LIBAC: An Annotated Corpus for Automated “Reading” of the Lithium-Ion Battery Research Literature-Reference-Cited by-同舟云学术

LIBAC: An Annotated Corpus for Automated “Reading” of the Lithium-Ion Battery Research Literature

Published:2023-02-21 Issue:5 Volume:35 Page:1849-1857
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

El-Bousiydy Hassna¹²³,Troncoso Javier F.¹⁴,Johansson Patrik²³^ORCID,Franco Alejandro A.¹²⁴⁵^ORCID

Affiliation:

1. Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l’Energie, 15 rue Baudelocque, 80039 Amiens Cedex, France

2. ALISTORE-European Research Institute, FR CNRS 3104, Hub de l’Energie, 15 rue Baudelocque, 80039 Amiens Cedex, France

3. Department of Physics, Chalmers University of Technology, 41296 Göteborg, Sweden

4. Réseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459, Hub de l’Energie, 15 rue Baudelocque, 80039 Amiens Cedex, France

5. Institut Universitaire de France, 103 boulevard Saint Michel, 75005 Paris, France

Funder

Energimyndigheten

VINNOVA

Institut Universitaire de France

H2020 European Research Council

ALISTORE European Research Institute

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.2c01356

Reference53 articles.

1. Artificial Intelligence Applied to Battery Research: Hype or Reality?

2. Crack detection in lithium-ion cells using machine learning

3. A Framework for Optimal Safety Li-ion Batteries Design using Physics-Based Models and Machine Learning Approaches

4. An efficient screening method for retired lithium-ion batteries based on support vector machine

5. Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression

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