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A New Dataset for Analyzing Battery Failures in Wheelchairs

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Intelligent Data Engineering and Automated Learning – IDEAL 2024 (IDEAL 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15347))

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Abstract

This study addresses battery failure in motorized wheelchairs, which are essential for the mobility of individuals with disabilities. The main objective was to concept a comprehensive Dataset comprising six attributes that directly impact battery life, consisting of 498 instances. Using the Random Forest algorithm, we demonstrate the ability to accurately predict battery failures. The results highlight the necessity for proactive measures to prevent battery degradation and extend its lifespan.

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Acknoledgements

The authors would like to than Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul - FAPERGRS (24/2551-0001396-2) and Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq with FAPERGS/CNPq (23/2551-0000126-8).

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Authors and Affiliations

  1. Centro de Ciências Sociais e Tecnológicas, Mestrado em Engenharia Eletrônica e Computação, Universidade Católica de Pelotas, Pelotas, Brazil

    William M. Manzolli, Tiago B. Rickes, Giancarlo Lucca & Lizandro de Souza Oliveira

  2. Universidade Federal de Pelotas, Pelotas, Brazil

    Adenauer Correa Yamin

Authors
  1. William M. Manzolli
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  2. Tiago B. Rickes
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  3. Giancarlo Lucca
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  4. Lizandro de Souza Oliveira
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  5. Adenauer Correa Yamin
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Corresponding author

Correspondence to William M. Manzolli .

Editor information

Editors and Affiliations

  1. Technical University of Valencia, Valencia, Valencia, Spain

    Vicente Julian

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. The University of Manchester, Manchester, UK

    Hujun Yin

  4. Universitat Politècnica de València, Valencia, Valencia, Spain

    Juan M. Alberola

  5. University of Evora, Evora, Portugal

    Vitor Beires Nogueira

  6. Universidade do Minho, Braga, Portugal

    Paulo Novais

  7. University of Huelva, Huelva, Spain

    Antonio Tallón-Ballesteros

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Manzolli, W.M., Rickes, T.B., Lucca, G., Oliveira, L.d.S., Yamin, A.C. (2025). A New Dataset for Analyzing Battery Failures in Wheelchairs. In: Julian, V., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2024. IDEAL 2024. Lecture Notes in Computer Science, vol 15347. Springer, Cham. https://doi.org/10.1007/978-3-031-77738-7_30

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  • DOI: https://doi.org/10.1007/978-3-031-77738-7_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77737-0

  • Online ISBN: 978-3-031-77738-7

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