Interpreting CNN-RNN Hybrid Model-Based Ensemble Learning with Explainable Artificial Intelligence to Predict the Performance of Li-Ion Batteries in Drone Flights

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dc.authoridSAGIROGLU, SEREF/0000-0003-0805-5818
dc.authoridErsoz, Betul/0000-0001-6221-1530
dc.authoridoyucu, saadin/0000-0003-3880-3039
dc.authoridAKSOZ, Ahmet/0000-0002-2563-1218
dc.authoridBicer, Emre/0000-0002-9871-4102
dc.contributor.authorErsoz, Betul
dc.contributor.authorOyucu, Saadin
dc.contributor.authorAksoz, Ahmet
dc.contributor.authorSagiroglu, Seref
dc.contributor.authorBicer, Emre
dc.date.accessioned2025-05-04T16:45:49Z
dc.date.available2025-05-04T16:45:49Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractLi-ion batteries are important in modern technology, especially for drones, due to their high energy density, long cycle life, and lightweight properties. Predicting their performance is crucial for enhancing drone flight safety, optimizing operations, and reducing costs. This involves using advanced techniques like machine learning (e.g., Convolutional Neural Network-CNNs, Recurrent Neural Network-RNNs), statistical modeling (e.g., Kalman Filtering), and explainable AI (e.g., SHAP, LIME, PDP) to forecast battery behavior, extend battery life, and improve drone efficiency. The study aims to develop a CNN-RNN-based ensemble model, enhanced with explainable AI, to predict key battery metrics during drone flights. The model's predictions will aid in enhancing battery performance via continuous, data-driven monitoring, improve drone safety, optimize operations, and reduce greenhouse gas emissions through advanced recycling methods. In the present study, comparisons are made for the behaviors of two different drone Li-ion batteries, numbered 92 and 129. The ensemble model in Drone 92 showed the best performance with MAE (0.00032), RMSE (0.00067), and R2 (0.98665) scores. Similarly, the ensemble model in Drone 129 showed the best performance with MAE (0.00030), RMSE (0.00044), and R2 (0.98094) performance metrics. Similar performance results are obtained in the two predictions. However, drone 129 has a minimally lower error rate. When the Partial Dependence Plots results, which are one of the explainable AI (XAI) techniques, are interpreted with the decision tree algorithm, the effect of the Current (A) value on the model estimations in both drone flights is quite evident. When the current value is around -4, the model is more sensitive and shows more changes. This study will establish benchmarks for future research and foster advancements in drone and battery technologies through extensive testing.
dc.identifier.doi10.3390/app142310816
dc.identifier.issn2076-3417
dc.identifier.issue23
dc.identifier.scopus2-s2.0-85212712934
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app142310816
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35212
dc.identifier.volume14
dc.identifier.wosWOS:001376270700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250504
dc.subjectLi-ion
dc.subjectCNN
dc.subjectRNN
dc.subjectensemble
dc.subjectXAI
dc.subjectPDP
dc.titleInterpreting CNN-RNN Hybrid Model-Based Ensemble Learning with Explainable Artificial Intelligence to Predict the Performance of Li-Ion Batteries in Drone Flights
dc.typeArticle

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