基于改进堆优化算法和TimesNet神经网络的锂电池剩余寿命预测方法

张楚; 王熠炜; 张越; 陈佳雷; 彭甜

doi:doi:10.19912/j.0254-0096.tynxb.2024-1219

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基于改进堆优化算法和TimesNet神经网络的锂电池剩余寿命预测方法

更新时间：2025-12-10

- 基于改进堆优化算法和TimesNet神经网络的锂电池剩余寿命预测方法
- Issue 11, Pages: 98-108(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1219
  CLC： M912
- Published Online：04 December 2025，
  
  Published：04 December 2025
- 稿件说明：
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张楚, 王熠炜, 张越, et al. 基于改进堆优化算法和TimesNet神经网络的锂电池剩余寿命预测方法[J]. 2025, (11): 98-108.
DOI：

张楚, 王熠炜, 张越, et al. 基于改进堆优化算法和TimesNet神经网络的锂电池剩余寿命预测方法[J]. 2025, (11): 98-108. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1219.

摘要

提出一种基于改进堆优化算法和TimesNet神经网络的锂电池剩余寿命（RUL）预测方法。首先

采用核主成分分析（KPCA）对原始数据实施降维与去冗余操作

以提取关键特征。随后

构建一个基于TimesNet的深度学习模型来评估电池的RUL。为进一步提升模型性能

结合改进的堆优化算法（IHBO）对TimesNet模型的超参数进行优化。此外

引入误差校正机制来提高预测精度。实验结果表明

该方法在预测锂电池RUL方面具有较高的准确性

其均方根误差（RMSE）达到1.9937次

优于传统的时间序列模型。

Abstract

This paper proposes a method for predicting the remaining useful life （RUL） of lithium-ion batteries based on an improved heap-based optimizer （HBO） and a TimesNet neural network. Firstly

kernel principal component analysis （KPCA） is used to perform dimensionality reduction and redundancy removal on the raw data to extract key features. Subsequently

a deep learning model based on TimesNet is constructed to evaluate the RUL of the battery. To further improve the performance of the model

the hyperparameters of the TimesNet model are optimized using the improved heap-based optimizer （IHBO）. In addition

an error correction mechanism is introduced to enhance the prediction accuracy. Experimental results show that this method has high accuracy in predicting the RUL of lithium-ion batteries

with a root mean square error（RMSE） of 1.9937 time

which is superior to traditional time-series models.

关键词

Keywords

references

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