Akademik Veri Yönetim Sistemi
Applied Sciences (Switzerland), cilt.16, sa.4, 2026 (SCI-Expanded, Scopus)
Reliable prediction of the Remaining Useful Life (RUL) of lithium-ion batteries (LIBs) plays a pivotal role in maintaining safe operation, enhancing system dependability, and supporting economically sustainable lifecycle planning in electric mobility and stationary energy storage applications. However, battery aging is governed by highly nonlinear, interacting, and chemistry-dependent processes, which pose significant challenges for conventional data-driven prognostic models. In this study, a unified RUL prediction framework is proposed by integrating multi-domain feature engineering, a Multi-Criteria Adaptive Selection (MCAS) strategy, and a Bidirectional Long Short-Term Memory (Bi-LSTM) network enhanced with dual multi-head attention. Degradation-relevant descriptors extracted from time, frequency, and chaotic domains are employed to capture complementary aging dynamics across battery cycling. In addition, a novel degradation-consistency indicator, termed the M-score, is introduced to characterize the regularity and stability of degradation behavior using observable electrical, thermal, and statistical signals. The MCAS mechanism systematically identifies informative and temporally stable features while suppressing redundancy, thereby improving both predictive robustness and interpretability. The resulting architecture jointly exploits adaptive feature refinement and attention-based temporal modeling to enhance the RUL estimation accuracy. The proposed framework is validated using two widely adopted benchmark datasets: the Toyota Research Institute (TRI) dataset, representing fast-charging lithium iron phosphate (LFP) cells, and the Sandia National Laboratories (SNL) dataset, which includes multiple chemistries, such as LFP, NMC, and NCA. Experimental results demonstrate substantial improvements in the RUL prediction accuracy compared with baseline Bi-LSTM and single-attention models, while systematic ablation studies confirm the individual contributions of the M-score and MCAS components. Within the evaluated datasets and operating conditions, the results suggest that the proposed framework offers a robust and interpretable data-driven solution for battery RUL estimation. However, extending its generalizability and validating its performance on unseen datasets and in real-world scenarios remain important areas for future research.