Comparative study of anomaly detection techniques for monitoring Lithium Iron Phosphate – LiFePO4 batteries

3 pages•Published: February 16, 2023

Álvaro Michelena Grandío, Francisco Zayas-Gato, Esteban Jove, Oscar Fontenla-Romero and Jose Luis Calvo-Rolle

Abstract

This research analyzes and compares the application of different intelligent supervised classification techniques for detecting anomalies in power cells. For this purpose, a labeled dataset is obtained and generated in which samples of the different charge and discharge cycles of a Lithium Iron Phosphate - LiFePO4 (LFP) battery commonly used in electric vehicles are collected. The final classifiers present successful results.

Keyphrases: anomaly detection, battery, lifepo4, supervised classifiers

In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, vol 14, pages 80-82.

Links:	https://easychair.org/publications/paper/WqQb
	https://doi.org/10.29007/qd3p

BibTeX entry

@inproceedings{XoveTIC2022:Comparative_study_anomaly_detection,
  author    = {Álvaro Michelena Grandío and Francisco Zayas-Gato and Esteban Jove and Oscar Fontenla-Romero and Jose Luis Calvo-Rolle},
  title     = {Comparative study of anomaly detection techniques for monitoring Lithium Iron Phosphate – LiFePO4 batteries},
  booktitle = {Proceedings of V XoveTIC Conference. XoveTIC 2022},
  editor    = {Alvaro Leitao and Lucía Ramos},
  series    = {Kalpa Publications in Computing},
  volume    = {14},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/WqQb},
  doi       = {10.29007/qd3p},
  pages     = {80-82},
  year      = {2023}}

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