基于级联回声状态网络的氢燃料电池剩余使用寿命预测

潘诗媛; 华志广; 王光伟; 赵冬冬; 窦满峰

doi:10.13334/j.0258-8013.pcsee.232370

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基于级联回声状态网络的氢燃料电池剩余使用寿命预测

可再生能源与储能 | 更新时间：2026-01-19

- 基于级联回声状态网络的氢燃料电池剩余使用寿命预测
- Remaining Useful Life Prediction of Hydrogen Fuel Cell Based on Cascade Echo State Network
- 中国电机工程学报 2025年45卷第12期页码：4718-4727
- 作者机构：
  
  1. 西北工业大学自动化学院, 陕西省西安市 710072
  2. 中国船舶集团有限公司第七〇七研究所, 陕西省西安市 710072
- 作者简介：
  
  [ "潘诗媛(2001)，女，硕士研究生，研究方向为燃料电池寿命预测，panshiyuan@mail.nwpu.edu.cn" ]
  华志广(1990)，男，教授，博士生导师，主要从事氢燃料电池寿命预测及健康管理方面研究工作，zhiguang.hua@nwpu.edu.cn
  [ "王光伟(1983)，男，高级工程师，主要从事自动控制系统方向研究，ray7772008@163.com" ]
  [ "赵冬冬(1985)，男，教授，博士生导师，主要从事氢燃料电池动力系统、电机驱动控制方向的研究，zhaodong@nwpu.edu.cn" ]
  [ "窦满峰(1967)，男，博士，教授，博士生导师，主要从事航空航天稀土电机设计、机器人关节电机设计及控制等方面研究，nwpu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金项目(52277226);国家自然科学基金项目(52307251);中国博士后科学基金项目(2023TQ0277)
- DOI：10.13334/j.0258-8013.pcsee.232370
  中图分类号： TM911
- 收稿：2023-12-10，
  
  网络出版：2024-03-19，
  
  纸质出版：2025-06-20
- 稿件说明：
移动端阅览
潘诗媛, 华志广, 王光伟, 等. 基于级联回声状态网络的氢燃料电池剩余使用寿命预测[J]. 中国电机工程学报, 2025,45(12):4718-4727.

Shiyuan PAN, Zhiguang HUA, Guangwei WANG, et al. Remaining Useful Life Prediction of Hydrogen Fuel Cell Based on Cascade Echo State Network[J]. Proceedings of the CSEE, 2025, 45(12): 4718-4727.
潘诗媛, 华志广, 王光伟, 等. 基于级联回声状态网络的氢燃料电池剩余使用寿命预测[J]. 中国电机工程学报, 2025,45(12):4718-4727. DOI： 10.13334/j.0258-8013.pcsee.232370.

Shiyuan PAN, Zhiguang HUA, Guangwei WANG, et al. Remaining Useful Life Prediction of Hydrogen Fuel Cell Based on Cascade Echo State Network[J]. Proceedings of the CSEE, 2025, 45(12): 4718-4727. DOI： 10.13334/j.0258-8013.pcsee.232370.

摘要

该文针对质子交换膜燃料电池(proton exchange membrane fuel cell，PEMFC)剩余使用寿命(remaining useful life，RUL)长期预测过程中预测精度低的问题，提出一种改进型回声状态网络(echo state network，ESN)结构以实现PEMFC输出电压的精准预测。采用多个蓄水池构建一种级联型回声状态网络(cascade echo state network，CasESN)结构，利用遗传算法(genetic algorithm，GA)优化CasESN网络结构参数，并基于优化后的CasESN实现PEMFC输出电压在未来数百个小时区间内的长期预测。在稳态和准动态数据集下分别对所提出的CasESN网络结构进行测试并与经典ESN进行量化比较。稳态和准动态数据集的训练时长分别为700和750 h时，2CasESN比单个ESN的均方根误差(root mean square error，RMSE)分别降低11.94%和23.67%。结果表明，级联型ESN结构能够在一定程度上提高PEMFC寿命预测精度。

Abstract

Aiming at the problem of low prediction accuracy in the long-term prediction process of proton exchange membrane fuel cell (RUL)

an improved echo state network (ESN) structure is proposed to achieve accurate prediction of PEMFC output voltage. A cascaded echo state network (CasESN) structure is constructed by using multiple reservoirs

and the parameters of CasESN network structure are optimized by genetic algorithm (GA). Based on the optimized CasESN

the long-term prediction of PEMFC output voltage in the next hundreds of hours is realized. The proposed CasESN network structure is tested under steady-state and quasi- dynamic data sets

and compared quantitatively with the classical ESN. When the training time of the steady-state and quasi-dynamic data sets is 700 h and 750 h

respectively

the root mean square error (RMSE) of 2CasESN is 11.94% and 23.67% lower than that of a single ESN

respectively. The results show that the cascaded ESN structure can improve the accuracy of PEMFC life prediction to a certain extent.

关键词

Keywords

references

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