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Machine Learning Based System for Detecting Battery State-of-Health

  • Conference paper
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18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (SOCO 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 749))

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Abstract

In different fields, such as electric mobility, the increasing use of small consumer electronics devices, and the development of renewable energy alternatives, energy storage systems have become crucial to technological advancement. Batteries are among the most frequently used techniques for storing electrical energy. However, it is essential to be aware of the operating status of their load cells to achieve proper and secure operation. This study compares the efficacy of various supervised classification techniques for categorizing the operating status of a Lithium Iron Phosphate - LiFePO4 (LFP) load cell using voltage values obtained from the battery. Each method was assessed using a real dataset, and excellent results were achieved, with accuracy values of up to 90%.

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Acknowledgement

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

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

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

    Míriam Timiraos

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

    Natalia Prieto Fernández, Héctor Alaiz-Moretón & María Teresa García-Ordás

Authors
  1. Álvaro Michelena
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  2. Antonio Díaz-Longueira
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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. Natalia Prieto Fernández
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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. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. School of Industrial, Computer, University of Leon, León, Spain

    Hilde Pérez García

  3. Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Francisco Javier Martínez de Pisón

  4. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. Applied Computational Intelligence, University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José Luis Calvo Rolle

  8. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  9. Faculty of Science, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Michelena, Á. et al. (2023). Machine Learning Based System for Detecting Battery State-of-Health. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_16

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