State of health prediction for vanadium flow batteries using adaptive variational mode decomposition and reconstruction

MAO Hengshan; LIU Haoji; LIU Xiaojie; WANG Jie; LI Wei; HU Weihao; HAN Wenjie; QIU Jie; WANG Xiao; XIONG Binyu

doi:10.19799/j.cnki.2095-4239.2025.0962

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State of health prediction for vanadium flow batteries using adaptive variational mode decomposition and reconstruction

Energy Storage Test\: Methods and Evaluation | 更新时间：2026-07-01

- State of health prediction for vanadium flow batteries using adaptive variational mode decomposition and reconstruction
- Energy Storage Science and Technology Vol. 15, Issue 4, Pages: 1412-1424(2026)
- 作者机构：
  
  1.中电建新能源集团股份有限公司，北京 100101
  2.武汉理工大学自动化学院，湖北武汉 430000
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2025.0962
  CLC： TM 911
- Received：27 October 2025，
  
  Revised：2025-11-22，
  
  Published：28 April 2026
- 稿件说明：
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毛恒山, 刘浩骥, 刘晓杰, 等. 基于自适应变分模态分解与重构的全钒液流电池健康状态预测[J]. 储能科学与技术, 2026, 15(4): 1412-1424.

MAO Hengshan, LIU Haoji, LIU Xiaojie, et al. State of health prediction for vanadium flow batteries using adaptive variational mode decomposition and reconstruction[J]. Energy Storage Science and Technology, 2026, 15(4): 1412-1424.
毛恒山, 刘浩骥, 刘晓杰, 等. 基于自适应变分模态分解与重构的全钒液流电池健康状态预测[J]. 储能科学与技术, 2026, 15(4): 1412-1424. DOI： 10.19799/j.cnki.2095-4239.2025.0962.

MAO Hengshan, LIU Haoji, LIU Xiaojie, et al. State of health prediction for vanadium flow batteries using adaptive variational mode decomposition and reconstruction[J]. Energy Storage Science and Technology, 2026, 15(4): 1412-1424. DOI： 10.19799/j.cnki.2095-4239.2025.0962.

摘要

全钒液流电池(vanadium flow batteries，VFBs)在长期运行过程中，常因电解液体积失衡而引起异常容量衰减。因此对其健康状态(state of health，SOH)进行准确预测，对维持系统稳定运行至关重要。本研究基于电池循环老化数据，提出采用自适应变分模态分解与重构(adaptive variational mode decomposition and reconstruction，AVMDR)方法，以处理电池老化过程中的容量再生现象，并将其应用于SOH时间序列分析。基于相关性分析，重构了表征容量再生特征的波动函数

(

)与表征主体容量衰减趋势的主趋势函数

(

)。进而结合两者特性，分别采用长短期记忆网络(long short-term memory，LSTM)和Transformer模型，构建融合神经网络(integrated neural network，INN)模型。此外，通过概率分布计算以处理预测结果的不确定性问题。通过实验所得老化数据验证了所提融合模型的可行性与有效性。结果表明：该模型在多时间尺度下的预测均方根误差(RMSE)可保持在0.45%以内，在精度与稳定性方面均优于其他模型。

Abstract

Vanadium flow batteries (VFBs) are susceptible to abnormal capacity decay due to electrolyte volume imbalance during long-term operation. Therefore

accurate state of health (SOH) prediction is essential for maintaining system stability. In this study

a battery cycle aging data-based method based on adaptive variational mode decomposition and reconstruction (AVMDR) is proposed to address the capacity regeneration phenomenon observed during battery aging. The proposed method is applied to state-of-health time-series analysis. Correlation analysis is employed

to reconstruct a fluctuation function

(

) characterizing the capacity regeneration features and a main trend function

(

) representing the dominant capacity decay trend. An integrated neural network (INN) model is then constructed by employing a long short-term memory (LSTM) network and a Transformer model to handle the distinct characteristics of functions

(

) and

(

)

respectively. Furthermore

probability distribution calculations are performed to address the uncertainties in the prediction outcomes. The feasibility and effectiveness of the proposed hybrid model are validated using experimental aging data. Results demonstrate that the model maintains a root mean square error below 0.45% across multiple time scales

outperforming conventional models in both accuracy and stability.

关键词

Keywords

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