Determination of the Lithium Ion Diffusion Coefficient in Graphite

Pei, Yu; Popov, Branko N.; Ritter, James A.; White, Ralph E.

doi:10.1149/1.1391556

Cited by 346 publications

(265 citation statements)

References 23 publications

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“…The curved diffusion pathway was chosen for Li in LiC 6 and Na in NaC 6 since it leads to a higher diffusion coefficient compared to the straight pathway, which will be discussed further below. Experimental diffusivity data [16][17][18][19][20][21][22][23][24] are included for comparison.…”

Section: Jumping Frequency and Diffusion Coefficientmentioning

confidence: 99%

“…18 The error bars show the data measured by electrochemical methods including PITT, 19 GITT 20 and EIS. 21,22 All the electrochemical data were measured at room temperature. The positions of the error bars are shifted to improve the readability.…”

Section: Jumping Frequency and Diffusion Coefficientmentioning

confidence: 99%

“…potentiostatic intermittent titration technique (PITT), 19 galvanostatic intermittent titration technique (GITT) 20 and electrochemical impedance spectroscopy (EIS), 21,22 are scattered over a relatively large range from 1.2⋅10 -15 to 1.7⋅10 -12 m 2 /s. The data from electrochemical methods are scafttered because the measurements cover a wide range of the state of charge (SOC): the concentration of Li in graphite varies during the measurements.…”

Section: Jumping Frequency and Diffusion Coefficientmentioning

confidence: 99%

“…Quasielastic Neutron Scattering (QENS) 16,17 and nuclear magnetic resonance (NMR) 18 have been used to derive the diffusion coefficients of Li in LiC 6 and K in KC 8 . Electrochemical methods, including potentiostatic intermittent titration technique (PITT), 19 galvanostatic intermittent titration technique (GITT) 20 and electrochemical impedance spectroscopy (EIS), 21,22 have also been employed to determine the diffusion coefficient of Li in graphite anode materials. The diffusion coefficient of Na in graphite has been measured by Rapopport test 23 and thermogravimetric analysis of sodium uptake in graphite.…”

Section: Introductionmentioning

confidence: 99%

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Diffusion of alkali metals in the first stage graphite intercalation compounds by vdW-DFT calculations

et al. 2015

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Diffusion of alkali metal cations in the first stage graphite intercalation compounds (GIC) LiC 6 , NaC 6 , NaC 8 and KC 8 has been investigated with density functional theory (DFT) calculations using the optPBE-vdW van der Waals functional. The formation energies of alkali vacancies, interstitials and Frenkel defects were calculated and vacancies were found to be the dominating point defects. The diffusion coefficients of the alkali metals in GIC were evaluated by a hopping model of point defects where the energy barriers for vacancy diffusion were derived from transition state theory. For LiC 6 , NaC 6 , NaC 8 and KC 8 , respectively, the diffusion coefficients were found to be 1.5⋅10 -15 , 2.8⋅10 -12 , 7.8⋅10 -13 and 2.0⋅10 -10 m 2 s -1 at room temperature, which is within the range of available experimental data. For LiC 6 and NaC 6 a curved vacancy migration path is the most energetically favourable, while a straight pathway was inferred for NaC 8 and KC 8 . The diffusion coefficients for alkali metal vacancy diffusion in first stage GICs scales with the graphene interlayer spacing: LiC 6 << NaC 8 < NaC 6 << KC 8 .

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Section: Jumping Frequency and Diffusion Coefficientmentioning

confidence: 99%

Section: Jumping Frequency and Diffusion Coefficientmentioning

confidence: 99%

Section: Jumping Frequency and Diffusion Coefficientmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diffusion of alkali metals in the first stage graphite intercalation compounds by vdW-DFT calculations

et al. 2015

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“…This model, due to its complexity, is not suitable for a model based estimation method such as Kalman filter. Not to mention that the nyquist plot can change a lot due to change of ambient temperature (Yu et al, 1999), SOC and external disturbances. …”

Section: Background Information On Battery Modelmentioning

confidence: 99%

SOC estimation algorithm for Li-ion battery in electric or hybrid vehicles

Wang

2016

IJEHV

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As over-charging and over-discharging can cause damage to batteries, to guarantee the stability and duration of batteries, it is necessary to avoid being over-charged and over-discharged. Therefore, knowing the batteries' state of charge (SOC) is essential. In this paper, the author describes in detail the procedure of building a SOC estimation algorithm. The SOC estimation algorithm is based on unscented Kalman filter. Moreover, changes of battery capacity and internal resistance are considered. The impact of these changes on the estimation accuracy is explored. Experiments on an Iron Phosphate Li-ion battery cell with real load were conducted to justify this method. This method was verified to be not only precise but also stable.

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Determination of the Diffusion Coefficient of Lithium ions in Graphite Coated with Polymer‐Derived SiCN Ceramic

Nowak

Graczyk‐Zajac

Riedel

2010

Ceramic Transactions Series

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The diffusion coefficients of lithium ions (Du+) in graphite/SiCN ceramic composites, pyrolyzed at temperatures between 850°C and 1300°C, were determined by cyclic voltammetry (CV), galvanostatic intermittent titration technique (GITT) and electrochemical impedance spectroscopy (EIS). The diffusion coefficient values calculated by these techniques are of the same order of magnitude. Depending on the potential the values vary from 10" 15 to 10" 08 cm 2 /s. Electrochemical measurements show that D L ¡+ depends on the material composition, pyrolysis temperature and charge/discharge state. For composites of graphite to ceramic ratio 50:50 the diffusion coefficient is constant and not temperature dependent. The composite materials of 75:25 ratio pyrolyzed at different temperatures exhibit an increase in the Du+ with the highest diffusion coefficient of lithium ions of the order ~ 10" 10 cm 2 /s for material pyrolyzed at 1300 °C. Cyclic voltammetry curves demonstrate that lithium ions intercalate into graphite and free carbon segregated from the SiCN phase. The presence of SiCN improves diffusion transport of lithium ions into the graphite and the derived free carbon. These findings strongly indicate that the SiCN ceramic network creates a pathway for lithium ion transfer.

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Determination of the Lithium Ion Diffusion Coefficient in Graphite

Cited by 346 publications

References 23 publications

Diffusion of alkali metals in the first stage graphite intercalation compounds by vdW-DFT calculations

Diffusion of alkali metals in the first stage graphite intercalation compounds by vdW-DFT calculations

SOC estimation algorithm for Li-ion battery in electric or hybrid vehicles

Determination of the Diffusion Coefficient of Lithium ions in Graphite Coated with Polymer‐Derived SiCN Ceramic

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