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Computational understanding of Li-ion batteries

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Urban_2016_CM_Li_ionbatteries_CC.pdf (1.974Mb)
Date
18/03/2016
Author
Urban, Alexander
Seo, Dong-Hwa
Ceder, Gerbrand
Keywords
QD Chemistry
QA75 Electronic computers. Computer science
Materials Science(all)
Computer Science Applications
Modelling and Simulation
Mechanics of Materials
SDG 7 - Affordable and Clean Energy
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Abstract
Over the last two decades, computational methods have made tremendous advances, and today many key properties of lithium-ion batteries can be accurately predicted by first principles calculations. For this reason, computations have become a cornerstone of battery-related research by providing insight into fundamental processes that are not otherwise accessible, such as ionic diffusion mechanisms and electronic structure effects, as well as a quantitative comparison with experimental results. The aim of this review is to provide an overview of state-of-the-art ab initio approaches for the modelling of battery materials. We consider techniques for the computation of equilibrium cell voltages, 0-Kelvin and finite-temperature voltage profiles, ionic mobility and thermal and electrolyte stability. The strengths and weaknesses of different electronic structure methods, such as DFT+U and hybrid functionals, are discussed in the context of voltage and phase diagram predictions, and we review the merits of lattice models for the evaluation of finite-temperature thermodynamics and kinetics. With such a complete set of methods at hand, first principles calculations of ordered, crystalline solids, i.e., of most electrode materials and solid electrolytes, have become reliable and quantitative. However, the description of molecular materials and disordered or amorphous phases remains an important challenge. We highlight recent exciting progress in this area, especially regarding the modelling of organic electrolytes and solid-electrolyte interfaces.
Citation
Urban , A , Seo , D-H & Ceder , G 2016 , ' Computational understanding of Li-ion batteries ' , npj Computational Materials , vol. 2 , 16002 . https://doi.org/10.1038/npjcompumats.2016.2
Publication
npj Computational Materials
Status
Peer reviewed
DOI
https://doi.org/10.1038/npjcompumats.2016.2
ISSN
2057-3960
Type
Journal item
Rights
© 2016 the Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Description
This work was supported primarily by the U.S. Department of Energy (DOE) under Contract No. DE-FG02-96ER45571.
Collections
  • University of St Andrews Research
URI
http://hdl.handle.net/10023/12860

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