Multiscale Dual-path Feature Aggregation Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
This work improves targeted maintenance strategies for industrial machinery by enhancing RUL prediction accuracy, though it appears incremental as it builds on existing deep learning approaches for battery degradation.
The paper tackles the problem of predicting the remaining useful life (RUL) of lithium-ion batteries by addressing inefficiencies in modeling local and global correlations of degradation sequences, proposing MDFA-Net, which outperforms existing top-tier methods on two public datasets.
Targeted maintenance strategies, ensuring the dependability and safety of industrial machinery. However, current modeling techniques for assessing both local and global correlation of battery degradation sequences are inefficient and difficult to meet the needs in real-life applications. For this reason, we propose a novel deep learning architecture, multiscale dual-path feature aggregation network (MDFA-Net), for RUL prediction. MDFA-Net consists of dual-path networks, the first path network, multiscale feature network (MF-Net) that maintains the shallow information and avoids missing information, and the second path network is an encoder network (EC-Net) that captures the continuous trend of the sequences and retains deep details. Integrating both deep and shallow attributes effectively grasps both local and global patterns. Testing conducted with two publicly available Lithium-ion battery datasets reveals our approach surpasses existing top-tier methods in RUL forecasting, accurately mapping the capacity degradation trajectory.