基于多尺度特征融合的并行神经网络剩余寿命预测方法

余萍; 王浩年; 曹洁

doi:doi:10.19912/j.0254-0096.tynxb.2024-1676

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基于多尺度特征融合的并行神经网络剩余寿命预测方法

更新时间：2026-02-06

- 基于多尺度特征融合的并行神经网络剩余寿命预测方法
- Vol. 47, Issue 1, Pages: 785-796(2026)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1676
  CLC： TM912
- Online First：05 February 2026，
  
  Published：05 February 2026
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余萍, 王浩年, 曹洁. 基于多尺度特征融合的并行神经网络剩余寿命预测方法[J]. 2026, 47(1): 785-796.
DOI：

余萍, 王浩年, 曹洁. 基于多尺度特征融合的并行神经网络剩余寿命预测方法[J]. 2026, 47(1): 785-796. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1676.

摘要

为更准确预测锂电池的剩余使用寿命（RUL）

提高其可靠性

确保稳定运行

提出一种基于多尺度特征融合的并行神经网络预测方法。首先

利用不同尺度的时间卷积网络（TCN）提取锂电池的多尺度特征

从而增强局部和全局特征的提取能力;接着

引入交叉注意力机制对特征进行筛选与融合

以提取关键的退化信息;随后

构建并行的Bi-LSTM和Bi-GRU网络

以学习退化特征并建立时间尺度上的长期依赖关系

最终实现RUL预测。通过美国航空航天局（NASA）和CALCE锂电池数据集验证

证明了所提方法在不同背景下的有效性。

Abstract

To achieve more accurate predictions of the remaining useful life （RUL） of lithium-ion batteries and enhance their reliability to ensure stable operation

this paper proposes a parallel neural network prediction method based on multi-scale feature fusion. First

multi-scale features of the lithium-ion batteryies are extracted using temporal convolutional network （TCN） at different scales

enhancing the capture of both local and global features. Then

a cross-attention mechanism is introduced to filter and fuse the features

focusing on key degradation information. Next

parallel Bi-LSTM and Bi-GRU networks are constructed to learn degradation features and establish long-term dependencies on the time scale

ultimately achieving RUL prediction. The proposed method is validated using the NASA and CALCE lithium battery datasets

demonstrating its effectiveness in various contexts.

关键词

Keywords

references

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