A Comparative Study of Machine Learning and Deep Learning Models for State of Charge and Remaining Useful Life Estimation on a Rotary-Wing UAV Battery
Drones, cilt.10, sa.7, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 10 Sayı: 7
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/drones10070549
- Dergi Adı: Drones
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Compendex, INSPEC, Directory of Open Access Journals, Technology Collection (ProQuest)
- Anahtar Kelimeler: battery management, gradient boosted trees, LSTM, machine learning, remaining useful life, rotary-wing UAV, RWKV, state of charge, unmanned aerial vehicle
- Erciyes Üniversitesi Adresli: Evet
Özet
Battery state estimation is the main safety constraint for electric rotary-wing unmanned aerial vehicles (UAVs): mission decisions depend on both the instantaneous State of Charge (SOC) and the Remaining Useful Life (RUL). The present study compares seven machine learning and deep learning models (LR, SVM, k-NN, GBT, EL, LSTM, and a simplified RWKV) on real flight data from a rotary-wing helicopter testbed with a Pixhawk autopilot and an NVIDIA Jetson Nano mission computer. The dataset has 1310 samples (∼262 s) of nine on-board sensor signals. Mission-based RUL is defined as the projected time until SOC reaches a (Formula presented.) safe-landing threshold. All models use an 80/20 random split, five regression metrics (RMSE, MAE, (Formula presented.), MSE, PRMSE), and five random seeds. GBT wins on SOC with (Formula presented.), MAE (Formula presented.), and (Formula presented.) s per-sample inference on a workstation CPU; this latency leaves headroom for on-board mission planning. Battery temperature and voltage together carry over (Formula presented.) of the predictive signal. GBT wins again on RUL ((Formula presented.), MAE (Formula presented.) s). The same ordering (tree ensemble ≻ recurrent ≻ linear) holds for both tasks; the remaining RUL gap reflects the single-flight dataset. The SOC labels originate from the on-board autopilot’s Coulomb-counting-based fuel-gauge estimator, so the SOC numbers should be read as a reproduction of that on-board trace at sub-microsecond inference latency rather than as independent accuracy; the calibration-free Coulomb-counting baseline reaches a marginally higher (Formula presented.) ((Formula presented.), MAE (Formula presented.)) on the same task.