多特征融合的MVMD-VSN_GRU锂离子电池健康状态预测方法研究

吴康佳; 姬钰培; 冀鹏宇; 袁泽宇; 崔方舒; 乔钢柱

doi:10.19799/j.cnki.2095-4239.2026.0198

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多特征融合的MVMD-VSN_GRU锂离子电池健康状态预测方法研究

XXXX | 更新时间：2026-04-30

- 多特征融合的MVMD-VSN_GRU锂离子电池健康状态预测方法研究
- Research on State of Health Prediction Method of Lithium-Ion Batteries Based on Multi-Feature Fusion MVMD-VSN_GRU
- 储能科学与技术 2026年页码：1-14
- 作者机构：
  
  1.中北大学计算机科学与技术学院，山西太原 030051
  2.中北大学极限环境光电动态测试技术与仪器全国重点实验室，山西太原 030051
- 作者简介：
  
  吴康佳（2001—），女，硕士研究生，研究方向为储能电池健康管理，E-mail： kjia0215@163.com；
  乔钢柱，教授，研究方向为物联网技术及应用、大数据处理技术等，E-mail： qiaogz@nuc.edu.cn。
- 基金信息：
  
  山西省基础研究计划(202303021222084);山西省基础研究计划(202303021222120)
- DOI：10.19799/j.cnki.2095-4239.2026.0198
  中图分类号： TM912
- 收稿：2026-03-10，
  
  修回：2026-04-29，
  
  网络首发：2026-04-30，
- 稿件说明：
移动端阅览
吴康佳, 姬钰培, 冀鹏宇, 等. 多特征融合的MVMD-VSN_GRU锂离子电池健康状态预测方法研究[J]. 储能科学与技术, XXXX, XX(XX): 1-14.

WU Kangjia, JI Yupei, JI Pengyu, et al. Research on State of Health Prediction Method of Lithium-Ion Batteries Based on Multi-Feature Fusion MVMD-VSN_GRU[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-14.
吴康佳, 姬钰培, 冀鹏宇, 等. 多特征融合的MVMD-VSN_GRU锂离子电池健康状态预测方法研究[J]. 储能科学与技术, XXXX, XX(XX): 1-14. DOI： 10.19799/j.cnki.2095-4239.2026.0198.

WU Kangjia, JI Yupei, JI Pengyu, et al. Research on State of Health Prediction Method of Lithium-Ion Batteries Based on Multi-Feature Fusion MVMD-VSN_GRU[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-14. DOI： 10.19799/j.cnki.2095-4239.2026.0198.

摘要

在新能源汽车、大规模储能电站等领域快速发展的背景下，精准预测锂离子电池健康状态（state of health，SOH）对于保障电池系统安全稳定运行以及延长服役寿命具有重要意义。针对锂离子电池健康状态直接性能参数获取困难，以及传统时序预测模型中单一变量预测精度偏低、多变量权重固定导致SOH预测精度受限的问题，文章提出一种基于变量选择网络（variable selection network，VSN）与门控循环单元（gated recurrent unit，GRU）的多特征融合的锂离子电池SOH预测模型。该方法从电池充放电循环数据中提取与SOH高度相关的健康因子（health indicator，HI）构成多元特征序列，采用多元变分模态分解对此多特征序列进行协同自适应分解，以充分提取并保留变量间的耦合时频特征。在每个分解得到的多元子序列上，构建基于袋獾优化算法超参数寻优的VSN_GRU模型，实现对关键健康因子的动态感知与建模预测。最后，将各分解分量的预测结果叠加求和得到最终SOH预测值。在NASA电池数据集与WZU随机电池退化数据上的实验结果表明，相较于消融实验中的对比模型，文中模型可有效提升锂离子电池SOH的预测精度。

Abstract

Against the backdrop of the rapid development of new energy vehicles

large-scale energy storage power stations and other fields

the accurate prediction of the state of health (SOH) of lithium-ion batteries is of great significance for ensuring the safe and stable operation of battery systems and extending their service life. Aiming at the difficulties in directly acquiring the performance parameters of lithium-ion battery state of health

as well as the limited SOH prediction accuracy of traditional time-series prediction models caused by low single-variable prediction precision and fixed multi-variable weights

this paper proposes a multi-feature fusion lithium-ion battery SOH prediction model based on the variable selection network (VSN) and gated recurrent unit (GRU). Health indicators (HIs) highly correlated with SOH are extracted from battery charge-discharge cycle data to form a multivariate feature sequence

which is then adaptively decomposed collaboratively via multivariate variational mode decomposition to fully extract and preserve the coupled time-frequency features among variables. For each decomposed multivariate subsequence

a VSN_GRU model with hyperparameter optimization by the Tasmanian devil optimization algorithm is constructed to realize dynamic perception

modeling and prediction of key HIs. Finally

the final SOH prediction value is obtained by superposing and summing the prediction results of all decomposed components. Experimental results on the NASA battery dataset and WZU random battery degradation dataset demonstrate that compared with the comparison models in ablation experiments

the proposed model can effectively improve the prediction accuracy of lithium-ion battery SOH.

关键词

Keywords

references

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