The Role of Machine Learning in Enhancing Battery Management for Drone Operations: A Focus on SoH Prediction Using Ensemble Learning Techniques

Basit Öğe Kaydı
dc.authoridBicer, Emre/0000-0002-9871-4102
dc.authoridoyucu, saadin/0000-0003-3880-3039
dc.authoridAKSOZ, Ahmet/0000-0002-2563-1218
dc.contributor.authorCetinus, Buesra
dc.contributor.authorOyucu, Saadin
dc.contributor.authorAksoz, Ahmet
dc.contributor.authorBicer, Emre
dc.date.accessioned2025-05-04T16:45:44Z
dc.date.available2025-05-04T16:45:44Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractThis study considers the significance of drones in various civilian applications, emphasizing battery-operated drones and their advantages and limitations, and highlights the importance of energy consumption, battery capacity, and the state of health of batteries in ensuring efficient drone operation and endurance. It also describes a robust testing methodology used to determine battery SoH accurately, considering discharge rates and using machine learning algorithms for analysis. Machine learning techniques, including classical regression models and Ensemble Learning methods, were developed and calibrated using experimental UAV data to predict SoH accurately. Evaluation metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) assess model performance, highlighting the balance between model complexity and generalization. The results demonstrated improved SoH predictions with machine learning models, though complexities may lead to overfitting challenges. The transition from simpler regression models to intricate Ensemble Learning methods is meticulously described, including an assessment of each model's strengths and limitations. Among the Ensemble Learning methods, Bagging, GBR, XGBoost, LightGBM, and stacking were studied. The stacking technique demonstrated promising results: for Flight 92 an RMSE of 0.03% and an MAE of 1.64% were observed, while for Flight 129 the RMSE was 0.66% and the MAE stood at 1.46%.
dc.identifier.doi10.3390/batteries10100371
dc.identifier.issn2313-0105
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85207638133
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/batteries10100371
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35205
dc.identifier.volume10
dc.identifier.wosWOS:001342741000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofBatteries-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250504
dc.subjectUAV data analysis
dc.subjectmachine learning
dc.subjectregression models
dc.subjectEnsemble Learning
dc.subjectLi-ion
dc.titleThe Role of Machine Learning in Enhancing Battery Management for Drone Operations: A Focus on SoH Prediction Using Ensemble Learning Techniques
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu