Data of physical and electrochemical characteristics of calendered NMC622 electrodes and lithium-ion cells at pilot-plant battery manufacturing.

Battery cycling Calendering process Electrochemical characteristics Electrode manufacturing Lithium-ion battery Machine learning Pilot-plant SEM/EDS images

Journal

Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995

Informations de publication

Date de publication:
Feb 2024
Historique:
received: 25 07 2023
revised: 03 11 2023
accepted: 06 11 2023
medline: 11 12 2023
pubmed: 11 12 2023
entrez: 11 12 2023
Statut: epublish

Résumé

The data reported here was prepared to study the effects of calendering process on NMC622 cathodes using a 3-3-2 full factorial design of experiments. The data set consists of 18 unique combinations of calender roll temperature (85 °C, 120 °C, or 145 °C), electrode porosity (30%, 35%, or 40%), and electrode mass loading (120 g/m² or 180 g/m²). The reported physical characteristics of the electrodes include thickness, coating weight, maximum tensile strength, and density. The electrochemical performances of the electrodes were obtained by testing coin cells. In this context, 54 half-cells were produced, 3 per each calendering experiment to ensure repeatability and reliability of the results. The responses of interest included, charge energy capacity at C/2, C/5, discharge energy capacity at C/20, C/5, C/2, C, 2C, 5C, 10C, gravimetric capacity (charge at C/2, C/5, discharge at C/20, C/5, C/2, C, 2C, 5C, 10C), volumetric capacity (charge at C/2, C/5, discharge at C/20, C/5, C/2, C, 2C, 5C, 10C), rate performance (5C:0.2C), area specific impedance (at 10% to 90% state of charge (SoC) in 10 breakpoints), long-term cycling capacity (charge at C/5 for 50 cycles, discharge at C/2 for 50 cycles), long-term cycling degradation (at C/2 during 50 cycles of charge and discharge), and cycling columbic efficiency (50 cycles of C/2 charge and discharge). The details of the experimental design that has led to this data as well as comprehensive statistical analysis, and machine learning-based models can be found in the recently published manuscripts by Hidalgo et al. and Faraji-Niri et al. [1,2].

Identifiants

pubmed: 38076480
doi: 10.1016/j.dib.2023.109798
pii: S2352-3409(23)00860-0
pmc: PMC10708988
doi:

Types de publication

Journal Article

Langues

eng

Pagination

109798

Informations de copyright

© 2023 The Authors.

Auteurs

Mona Faraji-Niri (M)

Warwick Manufacturing Group, University of Warwick, CV4 7AL Coventry, UK.
The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot, UK.

Marc Fransic V Hidalgo (MFV)

Warwick Manufacturing Group, University of Warwick, CV4 7AL Coventry, UK.
The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot, UK.

Geanina Apachitei (G)

Warwick Manufacturing Group, University of Warwick, CV4 7AL Coventry, UK.
The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot, UK.

Daniela Dogaru (D)

Warwick Manufacturing Group, University of Warwick, CV4 7AL Coventry, UK.
The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot, UK.

Michael Lain (M)

Warwick Manufacturing Group, University of Warwick, CV4 7AL Coventry, UK.
The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot, UK.

Mark Copley (M)

Warwick Manufacturing Group, University of Warwick, CV4 7AL Coventry, UK.
The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot, UK.

James Marco (J)

Warwick Manufacturing Group, University of Warwick, CV4 7AL Coventry, UK.
The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot, UK.

Classifications MeSH