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

doi:10.1109/ROPEC.2018.8661446

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

Transport Engineering Research Group (GIIT)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538659359
ISBN (Print)	9781538659359
DOIs	https://doi.org/10.1109/ROPEC.2018.8661446
State	Published - 5 Mar 2019
Event	2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018 - Ixtapa, Guerrero, Mexico Duration: 14 Nov 2018 → 16 Nov 2018

Publication series

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

Conference

Conference	2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018
Country/Territory	Mexico
City	Ixtapa, Guerrero
Period	14/11/18 → 16/11/18

Bibliographical note

Funding Information:
This study has been supported by “Grupo de Investi-gación en Ingeniería del Transporte (GIIT)” in the “Reacondi-cionamiento de Baterías para Movilidad Alternativa” research proyect of Universidad Politecnica Salesiana.

Publisher Copyright:
© 2018 IEEE.

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

Keywords

Classifier
Hybrid electric vehicle
Machine learning
Ni-MH

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10.1109/ROPEC.2018.8661446

Cite this

Ortiz, J. P., Valladolid, J. D., Garcia, C. L., Novillo, G., & Berrezueta, F. (2019). Analysis of machine learning techniques for the intelligent diagnosis of Ni-MH battery cells. In 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018 [8661446] (2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROPEC.2018.8661446

Ortiz, Juan P. ; Valladolid, Juan D. ; Garcia, Cristian L. et al. / Analysis of machine learning techniques for the intelligent diagnosis of Ni-MH battery cells. 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. (2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018).

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abstract = "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.",

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Ortiz, JP , Valladolid, JD , Garcia, CL, Novillo, G & Berrezueta, F 2019, Analysis of machine learning techniques for the intelligent diagnosis of Ni-MH battery cells. in 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018., 8661446, 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018, Ixtapa, Guerrero, Mexico, 14/11/18. https://doi.org/10.1109/ROPEC.2018.8661446

Analysis of machine learning techniques for the intelligent diagnosis of Ni-MH battery cells. / Ortiz, Juan P.; Valladolid, Juan D.; Garcia, Cristian L. et al.

2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. 8661446 (2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Ortiz JP , Valladolid JD , Garcia CL, Novillo G, Berrezueta F. Analysis of machine learning techniques for the intelligent diagnosis of Ni-MH battery cells. In 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. 8661446. (2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018). doi: 10.1109/ROPEC.2018.8661446

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

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