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

Research Group in Transportation Engineering (GIIT)

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

14 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

Publisher Copyright:
© 2018 IEEE.

Keywords

Classifier
Hybrid electric vehicle
Machine learning
Ni-MH

Access to Document

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 Article 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).

@inproceedings{0085e5852eeb45bf92829b448f129f7e,

title = "Analysis of machine learning techniques for the intelligent diagnosis of Ni-MH battery cells",

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.",

keywords = "Classifier, Hybrid electric vehicle, Machine learning, Ni-MH",

author = "Ortiz, {Juan P.} and Valladolid, {Juan D.} and Garcia, {Cristian L.} and Gina Novillo and Felipe Berrezueta",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018 ; Conference date: 14-11-2018 Through 16-11-2018",

year = "2019",

month = mar,

day = "5",

doi = "10.1109/ROPEC.2018.8661446",

language = "English",

isbn = "9781538659359",

series = "2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018",

address = "United States",

}

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

TY - GEN

T1 - Analysis of machine learning techniques for the intelligent diagnosis of Ni-MH battery cells

AU - Ortiz, Juan P.

AU - Valladolid, Juan D.

AU - Garcia, Cristian L.

AU - Novillo, Gina

AU - Berrezueta, Felipe

PY - 2019/3/5

Y1 - 2019/3/5

N2 - 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.

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.

KW - Classifier

KW - Hybrid electric vehicle

KW - Machine learning

KW - Ni-MH

UR - http://www.scopus.com/inward/record.url?scp=85063899727&partnerID=8YFLogxK

U2 - 10.1109/ROPEC.2018.8661446

DO - 10.1109/ROPEC.2018.8661446

M3 - Conference contribution

AN - SCOPUS:85063899727

SN - 9781538659359

T3 - 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018

Y2 - 14 November 2018 through 16 November 2018

ER -

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this