Learning Optimal Forms of Constitutive Relations Characterizing Ion Intercalation from Data in Mathematical Models of Lithium-Ion Batteries-Reference-Cited by-同舟云学术

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Learning Optimal Forms of Constitutive Relations Characterizing Ion Intercalation from Data in Mathematical Models of Lithium-Ion Batteries

Published:2023-08-25 Issue:35 Volume:127 Page:17508-17523
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Daniels Lindsey¹,Sahu Smita²³^ORCID,Sanders Kevin J.⁴,Goward Gillian R.⁴^ORCID,Foster Jamie M.²³,Protas Bartosz¹^ORCID

Affiliation:

1. Department of Mathematics and Statistics, McMaster University, Hamilton L8S 4K1, Ontario, Canada

2. The Faraday Institution, Quad One, Becquerel Avenue, Harwell Campus, Didcot OX11 0RA, U.K.

3. School of Mathematics and Physics, University of Portsmouth, Lion Terrace, Portsmouth, Hampshire PO1 2UP, U.K.

4. Department of Chemistry, McMaster University, Hamilton L8S 4L8, Ontario, Canada

Funder

Faraday Institution

Natural Sciences and Engineering Research Council of Canada

Publisher

American Chemical Society (ACS)

Subject

Surfaces, Coatings and Films,Physical and Theoretical Chemistry,General Energy,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.3c02915

Reference71 articles.

1. Review of parameterisation and a novel database (LiionDB) for continuum Li-ion battery models

2. Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries

3. An accurate machine-learning calculator for optimization of Li-ion battery cathodes

4. State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

5. Quantifying Mass Transport during Polarization in a Li Ion Battery Electrolyte by in Situ 7Li NMR Imaging

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