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 output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538659359
ISBN (Print)9781538659359
DOIs
StatePublished - 5 Mar 2019
Event2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018 - Ixtapa, Guerrero, Mexico
Duration: 14 Nov 201816 Nov 2018

Publication series

Name2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018

Conference

Conference2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018
CountryMexico
CityIxtapa, Guerrero
Period14/11/1816/11/18

Fingerprint

Hydrides
Learning systems
Classifiers
Hybrid vehicles
Vector spaces
Electric vehicles
Metals
Principal component analysis
Logistics
Neural networks

Keywords

  • Classifier
  • Hybrid electric vehicle
  • Machine learning
  • Ni-MH

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. ; Novillo, Gina ; Berrezueta, Felipe. / 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",
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",
year = "2019",
month = "3",
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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",
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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.; Novillo, Gina; Berrezueta, Felipe.

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 proceedingConference contribution

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

KW - Classifier

KW - Hybrid electric vehicle

KW - Machine learning

KW - Ni-MH

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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.

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). https://doi.org/10.1109/ROPEC.2018.8661446