Manav G., Yapici M. M., Saygin A.
2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA), Ankara, Türkiye, 23 - 24 Mayıs 2025, ss.1-9, (Tam Metin Bildiri)
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Yayın Türü:
Bildiri / Tam Metin Bildiri
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Doi Numarası:
10.1109/ichora65333.2025.11017233
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Basıldığı Şehir:
Ankara
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Basıldığı Ülke:
Türkiye
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Sayfa Sayıları:
ss.1-9
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Ankara Üniversitesi Adresli:
Evet
Özet
The accurate prediction of
battery health is crucial for the safe, reliable, and efficient
operation of applications such as electric vehicles, renewable energy
storage, and portable electronics.Battery State of Health (SoH)
prediction enables maintenance strategies, preventing unexpected
failures and extending battery lifespan. This study has performed the
SoH prediction of lithium-ion batteries using discharge voltage data
through machine learning techniques.Utilizing the NASA battery dataset, a
comparative analysis is conducted among Multi-Layer Perceptron (MLP),
Random Forest (RF), and Support Vector Regression (SVR) algorithms. In
this study, unlike other works in the literature, discharge data has
been synchronized over time. Furthermore, the effect of normalized data
on battery State of Health (SoH) estimation has been investigated by
testing across various data formats. The results obtained reveal that
the SVR method achived superior performance across all data formats,
with an average MSE error rate of 0.000021. Normalized data yielded
better results across all models.