基于TCN和AUKF联合迭代的PEMFC寿命融合预测方法  

PEMFC Remaining Useful Life Fusion Method Based on Joint Iteration of TCN and AUKF

作　　者：赵波 张领先 章雷其 陈哲 刘相万 谢长君[2] ZHAO Bo;ZHANG Lingxian;ZHANG Leiqi;CHEN Zhe;LIU Xiangwan;XIE Changjun(State Grid Zhejiang Electric Power Company Research Institute,Hangzhou 310014,Zhejiang Province,China;School of Automation,Wuhan University of Technology,Wuhan 430070,Hubei Province,China)

机构地区：[1]国网浙江省电力有限公司电力科学研究院,浙江省杭州市310014 [2]武汉理工大学自动化学院,湖北省武汉市430070

出　　处：《中国电机工程学报》2025年第9期3609-3623,I0029,共16页PROCEEDINGS OF THE CHINESE SOCIETY FOR ELECTRICAL ENGINEERING

基　　金：国家重点研发计划项目(2020YFB1506800);国家电网有限公司科技项目(52110421005H)。

摘　　要：针对质子交换膜燃料电池(proton exchange membrane fuel cell,PEMFC)的剩余使用寿命预测问题,该文提出一种基于时序卷积神经网络(temporal convolutional network,TCN)和自适应无迹卡尔曼滤波(adaptive unscented Kalman filter,AUKF)联合迭代的融合预测方法。该方法首先利用TCN进行短期预测,并用贝叶斯算法计算融合权重。然后利用离散小波变换将老化数据分解为波动趋势和老化趋势,基于TCN长期迭代预测波动趋势,基于TCN和AUKF联合迭代长期预测老化趋势,并将两种趋势叠加得到长期预测结果。最后利用融合权重将多个单体PEMFC的长期预测结果相融合。基于2种工况下5个单体电池的数据验证,短期预测结果表明TCN具有高预测精度,长期预测结果表明融合过程降低了PEMFC单体间老化程度不均衡的影响,提高电堆整体寿命预测的稳定性。Aiming at the problem of predicting the remaining service life of proton exchange membrane fuel cell(PEMFC),this paper proposes a fusion prediction method based on the joint iteration of temporal convolutional neural network(TCN)and adaptive unscented Kalman filter(AUKF).This method first uses TCN for short-term prediction and uses Bayesian algorithm to calculate fusion weights.Then the discrete wavelet transform is used to decompose the aging data into fluctuation trends and aging trends.The fluctuation trend is predicted in a long-term iteration based on TCN.The aging trend is predicted in a long-term iteration based on the joint iteration of TCN and AUKF.The two trends are superimposed to obtain the long-term prediction results.Finally,fusion weights are used to fuse the long-term prediction results of multiple single PEMFCs.Based on the data verification of five single cells under two working conditions,the short-term prediction results show that TCN has high prediction accuracy.The long-term prediction results show that the fusion process reduces the impact of uneven aging between PEMFC cells and improves the accuracy of stack life prediction.stability.

关 键 词：质子交换膜燃料电池 剩余使用寿命 时序卷积网络 联合迭代 融合权重 

分 类 号：TM911[电气工程—电力电子与电力传动]