@MastersThesis{Donato:2018:MaLeSy,
author = "Donato, Thiago Henrique Rizzi",
title = "Machine learning systems applied in satellite lithium-ion battery
set impedance estimation",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "2018",
address = "S{\~a}o Jos{\'e} dos Campos",
month = "2018-04-02",
keywords = "lithion-ion battery, state of charge, gradient tree boosting,
multi layer perceptron, bateria l{\'{\i}}tio-{\'{\i}}on,
estado de carga,.",
abstract = "In this work, the internal impedance of the lithium-ion battery
pack, an essential measure of the degradation level of the
batteries, is estimated employing ensembles of machine learning
models. In this study, we take the supervised learning techniques
Multi-Layer Perceptron bagging neural network and gradient tree
boosting into account. Characteristics of the electric power
system, in which the battery pack is inserted, are extracted and
used in the modeling and training phases. During this process, the
architecture of the ensembles and the configuration of their base
learners are tuned through validation iterations. Finally, with
the application of statistical testing and similarity analysis
techniques, the best ensembles of models are examined and compared
to other methods found in the literature. Results indicate that
our approach is a suitable manner to estimate the internal
impedance of batteries. RESUMO: Neste trabalho, a imped{\^a}ncia
interna de um conjunto de baterias l{\'{\i}}tio-{\'{\i}}on
(uma importante medida do n{\'{\i}}vel de
degrada{\c{c}}{\~a}o) {\'e} estimada por meio de conjuntos de
modelos de aprendizado supervisionado tais como: rede neural tipo
MLP (Multi- Layer Perceptron) e Gradient Tree Boosting. Para isto,
caracter{\'{\i}}sticas do sistema de alimenta{\c{c}}{\~a}o
el{\'e}trica, em que o conjunto de baterias est{\'a} inserido,
s{\~a}o extra{\'{\i}}das e utilizadas na constru{\c{c}}{\~a}o
de conjuntos de modelos supervisionados (MLP e xgBoost). Ao longo
deste processo, a arquitetura de tais conjuntos de modelos e suas
respectivas configura{\c{c}}{\~o}es s{\~a}o ajustados por meio
de valida{\c{c}}{\~o}es. Finalmente, com a aplica{\c{c}}{\~a}o
de t{\'e}cnicas de teste e verifica{\c{c}}{\~a}o
estat{\'{\i}}stica, as acur{\'a}cias dos modelos s{\~a}o
calculadas e testes comparativos s{\~a}o conduzidos. Os
resultados obtidos mostram que a abordagem proposta {\'e}
adequada para o problema de estimativa da impend{\^a}ncia de
baterias.",
committee = "Santos, Rafael Duarte Coelho dos (presidente) and Quiles, Marcos
Gon{\c{c}}alves (orientador) and Rosa, Reinaldo Roberto and
Shiguemori, Elcio Hideiti and Vianna, Wlamir Olivares Loesch and
Basgalupp, Marcio Porto",
englishtitle = "Estimativa da imped{\^a}ncia de conjuntos de baterias de
l{\'{\i}}tio-{\'{\i}}on por meio de aprendizado de
m{\'a}quina",
language = "en",
pages = "85",
ibi = "8JMKD3MGP3W34R/3R2BN4B",
url = "http://urlib.net/ibi/8JMKD3MGP3W34R/3R2BN4B",
targetfile = "publicacao.pdf",
urlaccessdate = "06 maio 2024"
}