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

Benjamín-Arturo Pérez-Peláez, Irahan-Otoniel José-Guzmán and Eddy Sánchez-DelaCruz
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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

A chapter in Health Technologies and Demographic Challenges, 2025, pp 359-370 from Springer

Abstract: 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.

Keywords: Lithium-ion batteries; Machine learning; Algorithms; State-of-health (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-94901-2_29

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DOI: 10.1007/978-3-031-94901-2_29

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