scholarly journals Modelling capacity fade in silicon-graphite composite electrode for lithium-ion batteries

2021 ◽  
pp. 138067
Author(s):  
Shweta Dhillon ◽  
Guiomar Hernández ◽  
Nils P. Wagner ◽  
Ann Mari Svensson ◽  
Daniel Brandell
2011 ◽  
Vol 79 (1) ◽  
pp. 6-9 ◽  
Author(s):  
Shinichi KOMABA ◽  
Tomoaki OZEKI ◽  
Naoaki YABUUCHI ◽  
Keiji SHIMOMURA

2017 ◽  
Vol 5 (7) ◽  
pp. 6343-6355 ◽  
Author(s):  
Takahiro Mochizuki ◽  
Shoko Aoki ◽  
Tatsuo Horiba ◽  
Martin Schulz-Dobrick ◽  
Zhen-Ji Han ◽  
...  

Author(s):  
ICHIRO ARISE ◽  
Yuto Miyahara ◽  
Kohei Miyazaki ◽  
Takeshi Abe

Abstract The separator is an essential important key material in lithium-ion batteries (LIBs) because it is in contact with the positive and negative electrodes and the electrolyte. Aramid coated separators (ACS) are widely used in automotive and consumer batteries as high-performance separators for LIBs with high safety and excellent lifetime characteristics. Although much effort has been made to improve the electrolyte composition, the lithium deposition on the surface of the graphite electrode at low temperature and the high charge rate is still an unsolved problem in LIBs. In this work, lithium metal is used as a counter electrode, and a separator was placed between lithium metal and graphite composite electrode. The lithium was deposited on the surface of the graphite composite electrode through the separator. Then, the functional role of ACS in the initial deposition process was investigated. The dendrite blocking effect of ACS was studied by the observation of dendrite growth and pulse cycle performance.


2015 ◽  
Vol 162 (12) ◽  
pp. A2245-A2249 ◽  
Author(s):  
Shoko Aoki ◽  
Zhen-Ji Han ◽  
Kiyofumi Yamagiwa ◽  
Naoaki Yabuuchi ◽  
Masahiro Murase ◽  
...  

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 723
Author(s):  
Saurabh Saxena ◽  
Darius Roman ◽  
Valentin Robu ◽  
David Flynn ◽  
Michael Pecht

Lithium-ion batteries power numerous systems from consumer electronics to electric vehicles, and thus undergo qualification testing for degradation assessment prior to deployment. Qualification testing involves repeated charge–discharge operation of the batteries, which can take more than three months if subjected to 500 cycles at a C-rate of 0.5C. Accelerated degradation testing can be used to reduce extensive test time, but its application requires a careful selection of stress factors. To address this challenge, this study identifies and ranks stress factors in terms of their effects on battery degradation (capacity fade) using half-fractional design of experiments and machine learning. Two case studies are presented involving 96 lithium-ion batteries from two different manufacturers, tested under five different stress factors. Results show that neither the individual (main) effects nor the two-way interaction effects of charge C-rate and depth of discharge rank in the top three significant stress factors for the capacity fade in lithium-ion batteries, while temperature in the form of either individual or interaction effect provides the maximum acceleration.


Author(s):  
Honglei Li ◽  
Liang Cong ◽  
Huazheng Ma ◽  
Weiwei Liu ◽  
Yelin Deng ◽  
...  

Abstract The rapidly growing deployment of lithium-ion batteries in electric vehicles is associated with a great waste of natural resource and environmental pollution caused by manufacturing and disposal. Repurposing the retired lithium-ion batteries can extend their useful life, creating environmental and economic benefits. However, the residual capacity of retired lithium-ion batteries is unknown and can be drastically different owing to various working history and calendar life. The main objective of this paper is to develop a fast and accurate capacity estimation method to classify the retired batteries by the remaining capacity. The hybrid technique of adaptive genetic algorithm and back propagation neural network is developed to estimate battery remaining capacity using the training set comprised of the selected characteristic parameters of incremental capacity curve of battery charging. Also, the paper investigated the correlation between characteristic parameters with capacity fade. The results show that capacity estimation errors of the proposed neural network are within 3%. Peak intensity of the incremental capacity curve has strong correlation with capacity fade. The findings also show that the translation of peak of the incremental capacity curve is strongly related with internal resistance.


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