Analysis of machine learning techniques for the intelligent diagnosis of Ni-MH battery cells

Juan P. Ortiz, Juan D. Valladolid, Cristian L. Garcia, Gina Novillo, Felipe Berrezueta

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

7 Citas (Scopus)

Resumen

This paper presents a comparison of different machine learning techniques for classification of the unbalance and damage Niquel-Metal Hydride (Ni-MH) battery cells used in hybrid electric vehicles (HEV) and electric vehicles (EV). The implemented linear and non-linear classification algorithms used in this study are: logistic regression (LR), k-nearest neighbors (k-NN), kernel space vector machine (KSVM), Gaussian naive Bayes (GNB) and a neural network (NN); the classifiers in this work used the principal component analysis (PCA) in dual variables to reduce the high dimensional data set. To evaluate the performance of the classifiers, experimental results and a detailed analysis of the confusion matrix are used where the effectiveness of the algorithms are demonstrated.

Idioma originalInglés
Título de la publicación alojada2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781538659359
ISBN (versión impresa)9781538659359
DOI
EstadoPublicada - 5 mar. 2019
Evento2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018 - Ixtapa, Guerrero, México
Duración: 14 nov. 201816 nov. 2018

Serie de la publicación

Nombre2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018

Conferencia

Conferencia2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018
País/TerritorioMéxico
CiudadIxtapa, Guerrero
Período14/11/1816/11/18

Nota bibliográfica

Publisher Copyright:
© 2018 IEEE.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

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