Bayesian uncertainty quantification in inverse modeling of electrochemical systems-Reference-Cited by-同舟云学术

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Bayesian uncertainty quantification in inverse modeling of electrochemical systems

Published:2018-12-28 Issue:5 Volume:40 Page:740-752
ISSN:0192-8651
Container-title:Journal of Computational Chemistry
language:en
Short-container-title:J. Comput. Chem.

Author:

Sethurajan Athinthra¹,Krachkovskiy Sergey²,Goward Gillian²,Protas Bartosz³^ORCID

Affiliation:

1. School of Computational Science & Engineering; McMaster University; Hamilton Ontario L8S 4K1 Canada

2. Department of Chemistry and Chemical Biology; McMaster University; Hamilton Ontario L8S 4K1 Canada

3. Department of Mathematics and Statistics; McMaster University; Hamilton Ontario L8S 4K1 Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Wiley

Subject

Computational Mathematics,General Chemistry

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/jcc.25759/fullpdf

Reference29 articles.

1. Li-ion batteries and portable power source prospects for the next 5–10 years

2. A review on the key issues for lithium-ion battery management in electric vehicles

3. Inverse Problem Theory and Methods for Model Parameter Estimation

4. Determination of the Lithium Ion Diffusion Coefficient in Graphite

5. Determination of the chemical diffusion coefficient of lithium in LiFePO4

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