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

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (11) : 98-108. DOI: 10.19912/j.0254-0096.tynxb.2024-1219

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^1,2, 王熠炜¹, 张越¹, 陈佳雷¹, 彭甜^1,2

作者信息 +

LITHIUM-ION BATTERY REMAINING USEFUL LIFE PREDICTION METHOD BASED ON IMPROVED HEAP OPTIMIZATION ALGORITHM AND TIMESNET NEURAL NETWORK

Zhang Chu^1,2, Wang Yiwei¹, Zhang Yue¹, Chen Jialei¹, Peng Tian^1,2

Author information +

文章历史 +

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.

导出引用

张楚, 王熠炜, 张越, 陈佳雷, 彭甜. 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]. 太阳能学报. 2025, 46(11): 98-108 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1219

Zhang Chu, Wang Yiwei, Zhang Yue, Chen Jialei, Peng Tian. LITHIUM-ION BATTERY REMAINING USEFUL LIFE PREDICTION METHOD BASED ON IMPROVED HEAP OPTIMIZATION ALGORITHM AND TIMESNET NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 98-108 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1219

中图分类号： TM912

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