融合动态卷积Transformer与CMA-ES的锂电池寿命预测方法

王雄燃; 张菁

doi:doi:10.19912/j.0254-0096.tynxb.2024-2097

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融合动态卷积Transformer与CMA-ES的锂电池寿命预测方法

更新时间：2025-12-02

- 融合动态卷积Transformer与CMA-ES的锂电池寿命预测方法
- Vol. 46, Issue 6, Pages: 1-8(2025)
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-2097
  CLC： TM912
- Published Online：07 July 2025，
  
  Published：2025
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王雄燃, 张菁. 融合动态卷积Transformer与CMA-ES的锂电池寿命预测方法[J]. 2025, 46(6): 1-8.
DOI：

王雄燃, 张菁. 融合动态卷积Transformer与CMA-ES的锂电池寿命预测方法[J]. 2025, 46(6): 1-8. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2097.

摘要

锂电池寿命预测对能源管理和维护具有重要意义

为解决预测过程中复杂的多维时间序列数据、长时间依赖关系以及特征的动态变化等问题

提出一种基于动态卷积神经网络层和Transformer（DCF）、协方差矩阵自适应调整的进化策略（CMA-ES）和多头自注意力机制的锂电池寿命预测模型。DCF通过动态提取时间序列中的关键特征

降低数据维度和冗余性

捕捉长时间依赖;CMA-ES优化模型超参数

增强模型对局部特征与全局依赖的建模能力;多头自注意力机制则进一步聚焦重要特征

处理复杂的非线性动态关系。使用NASA提供的公开锂电池数据集进行实验验证

结果表明该方法的平均绝对误差最小达到0.28%

优于大部分使用同一数据集的现有方法。实验结果进一步证明

模型在预测准确度和泛化能力上均有提升

尤其在长期寿命预测中展现出更高的精度和鲁棒性

可为锂电池的寿命预测提供更为可靠的技术支持。

Abstract

The prediction of lithium battery lifespan is of great significance for energy management and maintenance. To address challenges such as complex multidimensional time series data

long-term dependencies

and dynamic changes in characteristics during the prediction process

this paper proposes a lithium-ion battery life prediction model based on dynamic convolutional neural networks and Transformer（DCF）

covariance matrix adaptive adjustment evolution strategy（CMA-ES）

and multi-head self-attention mechanism. The DCF dynamically extracts key features from time series data

reduces data dimensionality and redundancy

and captures long-term dependencies. CMA-ES optimizes model hyperparameters to enhance the model's ability to model local features and global dependencies. The multi-head self-attention mechanism further focuses on important features and handles complex nonlinear dynamic relationships. Experimental validation is conducted using the publicly available lithium battery dataset provided by NASA. Results show that the proposed method achieves a minimum average absolute error of 0.28%

outperforming most existing methods using the same dataset. The experiments further demonstrate improvements in prediction accuracy and generalization ability

especially in long-term lifespan prediction

where the model exhibits higher precision and robustness

providing more reliable technical support for lithium battery lifespan prediction.

关键词

Keywords

references

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