中国电力2025，Vol.58Issue(5)：102-109,8.DOI:10.11930/j.issn.1004-9649.202402054
基于改进回声状态网络的质子交换膜燃料电池剩余寿命预测
Residual Life Prediction of Proton Exchange Membrane Fuel Cell Based on Improved ESN
摘要
Abstract
Aiming at the problem that the current residual effective life prediction(RUL)technique for proton exchange membrane fuel cells(PEMFCs)has poor prediction effect in the medium and long term,a residual life prediction method based on the Improved Gray Wolf Optimization algorithm(IGWO)and Echo State Network(ESN)is proposed,in which the voltage of the electric stack is firstly selected as a health indicator,and the PEMFC dataset is processed by using convolutional smoothing filtering method to carry out data Smoothing and normalization are used to effectively reduce the interference of outliers on the subsequent model training.Then the reserve pool parameters of the ESN are optimized using the local and global optimization search capability of IGWO,and the IGWO-ESN network model is constructed,and the processed dataset is used for the training of the remaining life prediction model of the PEMFC,and finally it is compared with the traditional ESN for verification.The results show that the improved ESN model predicts the root mean square error and average absolute percentage error of 0.034 2 and 0.931 5%,respectively,and the prediction accuracy is significantly improved compared with the ordinary ESN model,and the prediction accuracy of the medium-and long-term RUL is also higher.关键词
质子交换膜燃料电池/回声状态网络/灰狼优化算法/剩余寿命预测Key words
proton exchange membrane fuel cell/echo state network/gray wolf optimization algorithm/remaining life prediction引用本文复制引用
袁铁江,李荣盛,康建东,闫华光..基于改进回声状态网络的质子交换膜燃料电池剩余寿命预测[J].中国电力,2025,58(5):102-109,8.基金项目
国家电网有限公司科技项目(兆瓦级电氢融合能源枢纽灵活高效运行及主动支撑关键技术研究与示范,5108-202218280A-2-386-XG). This work is supported by Science and Technology Project of SGCC(Research and Demonstration of Key Technologies for Flexible and Efficient Operation and Active Support of Megawatt-Level Electric-Hydrogen Integrated Energy Hub,No.5108-202218280A-2-386-XG). （兆瓦级电氢融合能源枢纽灵活高效运行及主动支撑关键技术研究与示范,5108-202218280A-2-386-XG）
