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- Title
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
- 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.
- Subjects
VOLTAGE; ELECTRIC currents; HOUSEHOLD electronics; STORAGE batteries; RENEWABLE energy sources
- Publication
Logic Journal of the IGPL, 2024, Vol 32, Issue 4, p712
- ISSN
1367-0751
- Publication type
Article
- DOI
10.1093/jigpal/jzae021