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Comparison of equivalent circuit and machine learning methods for CubeSat battery discharge modeling

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Published in:Радіоелектронні і комп'ютерні системи
Format: Online Article RSS Article
Published: 2025
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container_title Радіоелектронні і комп'ютерні системи
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:6808
institution FRELIP
journal_source_facet Радіоелектронні і комп'ютерні системи
publishDate 2025
publishDateSort 2025
record_format rss_article
spellingShingle Comparison of equivalent circuit and machine learning methods for CubeSat battery discharge modeling
— — — — — Computer Engineering
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display — — — — — Computer Engineering
Computer Science & IT
Engineering & Technology
— — — — — Computer Engineering
Computer Science & IT
Engineering & Technology
subject_facet — — — — — Computer Engineering
Computer Science & IT
Engineering & Technology
title Comparison of equivalent circuit and machine learning methods for CubeSat battery discharge modeling
title_auth Comparison of equivalent circuit and machine learning methods for CubeSat battery discharge modeling
title_full Comparison of equivalent circuit and machine learning methods for CubeSat battery discharge modeling
title_fullStr Comparison of equivalent circuit and machine learning methods for CubeSat battery discharge modeling
title_full_unstemmed Comparison of equivalent circuit and machine learning methods for CubeSat battery discharge modeling
title_short Comparison of equivalent circuit and machine learning methods for CubeSat battery discharge modeling
title_sort comparison of equivalent circuit and machine learning methods for cubesat battery discharge modeling
topic — — — — — Computer Engineering
Computer Science & IT
Engineering & Technology
url https://nti.khai.edu/ojs/index.php/reks/article/view/reks.2025.3.16