG; (b) The cycle performance and (c) discharge-charge curves of the Li-S pouch cell embedded with FBG and a normal cell without FBG[23] (EnergyEnvironmentalScience has image copyright)
Fig. 9
The schematic diagram of experimental setup for strain monitoring of anode-free lithium metal batteries[51] (AdvancedScience has image copyright)
Fig. 10
(a) Schematic depiction of the measurement setup with a thin film pouch cell placed in the coil of a NMR device, connected to a battery cycler; (b) Schematic thin film pouch cell; (c)—(f) 7Li-NMR spectra of thin film pouch cells and SEM images of the corresponding electrodes after (c1—f1) and (c2—f2) 8 hours of electrodeposition at 0.5 mA/cm2 containing 1-PF6-C, 1-TFSI-C, 1-TFSI-E and 3-TFSI-E[59] (PhysChemChemPhys has image copyright)
Fig. 12
Noninvasive electron paramagnetic resonance imaging technique monitors changes in lithium deposits inside the battery during charging and discharging[25] (ChemistryofMaterials has image copyright)
Wang等[64]利用原位EPR技术研究锂离子电池中石墨阳极在室温下的锂化/沉积行为。EPR谐振器质量因子、自旋密度和EPR光谱变化分别反映了石墨的电导率、锂化程度和沉积过程。该方法基于微波趋肤深度对整个电池电导率的影响而发生的变化来评估在第1个电位循环形成的SEI膜,得出SEI层膜的形成开始于1.3 V左右。此外对石墨阳极多次锂化/脱锂循环期间EPR自旋密度变化进行定量分析,并将这些数据与同一条件下的电化学测量相关联。研究结果表明,锂金属在石墨负极上的沉积不需要过充电(电位通常≤0 V vs. Li+/Li)。通过光谱模拟得到即使在低扫描速率(0.04 mV/s)下,石墨上的不可逆沉积在约+0.04 V(vs. Li+/Li)开始,长循环期间锂信号的降低证实了碳酸亚乙烯酯(VC)电解质添加剂对锂沉积的抑制,这主要归因于机械柔性和聚合物SEI层具有更高的离子电导率。其研究表明,对于高倍率循环,建议把阳极的安全截止电位限制为+0.05 V,并通过EPR在长循环中的响应进一步证实该提议。
Schellenberger等[67]基于具有两个软X射线透明氮化硅(SiN x )膜窗口的微流体电化学半电池研究硅阳极上的SEI,通过控制气泡形成柔软的X射线透明薄电解质层。在循环过程中形成SEI后,他们使用高强度X射线在入口窗口附近产生气泡并从中推出多余的电解质探测体积,气泡稳定地处于膜之间的中心,留在光束路径中的是一层薄薄的电解质,覆盖着SEI和阳极。通过使用低强度的X射线来避免光束损伤,然后利用透射sXAS在硅薄膜阳极上进行原位SEI研究,而不需要拆解电池。实验结果参考化合物的吸收光谱并进行比较,确定了形成物质并研究了SEI的组成,如乙酸锂、乙烯二碳酸锂或乙烯单碳酸锂、乙酰丙酮锂、氢氧化锂和氟化锂,此外还观察到了醛类物质,这可能是源于多孔SEI膜形态中存在的液体夹杂物。
Fig. 14
Evolution of dendrite growth. (a)—(d) X-ray tomography slices showing the cross-sections of symmetric lithium cells cycled to various stages; (e)—(h) The corresponding 3D reconstructed image[15](NatureMaterials has image copyright)
Fig. 15
(a) 3D visualization of Sn particles at the first lithiation-delithiation process; (b) 3D morphological change of Sn particles at the first sodiation-desodiation cycle; (c) Schematic illustration showing the difference of Sn microstructural change in sodium-ion battery and lithium ion battery[75](NatureCommunications has image copyright)
Fig. 16
(a) Schematic view of conventional X-ray tomography; (b) Schematic of the oblique soft X-ray tomography[80](ElectrochimicaActa has image copyright)
图18
中子和X射线断层扫描的重建图像以及虚拟展开重建后提取的截面示例。在X射线图像中清晰可见的是镍电流收集网,其看起来比Li X MnO2 活性电极材料更亮[27](Nature communication 拥有图片版权)
Fig. 18
Image displays the reconstructed tomograms from neutron and X-ray CT along with examples of sections extracted following virtual unrolling of the reconstructions. Clearly visible in the X-ray images is the nickel current collecting mesh, which appears brighter than the Li X MnO2 active electrode material[27](Naturecommunication has image copyright )
Euser等[28]为了确定锂离子电池的退化机理和失效机制,他们利用原位拉曼光谱监测工况下锂离子电池循环过程中碳酸酯液体电解质的化学反应(图20)。通过从LiNi x Mn y Co1-x-y O2(NMC,x>0.6)-石墨全电池中提取少量电解质{LP57[1.0 mol/L的LiPF6分散于由体积比为3∶7的碳酸乙烯酯(EC)和碳酸甲乙酯(EMC)组成的电解液中]添加了2%的碳酸亚乙烯酯},利用无硅背景拉曼光谱分析电池在第1个电化学循环期间SEI膜形成过程中的各种电解质化学性质,重复取样以便于实时跟踪电池循环过程中电解质化学变化,此外每次测量完光谱后,再将电解质样品注入电池中。为了进一步追踪锂离子溶剂化动力学,该团队将未含有不同浓度锂盐的电解质进行异位光谱测量,实验结果表明,电解液中锂盐浓度和电极-电解质界面化学能改变碳酸盐类溶剂的溶剂化结构,此外在一个充放电循环中,可从EC的C=O峰(1782~1817 cm-1)和EMC的C=O峰(1730~1765 cm-1)的强度比间接跟踪溶剂化环状碳酸盐(Li+—O=C,EC)和溶剂化线性碳酸盐(Li+—O=C,EMC)之间的平衡演变。
Hardwick等[92]报道用克尔门控拉曼光谱仪研究LiPF6基碳酸酯电解液中锂离子石墨负极插层结构的变化,追踪完整的电化学锂化和脱锂过程。由于这种电解液在低电位分解会产生具有荧光性质的副产物,使得拉曼散射与荧光发射信号重叠,为此引入能够过滤掉荧光信号的克尔门控光谱仪,即使在高锂化状态(0.5≤x≤1,Li x C6)石墨的拉曼信号也比传统的拉曼显微镜技术清晰度高。实验数据表明石墨带Li0.5C6最初位于1590 cm-1,随着进一步锂化为LiC6,化学位移到1564 cm-1处,再通过数值拟合趋势变化,将此作为石墨负极充电状态(锂化)的函数,提供了一个较为灵敏的监测锂离子电池中石墨负极的高荷电状态的方法。
SUN H T, MEI L, LIANG J F, et al. Three-dimensional holey-graphene/niobia composite architectures for ultrahigh-rate energy storage[J]. Science, 2017, 356(6338): 599-604.
ASSAT G, TARASCON J M. Fundamental understanding and practical challenges of anionic redox activity in Li-ion batteries[J]. Nature Energy, 2018, 3(5): 373-386.
PAREKH M H, LI B, PALANISAMY M, et al. Insitu thermal runaway detection in lithium-ion batteries with an integrated internal sensor[J]. ACS Applied Energy Materials, 2020, 3(8): 7997-8008.
TANG F C, WU Z B, YANG C, et al. Synchrotron X-ray tomography for rechargeable battery research: fundamentals, setups and applications[J]. Small Methods, 2021, 5(9): e2100557.
DENG L W, FENG T Y, SHU S W, et al. Nondestructive lithium plating online detection for lithium-ion batteries: A review [J]. Energy Storage Science and Technology, 2023, 12(1): 263-277.
LIU Q N, TANG Y F, SUN H M, et al. Insitu electrochemical study of Na-O2/CO2 batteries in an environmental transmission electron microscope[J]. ACS Nano, 2020, 14(10): 13232-13245.
HAN S B, CAI C, YANG F, et al. Interrogation of the reaction mechanism in a Na-O2 battery using insitu transmission electron microscopy[J]. ACS Nano, 2020, 14(3): 3669-3677.
RONG G L, ZHANG X Y, ZHAO W, et al. Liquid-phase electrochemical scanning electron microscopy for insitu investigation of lithium dendrite growth and dissolution[J]. Advanced Materials, 2017, 29(13): 1606187.
GOLOZAR M, PAOLELLA A, DEMERS H, et al. Insitu observation of solid electrolyte interphase evolution in a lithium metal battery[J]. Communications Chemistry, 2019, 2(1).
ZACHMAN M J, TU Z Y, CHOUDHURY S, et al. Cryo-STEM mapping of solid-liquid interfaces and dendrites in lithium-metal batteries[J]. Nature, 2018, 560(7718): 345-349.
WU H, ZHUO D, KONG D S, et al. Improving battery safety by early detection of internal shorting with a bifunctional separator[J]. Nature Communications, 2014, 5(1): 5193.
SHI S, LYU N W, MA J X, et al. Comparative study on the effectiveness of different types of gas detection on the overcharge safety early warning of a lithium iron phosphate battery energy storage compartment[J]. Energy Storage Science and Technology, 2022,11(8): 2452-2462.
CHANDRASHEKAR S, TREASE N M, CHANG H J, et al. 7Li MRI of Li batteries reveals location of microstructural lithium [J]. Nature Materials, 2012, 11(4): 311-315.
LOULI A J, ELDESOKY A, WEBER R, et al. Diagnosing and correcting anode-free cell failure via electrolyte and morphological analysis[J]. Nature Energy, 2020, 5(9): 693-702.
HARRY K J, HALLINAN D T, PARKINSON D Y, et al. Detection of subsurface structures underneath dendrites formed on cycled lithium metal electrodes [J]. Nature Materials, 2014, 13(1): 69-73.
HEENAN T M M, TAN C, HACK J, et al. Developments in X-ray tomography characterization for electrochemical devices[J]. Materials Today, 2019, 31: 69-85.
XIANG Y X, LI X, CHENG Y Q, et al. Advanced characterization techniques for solid state lithium battery research[J]. Materials Today, 2020, 36: 139-157.
DENG Z, HUANG Z Y, LIU L, et al. Applications of ultrasound technique in characterization of lithium-ion batteries [J]. Energy Storage Science and Technology, 2019, 8(6): 1033-1039.
SUN H B, MURALIDHARAN N, AMIN R, et al. Ultrasonic nondestructive diagnosis of lithium-ion batteries with multiple frequencies[J]. Journal of Power Sources, 2022, 549: https://doi.org/10.1016/j.jpowsour.2022.232091.
APPLEBERRY M C, KOWALSKI J A, AFRICK S A, et al. Avoiding thermal runaway in lithium-ion batteries using ultrasound detection of early failure mechanisms[J]. Journal of Power Sources, 2022, 535: https://doi.org/10.1016/j.jpowsour.2022.231423.
CHENG Q, WEI L, LIU Z, et al. Operando and three-dimensional visualization of anion depletion and lithium growth by stimulated Raman scattering microscopy[J]. Nature Communications, 2018, 9(1): doi: 10.1038/s41467-018-05289-z.
LI B, PAREKH M H, ADAMS R A, et al. Lithium-ion battery thermal safety by early internal detection, prediction and prevention[J]. Scientific Reports, 2019, 9(1): 13255.
MIAO Z Y, LI Y P, XIAO X P, et al. Direct optical fiber monitor on stress evolution of the sulfur-based cathodes for lithium-sulfur batteries[J]. Energy & Environmental Science, 2022, 15(5): 2029-2038.
GENG F S, YANG Q, LI C, et al. Mapping the distribution and the microstructural dimensions of metallic lithium deposits in an anode-free battery by insitu EPR imaging[J]. Chemistry of Materials, 2021, 33(21): 8223-8234.
LIU X S, WANG D D, LIU G, et al. Distinct charge dynamics in battery electrodes revealed by insitu and operando soft X-ray spectroscopy [J]. Nature Communications, 2013, 4: 2568.
ZIESCHE R F, ARLT T, FINEGAN D P, et al. 4D imaging of lithium-batteries using correlative neutron and X-ray tomography with a virtual unrolling technique[J].Nature Communications, 2020, 11(1): 777.
MIELE E, DOSE W M, MANYAKIN I, et al. Hollow-core optical fibre sensors for operando Raman spectroscopy investigation of Li-ion battery liquid electrolytes[J].Nature Communications, 2022, 13(1): 1651.
LIU X H, ZHANG L S, YU H Q, et al. Bridging multiscale characterization technologies and digital modeling to evaluate lithium battery full lifecycle[J]. Advanced Energy Materials, 2022, 12(33): https://doi.org/10.1002/aenm.202200889.
LEE C Y, CHEN C H, CHIU C Y, et al. Application of flexible four-in-one microsensor to internal real-time monitoring of proton exchange membrane fuel cell[J]. Sensors, 2018, 18(7): doi: 10.3390/s180722.
LEE C-Y, CHUANG S-M, LEE S-J, et al. Flexible micro sensor for in-situ monitoring temperature and voltage of coin cells[J]. Sensors and Actuators A: Physical, 2015, 232: 214-222.
BAGHDADI I, BRIAT O, GYAN P, et al. State of health assessment for lithium batteries based on voltage-time relaxation measure[J]. Electrochimica Acta, 2016, 194: 461-472.
MUTYALA M S K, ZHAO J Z, LI J Y, et al. In-situ temperature measurement in lithium ion battery by transferable flexible thin film thermocouples[J]. Journal of Power Sources, 2014, 260: 43-49.
PENG X L, HAN J, ZHANG Q, et al. Real-time mechanical and thermal monitoring of lithium batteries with PVDF-TrFE thin films integrated within the battery[J]. Sensors and Actuators A: Physical, 2022, 338: doi: 10.1016/j.sna.2022.113484.
WANG H Y, YANG W J, KIM Y B. Analyzing in-plane temperature distribution via a micro-temperature sensor in a unit polymer electrolyte membrane fuel cell[J]. Applied Energy, 2014, 124: 148-155.
LAMMER M, KÖNIGSEDER A, HACKER V. Holistic methodology for characterisation of the thermally induced failure of commercially available 18650 lithium ion cells[J]. RSC Advances, 2017, 7(39): 24425-24429.
CAI T, VALECHA P, TRAN V, et al. Detection of Li-ion battery failure and venting with carbon dioxide sensors[J]. eTransportation, 2021, 7: doi: 10.1016/j.etran.2020.100100.
KOCH S, FILL A, BIRKE K P. Comprehensive gas analysis on large scale automotive lithium-ion cells in thermal runaway[J]. Journal of Power Sources, 2018, 398: 106-112.
WANG Z, ZHU L, LIU J W, et al. Gas sensing technology for the detection and early warning of battery thermal runaway: A review[J]. Energy & Fuels, 2022, 36(12): 6038-6057.
LIAO Z H, ZHANG J G, GAN Z Y, et al. Thermal runaway warning of lithium-ion batteries based on photoacoustic spectroscopy gas sensing technology[J]. International Journal of Energy Research, 2022, 46(15): 21694-21702.
LYU S Q, LI N, SUN L, et al. Rapid operando gas monitor for commercial lithium ion batteries: Gas evolution and relation with electrode materials[J]. Journal of Energy Chemistry, 2022, 72: 14-25.
HUANG Y C, XIAO X L, KANG H F, et al. Thermal management of polymer electrolyte membrane fuel cells: A critical review of heat transfer mechanisms, cooling approaches, and advanced cooling techniques analysis[J]. Energy Conversion and Management, 2022, 254: doi: 10.1016/j.enconman.2022.115221.
WANG H Q, MORANDO S, GAILLARD A, et al. Sensor development and optimization for a proton exchange membrane fuel cell system in automotive applications[J]. Journal of Power Sources, 2021, 487: 10.1016/j.jpowsour.2020.229415.
PENG J, ZHOU X, JIA S H, et al. High precision strain monitoring for lithium ion batteries based on fiber Bragg grating sensors[J]. Journal of Power Sources, 2019, 433: 10.1016/j.jpowsour.2019.226692.
BAE C J, MANANDHAR A, KIESEL P, et al. Monitoring the strain evolution of lithium-ion battery electrodes using an optical fiber Bragg grating sensor[J]. Energy Technology, 2016, 4(7): 851-855.
HUANG J Q, HAN X L, LIU F, et al. Monitoring battery electrolyte chemistry viain-operando tilted fiber Bragg grating sensors [J]. Energy & Environmental Science, 2021, 14(12): 6464-6475.
NASCIMENTO M, NOVAIS S, DING M S, et al. Internal strain and temperature discrimination with optical fiber hybrid sensors in Li-ion batteries[J]. Journal of Power Sources, 2019, 410/411: 1-9.
HUANG J Q, ALBERO BLANQUER L, BONEFACINO J, et al. Operando decoding of chemical and thermal events in commercial Na(Li)-ion cells via optical sensors[J]. Nature Energy, 2020, 5(9): 674-83.
LI Y P, ZHANG Y, LI Z, et al. Operando decoding of surface strain in anode-free lithium metal batteries via optical fiber sensor[J]. Advanced Science, 2022, 9(26): doi.org/10.1002/advs.202203247.
REES G J, SPENCER JOLLY D, NING Z Y, et al. Imaging sodium dendrite growth in all-solid-state sodium batteries using 23Na T2-weighted magnetic resonance imaging[J]. Angewandte Chemie International Edition, 2021, 60(4): 2110-2115.
GUNNARSDóTTIR A B, AMANCHUKWU C V, MENKIN S, et al. Noninvasive insitu NMR study of "dead lithium" formation and lithium corrosion in full-cell lithium metal batteries[J]. Journal of the American Chemical Society, 2020, 142(49): 20814-20827.
DUTOIT C E, TANG M X, GOURIER D, et al. Monitoring metallic sub-micrometric lithium structures in Li-ion batteries by insitu electron paramagnetic resonance correlated spectroscopy and imaging[J]. Nature Communications, 2021, 12(1): 1410.
BHATTACHARYYA R, KEY B, CHEN H L, et al. Insitu NMR observation of the formation of metallic lithium microstructures in lithium batteries [J]. Nature Materials, 2010, 9(6): 504-510.
HSIEH Y-C, LEIßING M, NOWAK S, et al. Quantification of dead lithium viainsitu nuclear magnetic resonance spectroscopy[J]. Cell Reports Physical Science, 2020, 1(8): doi:10.1016/j.xcrp.2020.100139 .
COLUMBUS D, ARUNACHALAM V, GLANG F, et al. Direct detection of lithium exchange across the solid electrolyte interphase by 7Li chemical exchange saturation transfer[J]. Journal of the American Chemical Society, 2022, 144(22): 9836-9844.
KUPERS V, KOLEK M, BIEKER P, et al. Insitu7Li-NMR analysis of lithium metal surface deposits with varying electrolyte compositions and concentrations[J]. Phys Chem Chem Phys, 2019, 21(47): 26084-26094.
LIU M, WANG C, ZHAO C L, et al. Quantification of the Li-ion diffusion over an interface coating in all-solid-state batteries via NMR measurements[J]. Nature Communications, 2021, 12(1): 5943.
MARKER K, XU C, GREY C P. Operando NMR of NMC811/graphite lithium-ion batteries: Structure, dynamics, and lithium metal deposition[J]. Journal of the American Chemical Society, 2020, 142(41): 17447-17456.
JANOSCHKA T, MARTIN N, HAGER M D, et al. An aqueous redox-flow battery with high capacity and power: The TEMPTMA/MV system[J]. Angewandte Chemie International Edition, 2016, 55(46): 14427-14430.
WANDT J, MARINO C, GASTEIGER H A, et al. Operando electron paramagnetic resonance spectroscopy-Formation of mossy lithium on lithium anodes during charge-discharge cycling [J]. Energy & Environmental Science, 2015, 8(4): 1358-1367.
WANG B, LE FEVRE L W, BROOKFIELD A, et al. Resolution of lithium deposition versus intercalation of graphite anodes in lithium ion batteries: An insitu electron paramagnetic resonance study[J]. Angewandte Chemie International Edition, 2021, 60(40): 21860-21867.
LIN F, LIU Y J, YU X Q, et al. Synchrotron X-ray analytical techniques for studying materials electrochemistry in rechargeable batteries[J]. Chemical Reviews, 2017, 117(21): 13123-13186.
SWALLOW J E N, FRASER M W, KNEUSELS N H, et al. Revealing solid electrolyte interphase formation through interface-sensitive operando X-ray absorption spectroscopy[J]. Nature Communications, 2022, 13(1): 6070.
SCHELLENBERGER M, GOLNAK R, QUEVEDO GARZON W G, et al. Accessing the solid electrolyte interphase on silicon anodes for lithium-ion batteries in-situ through transmission soft X-ray absorption spectroscopy[J]. Materials Today Advances, 2022, 14: https://doi.org/10.1016/j.mtadv.2022.100215.
WANDT J, FREIBERG A, THOMAS R, et al. Transition metal dissolution and deposition in Li-ion batteries investigated by operando X-ray absorption spectroscopy[J]. Journal of Materials Chemistry A, 2016, 4(47): 18300-18305.
BAUMGARTEL H. X-Ray absorption-Principles, applications, techniques of EXAFS, SEXAFS and XANES. Von D. Koningsberger und R. Prins. John Wiley & Sons Ltd., Chichester 1988. 673 S., Abb., Tab., Formeln. ISBN 0-471-87547-3[J]. Nachrichten aus Chemie, Technik und Laboratorium, 1988, 36(6): 650.
LI Y Y, EL GABALY F, FERGUSON T R, et al. Current-induced transition from particle-by-particle to concurrent intercalation in phase-separating battery electrodes[J]. Nature Materials, 2014, 13(12): 1149-1156.
SUN G, YU F D, LU M, et al. Surface chemical heterogeneous distribution in over-lithiated Li1+xCoO2 electrodes [J]. Nature Communications, 2022, 13(1): 6464.
LAHTINEN K, LABMAYR M, MÄKELÄ V, et al. Long-term cycling behavior of Mg-doped LiCoO2 materials investigated with the help of laboratory scale X-ray absorption near-edge spectroscopy[J]. Materials Today Energy, 2022, 27: 101040-101053.
SHEARING P R, HOWARD L E, JØRGENSEN P S, et al. Characterization of the 3-dimensional microstructure of a graphite negative electrode from a Li-ion battery[J]. Electrochemistry Communications, 2010, 12(3): 374-377.
BAK S-M, SHADIKE Z, LIN R Q, et al. Insitu/operando synchrotron-based X-ray techniques for lithium-ion battery research [J]. NPG Asia Materials, 2018, 10(7): 563-580.
WANG J J, ENG C, CHEN-WIEGART Y C C, et al. Probing three-dimensional sodiation-desodiation equilibrium in sodium-ion batteries by insitu hard X-ray nanotomography[J]. Nature Communications, 2015, 6: 7496.
BOYCE A M, MARTÍNEZ-PAÑEDA E, WADE A, et al. Cracking predictions of lithium-ion battery electrodes by X-ray computed tomography and modelling[J].Journal of Power Sources, 2022, 526: 10.1016/j.jpowsour.2022.231119.
HOU J W, WU W C, LI L F, et al. Estimation of remaining capacity of lithium-ion batteries based on X-ray computed tomography[J]. Journal of Energy Storage, 2022, 55: .
YU Y S, FARMAND M, KIM C, et al. Three-dimensional localization of nanoscale battery reactions using soft X-ray tomography[J]. Nature Communications, 2018, 9(1): 921.
LIANG G M, DIDIER C, GUO Z P, et al. Understanding rechargeable battery function using inoperando neutron powder diffraction [J]. Adv Mater, 2020, 32(18): e1904528.
WANG C Q, WANG R, HUANG Z Y, et al. Unveiling the migration behavior of lithium ions in NCM/graphite full cell viainoperando neutron diffraction[J]. Energy Storage Materials, 2022, 44: 1-9.
LYU S, VERHALLEN T, VASILEIADIS A, et al. Operando monitoring the lithium spatial distribution of lithium metal anodes[J]. Nature Communications, 2018, 9(1): 2152.
RISSE S, HÄRK E, KENT B, et al. Operando analysis of a lithium/sulfur battery by small-angle neutron scattering[J]. ACS Nano, 2019, 13(9): 10233-10241.
COPLEY R J, CUMMING D, WU Y, et al. Measurements and modelling of the response of an ultrasonic pulse to a lithium-ion battery as a precursor for state of charge estimation[J]. Journal of Energy Storage, 2021, 36: 102406-102421.
HSIEH A G, BHADRA S, HERTZBERG B J, et al. Electrochemical- acoustic time of flight: Inoperando correlation of physical dynamics with battery charge and health[J]. Energy & Environmental Science, 2015, 8(5): 1569-1577.
ZHAO G Q, LIU Y, LIU G, et al. State-of-charge and state-of-health estimation for lithium-ion battery using the direct wave signals of guided wave[J]. Journal of Energy Storage, 2021, 39: 102657-102668.
BOMMIER C, CHANG W, LU Y F, et al. Inoperando acoustic detection of lithium metal plating in commercial LiCoO2/graphite pouch cells[J]. Cell Reports Physical Science, 2020, 1(4): 10.1016/j.xcrp.2020.100035 .
LANG S Y, YU S H, FENG X R, et al. Understanding the lithium-sulfur battery redox reactions via operando confocal Raman microscopy[J]. Nature Communications, 2022, 13(1): 4811.
FONSECA RODRIGUES M T, MARONI V A, GOSZTOLA D J, et al. Lithium acetylide: A spectroscopic marker for lithium deposition during fast charging of Li-ion cells[J]. ACS Applied Energy Materials, 2018, 2(1): 873-881.
NEALE A R, MILAN D C, BRAGA F, et al. Lithium insertion into graphitic carbon observed via operando Kerr-gated Raman spectroscopy enables high state of charge diagnostics[J]. ACS Energy Letters, 2022, 7(8): 2611-2618.
WEI Z, SALEHI A, LIN G Z, et al. Probing Li-ion concentration in an operating lithium ion battery using insitu Raman spectroscopy[J]. Journal of Power Sources, 2020, 449: 10.1016/j.jpowsour.2019.227361.
FENG X N, PAN Y, HE X M, et al. Detecting the internal short circuit in large-format lithium-ion battery using model-based fault-diagnosis algorithm[J]. Journal of Energy Storage, 2018, 18: 26-39.
FENG X N, WENG C H, OUYANG M G, et al. Online internal short circuit detection for a large format lithium ion battery[J]. Applied Energy, 2016, 161: 168-180.
ROMANENKO K, JIN L Y, HOWLETT P, et al. Insitu MRI of operating solid-state lithium metal cells based on ionic plastic crystal electrolytes[J]. Chemistry of Materials, 2016, 28(8): 2844-2851.
CHIEN P H, FENG X Y, TANG M X, et al. Li distribution heterogeneity in solid electrolyte Li10GeP2S12 upon electrochemical cycling probed by 7Li MRI[J]. The Journal of Physical Chemistry Letters, 2018, 9(8): 1990-1998.
FREYTAG A I, PAURIC A D, KRACHKOVSKIY S A, et al. Insitu magic-angle spinning 7Li NMR analysis of a full electrochemical lithium-ion battery using a jelly roll cell design[J]. Journal of the American Chemical Society, 2019, 141(35): 13758-13761.
SONG B H, DHIMAN I, CAROTHERS J C, et al. Dynamic lithium distribution upon dendrite growth and shorting revealed by operando neutron imaging[J]. ACS Energy Letters, 2019, 4(10): 2402-2408.
YAMANAKA T, NAKAGAWA H, TSUBOUCHI S, et al. Insitu diagnosis of the electrolyte solution in a laminate lithium ion battery by using ultrafine multi-probe Raman spectroscopy[J]. Journal of Power Sources, 2017, 359: 435-440.
YAO N, CHEN X, FU Z H, et al. Applying classical, ab initio, and machine-learning molecular dynamics simulations to the liquid electrolyte for rechargeable batteries[J]. Chemical Reviews, 2022, 122(12): 10970-11021.
SUN Y W, YANG T Z, JI H Q, et al. Boosting the optimization of lithium metal batteries by molecular dynamics simulations: A perspective[J]. Advanced Energy Materials, 2020, 10(41): 10.1002/aenm.202002373.
ZHANG H M, WANG J, WANG Y B, et al. Multiscale modeling of the SEI of lithium-ion batteries [J]. Energy Storage Science and Technology, 2023, 12(2): 366-382.
CHEN L, XIA Q, REN Y, et al. Research prospect on reliability of Li-ion battery packs under coupling of multiple physical fields[J]. Energy Storage Science and Technology, 2022, 11(7): 2316-2323.
... [11](NatureCommunications 拥有图片版权)Schematic of the smart battery design and fabrication. (a) Dendrite formation in a traditional lithium battery where complete penetration of the separator by a lithium dendrite is only detected when the battery fails due to an internal short circuit and VLi-Li drops to zero; (b) In comparison, a lithium battery with a bifunctional separator where the overgrown lithium dendrite penetrates into the separator and makes contact with the conducting copper layer, giving rise to a drop in VCu-Li as warning of impending failure due to an internal short circuit. As well as, the full battery remains safely operational with non-zero potential[11] (NatureCommunications has image copyright)Fig. 3
... [15](NatureMaterials 拥有图片版权)Evolution of dendrite growth. (a)—(d) X-ray tomography slices showing the cross-sections of symmetric lithium cells cycled to various stages; (e)—(h) The corresponding 3D reconstructed image[15](NatureMaterials has image copyright)Fig. 14
... [20](JournalofPowerSources 拥有图片版权)Schematic diagram of ultrasonic monitoring technology and analysis of results[20](JournalofPowerSources has image copyright)Fig. 19
... [22](ScientificReports 拥有图片版权)(a) Schematic of customized RTD embedded LIB coin cell; (b) RTD embedded polylactic acid spacer and CR2032 cell with internal RTD. Dimension of the RTD embedded spacer was comparable to ordinary CR2032 coin cell spacer, allowing for reliable sensor-electrode contact and cell sealing after assembly[22](ScientificReports has image copyright)Fig. 4
... [23](EnergyEnvironmentalScience 拥有图片版权)Schematic of the cell embedded with FBG and corresponding electrochemical performance. (a) The schematic illustration of the structure of FBG and the Li-S pouch cell embedded with FBG; (b) The cycle performance and (c) discharge-charge curves of the Li-S pouch cell embedded with FBG and a normal cell without FBG[23] (EnergyEnvironmentalScience has image copyright)Fig. 8
... Euser等[28]为了确定锂离子电池的退化机理和失效机制,他们利用原位拉曼光谱监测工况下锂离子电池循环过程中碳酸酯液体电解质的化学反应(图20).通过从LiNi x Mn y Co1-x-y O2(NMC,x>0.6)-石墨全电池中提取少量电解质{LP57[1.0 mol/L的LiPF6分散于由体积比为3∶7的碳酸乙烯酯(EC)和碳酸甲乙酯(EMC)组成的电解液中]添加了2%的碳酸亚乙烯酯},利用无硅背景拉曼光谱分析电池在第1个电化学循环期间SEI膜形成过程中的各种电解质化学性质,重复取样以便于实时跟踪电池循环过程中电解质化学变化,此外每次测量完光谱后,再将电解质样品注入电池中.为了进一步追踪锂离子溶剂化动力学,该团队将未含有不同浓度锂盐的电解质进行异位光谱测量,实验结果表明,电解液中锂盐浓度和电极-电解质界面化学能改变碳酸盐类溶剂的溶剂化结构,此外在一个充放电循环中,可从EC的C=O峰(1782~1817 cm-1)和EMC的C=O峰(1730~1765 cm-1)的强度比间接跟踪溶剂化环状碳酸盐(Li+—O=C,EC)和溶剂化线性碳酸盐(Li+—O=C,EMC)之间的平衡演变.
... [25](ChemistryofMaterials 拥有图片版权)Noninvasive electron paramagnetic resonance imaging technique monitors changes in lithium deposits inside the battery during charging and discharging[25] (ChemistryofMaterials has image copyright)Fig. 12
... [26](NatureCommunications 拥有图片版权)The schematic depiction of experimental setup of the insitu cell for simultaneous[26](NatureCommunications has image copyright)Fig. 13
... [27](Nature communication 拥有图片版权)Image displays the reconstructed tomograms from neutron and X-ray CT along with examples of sections extracted following virtual unrolling of the reconstructions. Clearly visible in the X-ray images is the nickel current collecting mesh, which appears brighter than the Li X MnO2 active electrode material[27](Naturecommunication has image copyright )Fig. 18
... Jin等[43]首次提出了一种利用H2气体捕集技术检测微米级锂枝晶的方法,用于早期安全预警.这种方法依赖于锂金属和常见电极聚合物黏结剂之间的自发反应产生H2,如聚偏二氟乙烯(PVDF)或苯乙烯丁二烯橡胶(SBR)和羧甲基纤维素(CMC)等.当H2气体通过阀从电池单体中释放出来时,H2气体传感器可以立即捕获到,作为锂金属形成的有效异常指标,进行早期安全预警,所提出的技术在不改变商业锂离子电池结构的情况下工作,并且与现有的电池管理系统兼容.该团队通过对磷酸铁锂-石墨电池组的安全预警实验,表明即使有其他电池组遮挡,也能在早期检测到H2,并且H2在CO、CO2、HCl、HF和SO2气体中最先被捕获,且捕获时间比烟雾早639 s、比火灾早769 s. ...
... [47](EnergyTechnology 拥有图片版权)Schematic diagram and image of FBG sensor attached (a) or implanted (b) on anode electrode of lithium ion battery respectively (c) and (d)[47] (EnergyTechnology has image copyright)Fig. 6
... [48](Energy&EnvironmentalScience 拥有图片版权)(a) Schematics and spectra of a FBG; (b) Schematics and spectra of a TFBG[48] (Energy&EnvironmentalScience has image copyright)Fig. 7
... [51](AdvancedScience 拥有图片版权)The schematic diagram of experimental setup for strain monitoring of anode-free lithium metal batteries[51] (AdvancedScience has image copyright)Fig. 91.2 磁共振技术
... [59](PhysChemChemPhys 拥有图片版权)(a) Schematic depiction of the measurement setup with a thin film pouch cell placed in the coil of a NMR device, connected to a battery cycler; (b) Schematic thin film pouch cell; (c)—(f) 7Li-NMR spectra of thin film pouch cells and SEM images of the corresponding electrodes after (c1—f1) and (c2—f2) 8 hours of electrodeposition at 0.5 mA/cm2 containing 1-PF6-C, 1-TFSI-C, 1-TFSI-E and 3-TFSI-E[59] (PhysChemChemPhys has image copyright)Fig. 10
... Schellenberger等[67]基于具有两个软X射线透明氮化硅(SiN x )膜窗口的微流体电化学半电池研究硅阳极上的SEI,通过控制气泡形成柔软的X射线透明薄电解质层.在循环过程中形成SEI后,他们使用高强度X射线在入口窗口附近产生气泡并从中推出多余的电解质探测体积,气泡稳定地处于膜之间的中心,留在光束路径中的是一层薄薄的电解质,覆盖着SEI和阳极.通过使用低强度的X射线来避免光束损伤,然后利用透射sXAS在硅薄膜阳极上进行原位SEI研究,而不需要拆解电池.实验结果参考化合物的吸收光谱并进行比较,确定了形成物质并研究了SEI的组成,如乙酸锂、乙烯二碳酸锂或乙烯单碳酸锂、乙酰丙酮锂、氢氧化锂和氟化锂,此外还观察到了醛类物质,这可能是源于多孔SEI膜形态中存在的液体夹杂物. ...
... [75](NatureCommunications 拥有图片版权)(a) 3D visualization of Sn particles at the first lithiation-delithiation process; (b) 3D morphological change of Sn particles at the first sodiation-desodiation cycle; (c) Schematic illustration showing the difference of Sn microstructural change in sodium-ion battery and lithium ion battery[75](NatureCommunications has image copyright)Fig. 15