提出双路径网络，精准预测锂电池剩余寿命。
Multiscale Dual-path Feature Aggregation Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
- 双路径设计：浅层网络保局部细节，深层网络捕获全局趋势。
- 在两个公开数据集上优于现有方法，准确追踪容量退化轨迹。
- 适合电池健康管理、工业设备维护等场景使用。
面向工业设备的精准维护策略需确保可靠性与安全性。然而，当前评估电池退化序列中局部与全局相关性的建模方法效率低，难以满足实际应用需求。为此，本文提出一种新型深度学习架构——多尺度双路径特征聚合网络（MDFA-Net），用于剩余使用寿命（RUL）预测。该网络包含双路径结构：第一路径为多尺度特征网络（MF-Net），保留浅层信息并避免信息丢失；第二路径为编码器网络（EC-Net），捕捉序列连续趋势并保留深层细节。通过有效融合深浅特征，全面把握局部与全局模式。在两个公开锂离子电池数据集上的测试表明，本方法在RUL预测性能上超越现有顶级方法，能准确映射容量退化轨迹。
原文摘要 · Abstract (English)
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.
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