Multiscale Dual-path Feature Aggregation Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

TL;DR

This paper introduces MDFA-Net, a deep learning model that effectively combines local and global features for accurate remaining useful life prediction of lithium-ion batteries, outperforming existing methods.

Contribution

The paper presents a novel multiscale dual-path network architecture that captures both shallow and deep features for improved RUL prediction in batteries.

Findings

MDFA-Net outperforms existing methods in RUL forecasting.

The model accurately captures capacity degradation trajectories.

Dual-path architecture effectively integrates local and global information.

Abstract

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.