基于双向长短期记忆网络与注意力机制的锂电池剩余使用寿命预测模型

朱光明; 胡锦豪; 徐松; 万代

doi:10.3969/j.issn.1008-0198.2025.05.001

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基于双向长短期记忆网络与注意力机制的锂电池剩余使用寿命预测模型

专家专栏：配网灵活储能技术 | 更新时间：2026-01-10

- 基于双向长短期记忆网络与注意力机制的锂电池剩余使用寿命预测模型
- Remaining Useful Life Prediction Model Based on Bidirectional Long Short-Term Memory Network and Attention Mechanism for Lithium Batteries
- 湖南电力 2025年45卷第5期页码：1-7
- 作者机构：
  
  1. 国网湖南省电力有限公司电力科学研究院,湖南,长沙,410208
  2. 高效清洁发电技术湖南省重点实验室,湖南,长沙,410208
  3. 湖南省湘电试验研究院有限公司,湖南,长沙,410208
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2025.05.001
  中图分类号： TM911
- 网络出版：2025-11-11，
  
  纸质出版：2025-11-11
- 稿件说明：
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朱光明, 胡锦豪, 徐松, 万代. 基于双向长短期记忆网络与注意力机制的锂电池剩余使用寿命预测模型[J]. 湖南电力, 2025, 45(5): 1-7.

朱光明, 胡锦豪, 徐松, et al. Remaining Useful Life Prediction Model Based on Bidirectional Long Short-Term Memory Network and Attention Mechanism for Lithium Batteries[J]. 2025, 45(5): 1-7.
朱光明, 胡锦豪, 徐松, 万代. 基于双向长短期记忆网络与注意力机制的锂电池剩余使用寿命预测模型[J]. 湖南电力, 2025, 45(5): 1-7. DOI： 10.3969/j.issn.1008-0198.2025.05.001.

朱光明, 胡锦豪, 徐松, et al. Remaining Useful Life Prediction Model Based on Bidirectional Long Short-Term Memory Network and Attention Mechanism for Lithium Batteries[J]. 2025, 45(5): 1-7. DOI： 10.3969/j.issn.1008-0198.2025.05.001.

摘要

为提升锂电池剩余使用寿命预测的可靠性与准确性

构建一种基于双向长短期记忆网络（bidirectional long short-term memory

BiLSTM)-注意力（Attention)机制的锂电池剩余使用寿命预测模型。该模型先以锂电池充放电过程中的关键性能参数为输入

利用BiLSTM网络的双向记忆特性

充分捕捉电池性能退化过程中的长短期时间依赖关系

有效挖掘隐藏在数据中的退化趋势特征。其次

引入Attention机制

对影响剩余使用寿命预测的关键时间步特征赋予更高权重

强化模型对重要信息的聚焦能力

进而输出精准的剩余使用寿命预测值。通过在CALCE公开的4组锂电池数据集上进行验证分析

实验结果表明

BiLSTM-Attention模型的R

值分别达到98.77%、99.64%、99.50%、98.47%

与其他预测方法相比

该方法展现出更优的预测性能。

Abstract

To enhance the reliability and accuracy of predictions

a lithium battery RUL prediction model based on Bidirectional Long Short-Term Memory Network(BiLSTM) and Attention mechanism is constructed. Firstly

the model takes the key performance parameters during the charge-discharge process of lithium batteries as the inputs

and utilizes the bidirectional memory characteristics of the BiLSTM network to fully capture the long-term and short-term temporal dependencies in the battery performance degradation process and effectively exca

vates the degradation trend features hidden in the data. Secondly

the Attention mechanism is introduced to assign higher weights to the key time-step features that affect RUL prediction

strengthening the model's ability to focus on important information and thereby outputting accurate RUL prediction values. Validation and analysis on four sets of public CALCE lithium battery datasets show that the R

values of the BiLSTM-Attention model reach 98.77%

99.64%

99.50%

and 98.47% respectively. Compared with other prediction methods

this method exhibits superior prediction performance.

关键词

Keywords

references

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