基于多尺度分解和多模型融合的锂电池剩余使用寿命预测

王鑫; 宝财吉拉呼; 马志强; 李杰; 高俊东; 李开心

doi:doi:10.19912/j.0254-0096.tynxb.2024-1095

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基于多尺度分解和多模型融合的锂电池剩余使用寿命预测

更新时间：2025-12-17

- 基于多尺度分解和多模型融合的锂电池剩余使用寿命预测
- Issue 10, Pages: 107-116(2025)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1095
  CLC： M912
- Published Online：31 October 2025，
  
  Published：31 October 2025
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王鑫, 宝财吉拉呼, 马志强, et al. 基于多尺度分解和多模型融合的锂电池剩余使用寿命预测[J]. 2025, (10): 107-116.
DOI：

王鑫, 宝财吉拉呼, 马志强, et al. 基于多尺度分解和多模型融合的锂电池剩余使用寿命预测[J]. 2025, (10): 107-116. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1095.

摘要

为了提高锂电池的剩余使用寿命预测的准确性

提出一种多尺度分解和多模型融合的锂电池剩余使用寿命预测模型

以应对容量退化数据中存在噪声和局部波动对电池剩余使用寿命预测的影响。首先

使用自适应噪声的完全集合经验模态分解将原始容量数据分解为若干个分量

其中高频分量包含短期局部变化和噪声

低频分量包含主要退化趋势信息。随后

采用双向长短期记忆网络和高斯过程回归对分解后的高频分量和低频分量分别进行建模

捕捉时间序列数据中的复杂模式和依赖关系

并利用自适应粒子群算法优化模型参数。最后

对预测结果进行叠加融合

并计算锂电池的剩余使用寿命。在公开数据集上通过对比、消融和泛化实验进行分析和测试。实验结果表明

所提模型在锂电池的剩余使用寿命预测任务中AE、MAE 和 RMSE 值最低为0、0.15%和0.18%

具有良好的泛化性和较高的准确性。

Abstract

To enhance the accuracy of RUL predictions

we proposed a novel lithium battery RUL prediction model that integrates multi-scale decomposition with multi-model fusion

effectively addressing noise and local fluctuations in capacity degradation data. Firstly

complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） is employed to decompose the original capacity data into several components. The high-frequency components primarily reflect short-term local variations and noise

while the low-frequency components capture the long-term degradation trends. Secondly

bidirectional long short-term memory （BiLSTM） networks and Gaussian process regression （GPR） are applied to model the decomposed high-frequency and low-frequency components

respectively

capturing the complex patterns and dependencies in the time series data. To further enhance predictive performance

the model parameters are optimized using an adaptive particle swarm optimization （APSO） algorithm. Finally

the individual predictions from each component are aggregated to compute the overall battery RUL. To evaluation on public datasets includes a comprehensive suite of experiments

such as comparison

ablation

and generalization studies. The results demonstrate that the proposed model achieves minimum AE

MAE and RMSE values of 0

0.15%

and 0.18%

respectively

for the RUL prediction task. These findings highlight the model’s excellent generalization ability and high prediction accuracy

establishing its effectiveness for lithium battery RUL prediction.

关键词

Keywords

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