Autors: Rizanov, S. M., Stoynova A., Kafadarova N., Sotirov S., Bonev, B. B.
Title: Machine Learning Assessment of Battery State-of-Health
Keywords: Artificial intelligence, Battery, Machine Learning, Spectrogram, State-of-Health

Abstract: 2024.The topic of LiIon battery State-of-Health assessment has become extremely prevalent within scientific research in the past 15 years due to the push towards a more sustainable industry. Within this work, we have developed and presented a Machine Learning method for evaluating the State-of-Health of batteries based on curve approximation coefficients. Additionally, we have proposed a method for converting the captured battery charging and discharging transient data into spectrograms, which can be used as data entries for the training of a Convolutional Neural Network image classifier.

References

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Issue

Lecture Notes in Networks and Systems, vol. 817, pp. 587-600, 2024, Switzerland, https://doi.org/10.1007/978-981-99-7886-1_48

Вид: книга/глава(и) от книга, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus