scholarly journals Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning

ACS Omega ◽  
2018 ◽  
Vol 3 (7) ◽  
pp. 7868-7874 ◽  
Author(s):  
Yasuharu Okamoto ◽  
Yoshimi Kubo
Keyword(s):  
Machine Learning ◽  
Ab Initio Calculations ◽  
Ab Initio ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Redox Potentials

Ab initio calculations of lithium titanates related to anodes of lithium-ion batteries

2020 ◽  
Vol 141 ◽  
pp. 109405 ◽  
Author(s):  
Sebastián Amaya-Roncancio ◽  
Luis Reinaudi ◽  
Susana Chauque ◽  
Fabiana Y. Oliva ◽  
Osvaldo R. Cámara ◽  
...  
Keyword(s):  
Ab Initio Calculations ◽  
Ab Initio ◽  
Lithium Ion Batteries ◽  
Lithium Ion

Fluorinated Boroxin-Based Anion Receptors for Lithium Ion Batteries: Fluoride Anion Binding, Ab Initio Calculations, and Ionic Conductivity Studies

2009 ◽  
Vol 113 (20) ◽  
pp. 5918-5926 ◽  
Author(s):  
Nanditha G. Nair ◽  
Mario Blanco ◽  
William West ◽  
F. Christoph Weise ◽  
Steve Greenbaum ◽  
...  
Keyword(s):  
Ionic Conductivity ◽  
Ab Initio Calculations ◽  
Ab Initio ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Anion Binding ◽  
Fluoride Anion ◽  
Anion Receptors

Structural and electrochemical properties of Mg-doped nickel based cathode materials LiNi0.6Co0.2Mn0.2−xMgxO2 for lithium ion batteries

RSC Advances ◽  
2015 ◽  
Vol 5 (108) ◽  
pp. 88773-88779 ◽  
Author(s):  
Zhenjun Huang ◽  
Zhixing Wang ◽  
Xiaobo Zheng ◽  
Huajun Guo ◽  
Xinhai Li ◽  
...  
Keyword(s):  
Ab Initio Calculations ◽  
Electrochemical Properties ◽  
Ab Initio ◽  
Lithium Ion Batteries ◽  
Cathode Materials ◽  
Lithium Ion ◽  
The Impact ◽  
Mg Doped

Combined with experiments and ab initio calculations, we investigated the impact of the substitution of Mn with Mg in LiNi0.6Co0.2Mn0.2O2.

scholarly journals Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 723
Author(s):  
Saurabh Saxena ◽  
Darius Roman ◽  
Valentin Robu ◽  
David Flynn ◽  
Michael Pecht
Keyword(s):  
Machine Learning ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Consumer Electronics ◽  
Stress Factors ◽  
Test Time ◽  
Capacity Fade ◽  
Maximum Acceleration ◽  
Accelerated Degradation ◽  
Qualification Testing

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.

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