A Hybrid Method for Direct Prediction of PEMFC Remaining Useful Life

被引：0

作者：

Zhao, Bo ^{[1
]}

Zhang, Lingxian ^{[2
]}

Zhang, Leiqi ^{[1
]}

Xie, Changjun ^{[2
]}

Chen, Zhe ^{[1
]}

Liu, Xiangwan ^{[2
]}

机构：

[1] State Grid Zhejiang Electric Power Company Research Institute, Zhejiang Province, Hangzhou,310014, China

[2] School of Automation, Wuhan University of Technology, Hubei Province, Wuhan,430070, China

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2024年 / 44卷 / 21期

关键词：

For the remaining lifetime prediction problem of proton exchange membrane fuel cell (PEMFC); this paper proposes a hybrid prediction method combining data-driven and model-driven by direct prediction approach based on a dynamic semi-empirical model of PEMFC considering the double-layer capacitance effect. For the data-driven approach; features of multi-dimensional aging data are extracted using a deep convolutional network and passed to a long and short-term memory network for aging voltage prediction. For model-driven; the voltage predictions are used as observations in an adaptive extended Kalman filtering framework. Short-term and long-term predictions are performed using hybrid prediction methods based on aging data under two operating conditions; static and dynamic; respectively. The short-term prediction results show that the dynamic semi-empirical model can fit the aging voltage data more effectively under dynamic conditions. The long-term prediction results show that the prediction error based on the dynamic semi-empirical model is smaller; and the remaining useful life of PEMFC predicted by the hybrid method is closer to the real value. ©2024 Chin.Soc.for Elec.Eng;

D O I：

10.13334/j.0258-8013.pcsee.230769

中图分类号：

学科分类号：

摘要：

引用

页码：8554 / 8567

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