scholarly journals Towards prediction of ordered phases in rechargeable battery chemistry via group–subgroup transformation

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yunbing Ran ◽  
Zheyi Zou ◽  
Bo Liu ◽  
Da Wang ◽  
Bowei Pu ◽  
...  
Keyword(s):  
Transformation Method ◽  
Rechargeable Batteries ◽  
Rechargeable Battery ◽  
Ion Diffusion ◽  
Vacancy Ordering ◽  
Wyckoff Position ◽  
Ordered Phases ◽  
Li Ion ◽  
Mobile Ions ◽  
Theoretical Limitation

AbstractThe electrochemical thermodynamic and kinetic characteristics of rechargeable batteries are critically influenced by the ordering of mobile ions in electrodes or solid electrolytes. However, because of the experimental difficulty of capturing the lighter migration ion coupled with the theoretical limitation of searching for ordered phases in a constrained cell, predicting stable ordered phases involving cell transformations or at extremely dilute concentrations remains challenging. Here, a group-subgroup transformation method based on lattice transformation and Wyckoff-position splitting is employed to predict the ordered ground states. We reproduce the previously reported Li0.75CoO2, Li0.8333CoO2, and Li0.8571CoO2 phases and report a new Li0.875CoO2 ground state. Taking the advantage of Wyckoff-position splitting in reducing the number of configurations, we identify the stablest Li0.0625C6 dilute phase in Li-ion intercalated graphite. We also resolve the Li/La/vacancy ordering in Li3xLa2/3−xTiO3 (0 < x < 0.167), which explains the observed Li-ion diffusion anisotropy. These findings provide important insight towards understanding the rechargeable battery chemistry.

Influence of VC and FEC Additives on Interphase Properties of Carbon in Li-Ion Cells Investigated by Combined EIS & EQCM-D

2020 ◽  
Author(s):  
Paul Kitz ◽  
Matthew Lacey ◽  
Petr Novák ◽  
Erik Berg
Keyword(s):  
Electrochemical Impedance ◽  
Solid Electrolyte Interphase ◽  
Electrochemical Quartz Crystal Microbalance ◽  
Gas Analysis ◽  
Side Reactions ◽  
Ion Diffusion ◽  
Battery Cell ◽  
Anode Passivation ◽  
Order Of Magnitude ◽  
Li Ion

<div>The electrolyte additives vinylene carbonate (VC) and fluoroethylene carbonate (FEC) are well known for increasing the lifetime of a Li-ion battery cell by supporting the formation of an effective solid electrolyte interphase (SEI) at the anode. In this study combined simultaneous electrochemical impedance spectroscopy (EIS) and <i>operando</i> electrochemical quartz crystal microbalance with dissipation monitoring (EQCM-D) are employed together with <i>in situ</i> gas analysis (OEMS) to study the influence of VC and FEC on the passivation process and the interphase properties at carbon-based anodes. In small quantities both additives reduce the initial interphase mass loading by 30 to 50 %, but only VC also effectively prevents continuous side reactions and improves anode passivation significantly. VC and FEC are both reduced at potentials above 1 V vs. Li<sup>+</sup>/Li in the first cycle and change the SEI composition which causes an increase of the SEI shear storage modulus by over one order of magnitude in both cases. As a consequence, the ion diffusion coefficient and conductivity in the interphase is also significantly affected. While small quantities of VC in the initial electrolyte increase the SEI conductivity, FEC decomposition products hinder charge transport through the SEI and thus increase overall anode impedance significantly. </div>

Advanced ordered mesoporous carbon with fast Li-ion diffusion for lithium–selenium sulfide batteries in a carbonate-based electrolyte

Carbon ◽  
2020 ◽  
Vol 170 ◽  
pp. 236-244
Author(s):  
Wonhee Kim ◽  
Jiyeon Lee ◽  
Seungmin Lee ◽  
KwangSup Eom ◽  
Chanho Pak ◽  
...  
Keyword(s):  
Mesoporous Carbon ◽  
Ordered Mesoporous Carbon ◽  
Ion Diffusion ◽  
Ordered Mesoporous ◽  
Li Ion ◽  
Selenium Sulfide

Effect of Li Source on Charge/Discharge Performance of LiFePO4

2012 ◽  
Vol 509 ◽  
pp. 51-55
Author(s):  
Hong Quan Liu ◽  
Fei Xiang Hao ◽  
Yi Jie Gu ◽  
Yun Bo Chen
Keyword(s):  
Low Cost ◽  
Utilization Efficiency ◽  
Ion Diffusion ◽  
Diffusion Distance ◽  
Pore Characteristics ◽  
Discharge Performance ◽  
High Theoretical Capacity ◽  
Low Toxicity ◽  
Li Ion ◽  
Potential Plateau

LiFePO4 has been considered as the most promising positive electrode due to its low cost, high theoretical capacity, stability and low toxicity, all highly required in vehicle applications. In this work, LiFePO4 compound was synthesized by the solid carbothermic reduction reactions with different Li resource. The pure LiFePO4 phase was confirmed for all samples by analysis of the XRD results. The different morphologies were obtained due to different Li resources. The potential plateau of all samples is in the range from 3V to 4V. The sample (LiCO3 as the Li resource) has a higher discharge capacity of 118mAhg−1 at 0.2C 20% greater than that of the sample (LiOH as the Li resource). The reason comes maybe from nano pore characteristics, which reduce Li ion diffusion distance, and increase the utilization efficiency of material.

Synthesis and Electrochemical Properties of Spherical LiFePO4/C Composite via Spray Drying

2015 ◽  
Vol 1120-1121 ◽  
pp. 554-558 ◽  
Author(s):  
Juan Mei Wang ◽  
Bing Ren ◽  
Ying Lin Yan ◽  
Qing Zhang ◽  
Yan Wang
Keyword(s):  
Diffusion Coefficient ◽  
Electrochemical Properties ◽  
Spray Drying ◽  
Electrochemical Impedance ◽  
Retention Rate ◽  
Lithium Ion ◽  
Constant Current ◽  
Ion Diffusion ◽  
Electrochemical Tests ◽  
Li Ion

In this work, spherical LiFePO4/C composite had been synthesized by co-precipitation and spray drying method. The structure, morphology and electrochemical properties of the samples were characterized by X-ray diffraction (XRD), scanning electron micrograph (SEM), transmission electron microscope (TEM), constant current charge-discharge tests and electrochemical impedance spectroscopy (EIS) tests. The spherical LiFePO4/C particles consisted of a number of smaller grains. The results showed that the morphology of LiFePO4/C particles seriously affected the Li-ion diffusion coefficient and electrochemical properties of lithium ion batteries. Electrochemical tests revealed the spherical LiFePO4/C composite had excellent Li-ion diffusion coefficient which was calculated to be 1.065×10-11 cm2/s and discharge capacity of 149 (0.1 C), 139 (0.2 C), 133 (0.5 C), 129 (1 C) and 124 mAhg-1(2 C). After 50 cycles, the capacity retention rate was still 93.5%.

Improved Li-ion diffusion process in TiO2/rGO anode for lithium-ion battery

2017 ◽  
Vol 727 ◽  
pp. 998-1005 ◽  
Author(s):  
Juan Li ◽  
Jianfeng Huang ◽  
Jiayin Li ◽  
Liyun Cao ◽  
Hui Qi ◽  
...  
Keyword(s):  
Diffusion Process ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Ion Diffusion ◽  
Li Ion

Online Estimation of Lithium-Ion Battery Capacity Using Deep Convolutional Neural Networks

2018 ◽  
Author(s):  
Sheng Shen ◽  
M. K. Sadoughi ◽  
Xiangyi Chen ◽  
Mingyi Hong ◽  
Chao Hu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Lithium Ion ◽  
Relevance Vector Machine ◽  
Rechargeable Batteries ◽  
Li Ion Battery ◽  
Deep Convolutional Neural Networks ◽  
Battery Capacity ◽  
Li Ion

Over the past two decades, safety and reliability of lithium-ion (Li-ion) rechargeable batteries have been receiving a considerable amount of attention from both industry and academia. To guarantee safe and reliable operation of a Li-ion battery pack and build failure resilience in the pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method, named deep convolutional neural networks, for cell-level SOH assessment based on the capacity, voltage, and current measurements during a charge cycle. The unique features of deep convolutional neural networks include the local connectivity and shared weights, which enable the model to estimate battery capacity accurately using the measurements during charge. To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the performance of the proposed method. Compared with traditional machine learning methods such as relevance vector machine and shallow neural networks, the proposed method is demonstrated to produce higher accuracy and robustness in capacity estimation.

Li-ion diffusion kinetics in LiMn2O4 thin films prepared by pulsed laser deposition

2008 ◽  
Vol 54 (2) ◽  
pp. 376-381 ◽  
Author(s):  
J. Xie ◽  
K. Kohno ◽  
T. Matsumura ◽  
N. Imanishi ◽  
A. Hirano ◽  
...  
Keyword(s):  
Thin Films ◽  
Pulsed Laser Deposition ◽  
Pulsed Laser ◽  
Laser Deposition ◽  
Diffusion Kinetics ◽  
Ion Diffusion ◽  
Limn2o4 Thin Films ◽  
Li Ion
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