基于DWT-LSTM混合驱动方法的PEMFC多步提前老化预测

杨印龙; 罗马吉

doi:doi:10.19912/j.0254-0096.tynxb.2024-2139

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基于DWT-LSTM混合驱动方法的PEMFC多步提前老化预测

更新时间：2026-07-22

- 基于DWT-LSTM混合驱动方法的PEMFC多步提前老化预测
- 基于DWT-LSTM混合驱动方法的PEMFC多步提前老化预测
- 太阳能学报 2026年47卷第4期页码：575-582
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-2139
  中图分类号： TK91
- 网络首发：2026-05-08，
  
  纸质出版：2026
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杨印龙, 罗马吉. 基于DWT-LSTM混合驱动方法的PEMFC多步提前老化预测[J]. 太阳能学报, 2026,47(4):575-582.

杨印龙, 罗马吉. 基于DWT-LSTM混合驱动方法的PEMFC多步提前老化预测[J]. 2026, 47(4): 575-582.
杨印龙, 罗马吉. 基于DWT-LSTM混合驱动方法的PEMFC多步提前老化预测[J]. 太阳能学报, 2026,47(4):575-582. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2139.

杨印龙, 罗马吉. 基于DWT-LSTM混合驱动方法的PEMFC多步提前老化预测[J]. 2026, 47(4): 575-582. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2139.

摘要

针对质子交换膜燃料电池（PEMFC）在动态工况下的电压多步提前预测精度较低的问题

提出一种基于离散小波变换-长短时记忆神经网络（DWT-LSTM）混合驱动的燃料电池多步提前老化预测方法。首先为消除动态工况下操作参数波动对电压预测的影响

对操作参数与电压进行相关性研究

使用LSTM对相关性强的操作参数和DWT分解电压分别构建预测模型

模型超参数和融合权重由粒子群优化算法（PSO）给出。同时为防止模型多步提前预测误差累计过快

搭建物理老化模型对多步提前预测结果进行修正。最后基于动态工况下老化的电堆耐久性数据进行测试

其中短期预测结果均方根误差（RMSE）和决定系数（R2）达到0.00254 V和0.9926;多步提前预测结果显示该方法能有效预测电压的老化趋势及局部波动

窗口值选择为4 h时均方根误差和决定系数达到0.0224 V和0.8941

程序消融实验也显示了多参数输入以及引入物理模型有利于电压多步提前预测的精度提升。

Abstract

Aiming at the problem of low accuracy of multi-step advance prediction of voltage of proton exchange membrane fuel cell （PEMFC） under dynamic conditions

a fuel cell multi-step advance prediction method based on DWT-LSTM hybrid drive is proposed. Firstly

in order to eliminate the influence of operating parameter fluctuations on voltage prediction under dynamic conditions

the correlation between operating parameters and voltage is studied

and the prediction models are constructed for the operating parameters with strong correlation and discrete wavelet transform （DWT） decomposition voltage respectively using long short-term memory neural network （LSTM）. The model hyperparameters and fusion weights are given by particle swarm optimization algorithm （PSO）. At the same time

in order to prevent the multi-step advance prediction error of the model from accumulating too quickly

a physical aging model is built to correct the multi-step advance prediction results. Finally

tests were carried out based on the durability data of the aged battery stack under dynamic conditions

in which the root mean square error （RMSE） and determination coefficient （） of the short-term prediction results reached 0.00254 and 0.9926; the multi-step advance prediction results show that this method can effectively predict the aging trend and local fluctuation of voltage. When the window value is selected as 4 hours

the root mean square error and determination coefficient reach 0.0224 and 0.8941. The program ablation experiment also shows that multi-parameter input and the introduction of physical models are conducive to improving the accuracy of multi-step advance prediction of voltage.

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references

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