质子交换膜燃料电池退化预测方法  

Degradation Prediction Method of Proton Exchange Membrane Fuel Cell

作　　者：汪建锋 王荣杰[1,2] 林安辉 王亦春[1] 张博[1] Wang Jianfeng;Wang Rongjie;Lin Anhui;Wang Yichun;Zhang Bo(School of Marine Engineering,Jimei University,Xiamen 361021,China;State Key Laboratory of Electrical Insulation and Power Equipment,Xi’an Jiaotong University,Xi’an,710049,China)

机构地区：[1]集美大学轮机工程学院,厦门361021 [2]电工材料电气绝缘国家重点实验室(西安交通大学),西安710049

出　　处：《电工技术学报》2024年第11期3367-3378,共12页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金(51879118);福建省自然科学基金(2020J01688);电力设备电气绝缘国家重点实验室基金(EIPE23202);福建省中青年教师教育科研项目(JAT220173)资助。

摘　　要：耐久度是制约质子交换膜燃料电池大规模应用的主要障碍之一,性能退化预测技术可以有效提高质子交换膜燃料电池的耐久度。该文提出一种结合小波阈值去噪方法的正则化堆叠长短期记忆网络的性能退化预测方法。通过小波阈值去噪法,获得消除噪声和尖峰后的平滑数据。针对退化数据不确定性和高度非线性导致的特征难以提取问题,引入了正则化堆叠长短期记忆网络模型,该模型通过引入参数优化算法有效地避免了过拟合风险,提高了预测精度和可靠性。为验证该方法的有效性,采用两种不同工况下的质子交换膜燃料电池老化数据进行验证。结果表明,所提方法在稳态工况下的最大误差为0.0163V,误差区间在0.5%以内;动态工况下的最大误差为0.0064 V,误差区间在0.2%以内。Durability is one of the main obstacles to the large-scale application of proton exchange membrane fuel cell(PEMFC).Performance degradation prediction technology can effectively improve the durability of PEMFC.Through the study of PEMFC aging data,it is found that the actual PEMFC aging data is highly nonlinear,periodic and random,which makes it difficult for the prediction algorithm to extract the features effectively.In addition,in the problem of degradation prediction,the prediction algorithm needs to predict the degradation of PEMFC under different working conditions,which requires the prediction algorithm to have stronger generalization ability.To solve the above problems,a performance degradation prediction method of regularization stack long short-term memory combined with wavelet threshold denoising method(WTD-RS-LSTM)method is proposed.Firstly,the WTD method is used to process the original data,and the smooth data after eliminating noise and spikes is obtained by wavelet decomposition,threshold processing and data reconstruction.Then the RS-LSTM model is introduced to solve the problem of feature extraction caused by uncertainty and high nonlinearity of degraded data.The generalization ability of the model is improved by introducing parameter optimization algorithm.The model is stacked to enhance its learning ability.For increase the reliability of the model,Warmup strategy was used to dynamically adjust the learning rate of the network.Through the above operations,the overfitting phenomenon which may occur in the training of the model is effectively avoided,and the prediction accuracy and reliability of the prediction algorithm are improved.For verify the effectiveness of the proposed method,PEMFC aging data under two different working conditions are used for verification.The datasets under different working conditions are divided into five different lengths of training sets and test sets to train and test the proposed algorithm.The verification results show that under steady-state conditions,the maximu

关 键 词：质子交换膜燃料电池 性能退化预测 小波阈值去噪 长短期记忆网络 

分 类 号：TM911[电气工程—电力电子与电力传动] TK91[动力工程及工程热物理]