print

Modeling battery energy storage systems based on remaining useful lifetime through regression algorithms and binary classifiers

author
Gilbert Zequera, Rolando Antonio
Rjabtšikov, Viktor
Rassõlkin, Anton
Vaimann, Toomas
Kallaste, Ants
statement of authorship
Rolando Gilbert Zequera, Viktor Rjabtšikov, Anton Rassõlkin,Toomas Vaimann and Ants Kallaste
source
Applied sciences
publisher
MDPI
journal volume number month
vol. 13, 13
year of publication
2023
pages
art. 7597
url
https://doi.org/10.3390/app13137597
subject term
täitematerjalid
heitmed
akumulaatorid
patareid
maht
energiasalvestus
keyword
charge
discharge
battery
capacity
classifier
regression
ISSN
2076-3417
notes
Bibliogr.: 14 ref
Open Access
Open Access
scientific publication
teaduspublikatsioon
classifier
1.1
Scopus
Journal metrics at Scopus
Article at Scopus
WOS
Journal metrics at WOS
Article at WOS
category (general)
Engineering
Physics and astronomy
Chemical engineering
Computer science
Materials science
category (sub)
Engineering. General engineering
Physics and astronomy. Instrumentation
Chemical engineering. Fluid flow and transfer processes
Computer science. Computer science applications
Materials science. General materials science
Chemical engineering. Process chemistry and technology
kvartiil
Q2
TTÜ department
elektroenergeetika ja mehhatroonika instituut
language
inglise
Uurimisrühm
Electrical machines research group
Gilbert Zequera, R., Rjabtšikov, V., Rassõlkin, A., Vaimann, T., Kallaste, A. Modeling battery energy storage systems based on remaining useful lifetime through regression algorithms and binary classifiers // Applied sciences (2023) vol. 13, 13, art. 7597. https://doi.org/10.3390/app13137597