Mesopores inside electrode particles can change the Li-ion transport mechanism and diffusion-induced stress

Harris, Stephen J.; Deshpande, Rutooj D.; Qi, Yue; Dutta, Indrajit; Cheng, Yang‐Tse

doi:10.1557/jmr.2010.0183

Cited by 83 publications

(80 citation statements)

References 24 publications

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“…In order to facilitate X-ray tomography studies, the Cu layer was removed by dissolution in nitric acid -this dissolution process is also expected to remove any residual electrolyte material. EDS and SEM images of the electrode composite show (see Harris et al (2010)) that the graphite particles are typically 10 to 15 microns in diameter. Since the electrode is only about 60 microns thick, the electrode is unlikely to be structurally homogeneous, but inherent statistical deviations in microstructure can occur.…”

Section: Methodsmentioning

confidence: 99%

Stochastic simulation model for the 3D morphology of composite materials in Li–ion batteries

Thiedmann¹,

Stenzel²,

Spettl³

et al. 2011

Computational Materials Science

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Battery technology plays an important role in energy storage. In particular, lithiumion (Li-ion) batteries are of great interest, because of their high capacity, long cycle life, and high energy and power density. However, for further improvements of Li-ion batteries, a deeper understanding of physical processes occurring within this type of battery, including transport, is needed. To provide a detailed description of these phenomena, a 3D representation is required for the morphology of composite materials used in Li-ion batteries. In this paper, we develop a stochastic simulation model in 3D, which is based on random marked point processes, to reconstruct real and generate virtual morphologies. For this purpose, a statistical technique to fit the model to 3D image data gained by X-ray tomography is developed. Finally, we validate the model by comparing real and simulated data using image characteristics which are especially relevant with respect to transport properties.

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Section: Methodsmentioning

confidence: 99%

Stochastic simulation model for the 3D morphology of composite materials in Li–ion batteries

Thiedmann¹,

Stenzel²,

Spettl³

et al. 2011

Computational Materials Science

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“…7c). The presence of the surface defects such as cracks and pores, both quantitatively and qualitatively, changes the internal stress distributions [41] such that the struts are prone to internal and surface cracking during battery assembly.…”

Section: Mechanical Properties Of Nanogravel Structured Nio/ni Electrmentioning

confidence: 99%

Nanogravel structured NiO/Ni foam as electrode for high-performance lithium-ion batteries

Rahman

Wen

2015

Ionics

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Nanogravel structured NiO/Ni electrodes were fabricated by using two-step thermal oxidation method of commercial nickel (Ni) foam in air for lithium-ion batteries (LIBs). The macro-and micro-structures of the NiO/Ni foam were characterized using X-ray diffraction (XRD), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDX), and Raman spectroscopy. Galvanostatic tests revealed that the electrode exhibits no obvious capacity fading over 40 cycles at 1 C (718 mAg −1 ) and 2.5 C (1.8 Ag −1 ) current rate. The discharge capacity was higher than the theoretical capacity of NiO even at a highcurrent rate of 2.5 C. The electrodes can deliver a reversible capacity of 1116.65 mAh g −1 after 20th cycle at 1 C rate and 1026.20 mAh g −1 after 40th cycle at 2.5 C rate. The cyclic voltammograms and impedance spectra analysis indicated that a redox reaction of NiO-Ni 0 with formation and decomposition of Li 2 O. The excellent electrochemical performance is mainly attributed to the nanogravel structure of the NiO/Ni foam electrodes as well as its excellent electrical contact between NiO and Ni. The unique nanostructured NiO on the highly conductive metallic Ni in core resulted in the enhanced discharge capacity, coulombic efficiency, cyclic stability, and rate capability when utilized as negative electrodes in LIBs.

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“…with a similar pressure as in the closed cell. We note that this is not the case for crosssections of single electrodes, 18,19,74,96,97 which might have expanded after separation of the cell components.…”

mentioning

confidence: 81%

“…Additionally, if the sample surface is scanned by the electron beam, maps of the chemical composition on the surface can be created by superposition with SEM images (EDX mapping). 39,95,97,119,149,150 However, EDX presents a major drawback since it is not able to detect Li. Therefore, complementary methods are necessary for its detection and quantification.…”

Section: 121mentioning

confidence: 99%

Review—Post-Mortem Analysis of Aged Lithium-Ion Batteries: Disassembly Methodology and Physico-Chemical Analysis Techniques

Waldmann

Iturrondobeitia

Kasper

et al. 2016

J. Electrochem. Soc.

235

149

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Improvement of life-time is an important issue in the development of Li-ion batteries. Aging mechanisms limiting the life-time can efficiently be characterized by physico-chemical analysis of aged cells with a variety of complementary methods. This study reviews the state-of-the-art literature on Post-Mortem analysis of Li-ion cells, including disassembly methodology as well as physicochemical characterization methods for battery materials. A detailed scheme for Post-Mortem analysis is deduced from literature, including pre-inspection, conditions and safe environment for disassembly of cells, as well as separation and post-processing of components. Special attention is paid to the characterization of aged materials including anodes, cathodes, separators, and electrolyte. More specifically, microscopy, chemical methods sensitive to electrode surfaces or to electrode bulk, and electrolyte analysis are reviewed in detail. The techniques are complemented by electrochemical measurements using reconstruction methods for electrodes built into half and full cells with reference electrode. The changes happening to the materials during aging as well as abilities of the reviewed analysis methods to observe them are critically discussed. Li-ion batteries are currently used in everyday objects such as smart-phones, power tools and tablet computers as well as in the growing fields of light electric vehicles (LEVs), unmanned aerial vehicles (UAVs), battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). [1][2][3][4] Furthermore, the rise of renewable energy sources like wind and solar power, which are only intermittently available, demands reliable and highly flexible stationary energy storage solutions, which provide high capacities and predictable life-times. 2,5Aging of Li-ion batteries is a general problem for manufacturers as they have to guarantee long-term reliability of their products. For state-of-the-art cells, degradation effects on the material level lead to capacity fade and resistance increase on the cell level. The aging state of a battery is often characterized by the state-of-health (SOH) in % according to 3,16,22,[29][30][31] SO H(t) = discharge capacity (t) discharge capacity (t = 0)[1]where t represents the aging time. In general, one has to differentiate between cycling 7,16,18,21,[23][24][25]32 and calendar aging. 7,19,[21][22][23][24]27 Since commercial Li-ion cells can be subject to calendar aging in the time between manufacturing and delivery, it is good practice to measure the discharge capacity at t = 0 for each cell that undergoes an aging test. Since the discharge capacity depends mainly on temperature, depth-of-discharge (DOD), and discharge current, the SOH is usually monitored by regular check-ups with defined parameter sets, 7,16,21,23,24 which can vary depending on the application. Typically, a temperature of 25• C, 16,22,24 DOD of 100%, 16,21 and discharge rates of 1C 7,16,21,22,24 or lower 23 are used in check-ups. The performance dec...

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Mesopores inside electrode particles can change the Li-ion transport mechanism and diffusion-induced stress

Cited by 83 publications

References 24 publications

Stochastic simulation model for the 3D morphology of composite materials in Li–ion batteries

Stochastic simulation model for the 3D morphology of composite materials in Li–ion batteries

Nanogravel structured NiO/Ni foam as electrode for high-performance lithium-ion batteries

Review—Post-Mortem Analysis of Aged Lithium-Ion Batteries: Disassembly Methodology and Physico-Chemical Analysis Techniques

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