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State-of-Health Identification in Lithium-Ion Batteries Using Machine Learning

In: Health Technologies and Demographic Challenges

Author

Listed:
  • Benjamín-Arturo Pérez-Peláez

    (Artificial Intelligence Lab, Tecnológico Nacional de México/Instituto Tecnológico Superior de Misantla)

  • Irahan-Otoniel José-Guzmán

    (Artificial Intelligence Lab, Tecnológico Nacional de México/Instituto Tecnológico Superior de Misantla)

  • Eddy Sánchez-DelaCruz

    (Artificial Intelligence Lab, Tecnológico Nacional de México/Instituto Tecnológico Superior de Misantla)

Abstract

We evaluated the performance of four machine learning algorithms to identify the performance of lithium-ion batteries, considering temperature variations. The training and test sets were performed for 60/40, 70/30, 80/20 and cross validation with 10 folds. Four algorithms: Naïve Bayes, Multi-Layer Perceptron, AdaBoost and JRip were implemented in the Waikato Environment for Knowledge Analysis framework. Based on the results, it was observed that the AdaBoost algorithm was the best at identifying lithium-ion battery performance with respect to temperature.

Suggested Citation

  • Benjamín-Arturo Pérez-Peláez & Irahan-Otoniel José-Guzmán & Eddy Sánchez-DelaCruz, 2025. "State-of-Health Identification in Lithium-Ion Batteries Using Machine Learning," Springer Proceedings in Business and Economics, in: Pedro Miguel Gaspar & Juan Manuel Cueva Lovelle & Carlos Mentenegro-Marín & Teresa Guarda (ed.), Health Technologies and Demographic Challenges, pages 359-370, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-94901-2_29
    DOI: 10.1007/978-3-031-94901-2_29
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