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Clustering techniques performance comparison for predicting the battery state of charge: A hybrid model approach.

Authors :
Ordás, María Teresa
Blanco, David Yeregui Marcos del
Aveleira-Mata, José
Zayas-Gato, Francisco
Jove, Esteban
Casteleiro-Roca, José-Luis
Quintián, Héctor
Calvo-Rolle, José Luis
Alaiz-Moreton, Héctor
Source :
Logic Journal of the IGPL; Aug2024, Vol. 32 Issue 4, p712-728, 17p
Publication Year :
2024

Abstract

Batteries are a fundamental storage component due to its various applications in mobility, renewable energies and consumer electronics among others. Regardless of the battery typology, one key variable from a user's perspective is the remaining energy in the battery. It is usually presented as the percentage of remaining energy compared to the total energy that can be stored and is labeled State Of Charge (SOC). This work addresses the development of a hybrid model based on a Lithium Iron Phosphate (LiFePO4) power cell, due to its broad implementation. The proposed model calculates the SOC, by means of voltage and electric current as inputs and the latter as the output. Therefore, four models based on k-Means, Agglomerative Clustering, Gaussian Mixture and Spectral Clustering techniques have been tested in order to obtain an optimal solution. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13670751
Volume :
32
Issue :
4
Database :
Complementary Index
Journal :
Logic Journal of the IGPL
Publication Type :
Academic Journal
Accession number :
178650248
Full Text :
https://doi.org/10.1093/jigpal/jzae021