Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) is crucial for enhancing the safety, reliability, and efficiency of battery-powered applications like electric vehicles and energy storage systems. This review analyzed over 200 peer-reviewed studies and categorized RUL prediction methods into three major approaches: physics-based, data-driven, and hybrid models. Hybrid models, which combine physical insights with data-driven methods, are the most widely used due to their adaptability, accuracy, and interpretability. Data-driven models, such as long short-term memory and convolutional neural networks, excel in capturing complex, nonlinear relationships but require large datasets and high computational power. While physics-based models offer high accuracy, they are less commonly employed due to their complexity and extensive parameter tuning requirements. Despite their benefits, hybrid models face challenges, including increased computational complexity and integration difficulties. This review also highlights key datasets and evaluation metrics used in LIB RUL prediction. The NASA dataset is the most frequently used, appearing in 30.8% of the papers, followed by the CALCE dataset. Root mean square error is the most common evaluation metric, used in 29.6% of the studies, followed by mean absolute error and mean absolute percentage error, which are essential for assessing prediction accuracy. Through comparative analysis, this review identified key challenges and outlined future research directions, including the need for lightweight hybrid models, standardized benchmarking datasets, and uncertainty-aware evaluation frameworks to support real-time, robust battery management systems. In conclusion, the future of LIB RUL prediction lies in the integration of advanced hybrid models, improved datasets, and uncertainty-aware performance metrics, with a focus on refining data-driven approaches for handling real-time, multi-sensor data.

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential for the safe and efficient operation of electric vehicles and energy storage systems. This study proposes a novel RUL prediction framework that combines a physically interpretable dual-exponential degradation model with an Extended Kalman Filter (EKF), enhanced through Bayesian optimization. The proposed method automatically tunes critical EKF parameters (P, Q, and R), addressing a key limitation of conventional EKF approaches that rely on expert-driven parameter settings. Additionally, a dynamic weighting loss function is introduced to improve robustness across different degradation stages, emphasizing capacity trend consistency in early life and RUL accuracy near failure. Validation is performed on two widely used datasets (CALCE and NASA) across four experimental configurations. Results show that the proposed model outperforms standard EKF, Seq2Seq, and other baseline models in both capacity tracking and RUL estimation, demonstrating superior accuracy, interpretability, and generalization across diverse battery types and aging patterns. This work provides a scalable, interpretable, and measurement-driven approach for battery health management in real-world applications.

Remaining useful life (RUL) prediction for offshore wind turbines is important for predictive maintenance. However, its practical use is limited by the lack of reliable RUL labels, the small number of fault events, and the complex degradation links among turbine components. Most existing data-driven methods require labeled failure data and cannot fully capture how the degradation of different components is related. To address these limitations, a Dual-Pathway Degradation-Aware Network for label-free RUL prediction is proposed. The proposed method first constructs component-level health indicators (HIs) from SCADA data in a self-supervised manner and generates pseudo-RUL labels through a linear countdown strategy. It then jointly models the global degradation evolution and cross-component interactions using a global temporal branch and a fully connected graph branch, where residual aggregation is adopted to fuse node representations for RUL estimation. Experiments are conducted on the German North Sea Farm B dataset under a leave-one-out cross-validation (LOOCV) protocol. From the obtained results, the proposed method achieves an MAE of 111.3 h and an RMSE of 122.6 h, reducing the average MAE by approximately 19% compared with the strongest baseline. The proposed method achieves the best performance on three of the five turbines with fault events. Furthermore, the proposed method demonstrates superior capability in capturing component interaction patterns and provides an effective label-free solution for offshore wind turbine RUL prediction under limited fault data.