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