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Comparative Analysis of Intelligent Techniques for Categorization of the Operational Status of LiFePo4 Batteries

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Hybrid Artificial Intelligent Systems (HAIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14001))

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Abstract

At present, energy storage systems are becoming a key factor for technological development in different fields, such as electric mobility or the improvement of energy production systems towards renewable energy alternatives. One of the most widely used systems for storing electrical energy are batteries. However, to obtain a correct and safe operation, it is essential to know the operational state of their load cells, which in some cases may require a high degree of sensorization of these devices. This research examines the performance of four machine-learning techniques to determine the operating battery status from only the voltage data recorded. For this purpose, a set of real data collected during the execution of a Capacity Confirmation Test (CCT) for a Lithium Iron Phosphate - LiFePO4 (LFP) cell is used. The results obtained have shown good performance in some of the techniques evaluated.

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Acknowledgments

Álvaro Michelena’s research was supported by the Spanish Ministry of Universities (https://www.universidades.gob.es/), under the “Formación de Profesorado Universitario” grant with reference FPU21/00932.

Míriam Timiraos’s research was supported by the Xunta de Galicia (Regional Government of Galicia) through grants to industrial Ph.D. (http://gain.xunta.gal), under the Doutoramento Industrial 2022 grant with reference: 04_IN606D_2022_ 2692965.

CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01).

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Authors and Affiliations

  1. Department of Industrial Engineering, CTC, CITIC, University of A Coruña, Calle Mendizábal s/n, 15403, Ferrol, A Coruña, Spain

    Antonio Díaz-Longueira, Álvaro Michelena, Míriam Timiraos, Francisco Zayas-Gato, Héctor Quintián & José Luis Calvo-Rolle

  2. Department of Water Technologies, National Technological Center, Fundación Instituto Tecnológico de Galicia, Cantón Grande 9, Planta 3, C.P. 15003, A Coruña, Spain

    Míriam Timiraos

  3. Department of Electrical and Systems Engineering, University of León, Campus de Vegazana, s/n, 24071, León, Spain

    Carmen Benavides Cuellar, Héctor Alaiz-Moretón & María Teresa García-Ordás

Authors
  1. Antonio Díaz-Longueira
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  2. Álvaro Michelena
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  3. Míriam Timiraos
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  4. Francisco Zayas-Gato
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  5. Héctor Quintián
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  6. Carmen Benavides Cuellar
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  7. Héctor Alaiz-Moretón
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  8. José Luis Calvo-Rolle
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  9. María Teresa García-Ordás
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Corresponding author

Correspondence to Álvaro Michelena .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of Leon, León, Spain

    Hilde Pérez García

  3. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  4. Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. University of A Coruña, Ferrol - Coruña, Spain

    José Luis Calvo Rolle

  8. University of A Coruña, Ferrol - Coruña, Spain

    Héctor Quintián

  9. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Díaz-Longueira, A. et al. (2023). Comparative Analysis of Intelligent Techniques for Categorization of the Operational Status of LiFePo4 Batteries. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_46

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  • DOI: https://doi.org/10.1007/978-3-031-40725-3_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40724-6

  • Online ISBN: 978-3-031-40725-3

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