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

Logic Journal of the IGPL (forthcoming)
  Copy   BIBTEX

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.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 92,227

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Predicting Performance.George Sher - 1987 - Social Philosophy and Policy 5 (1):188.

Analytics

Added to PP
2024-05-10

Downloads
0

6 months
0

Historical graph of downloads

Sorry, there are not enough data points to plot this chart.
How can I increase my downloads?

Author's Profile

Citations of this work

No citations found.

Add more citations