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

共 50 条

[1] An Adaptive Remaining Useful Life Prediction Method for Hybrid Ceramic Bearing
Qu, Yong-Zhi
He, David
2016 INTERNATIONAL CONFERENCE ON MECHANICS DESIGN, MANUFACTURING AND AUTOMATION (MDM 2016), 2016, : 310 - 318
[2] Hybrid method for remaining useful life prediction in wind turbine systems
Djeziri, M. A.
Benmoussa, S.
Sanchez, R.
RENEWABLE ENERGY, 2018, 116 : 173 - 187
[3] A Robust Hybrid Filtering Method for Accurate Battery Remaining Useful Life Prediction
Li, Xifeng
Peng, Libiao
Gao, Le
Bi, Dongjie
Xie, Xuan
Xie, Yongle
IEEE ACCESS, 2019, 7 : 57843 - 57856
[4] A Hybrid Method of Remaining Useful Life Prediction for Aircraft Auxiliary Power Unit
Liu, Xiaolei
Liu, Liansheng
Liu, Datong
Wang, Lulu
Guo, Qing
Peng, Xiyuan
IEEE SENSORS JOURNAL, 2020, 20 (14) : 7848 - 7858
[5] Remaining useful life prediction of PEMFC based on CNN-Birnn model
Luo, Jiale
Chen, Tao
Xiao, Fei
Peng, Yulin
INTERNATIONAL JOURNAL OF GREEN ENERGY, 2023, 20 (15) : 1729 - 1740
[6] Remaining useful life prediction of PEMFC based on cycle reservoir with jump model
Jin, Jiashu
Chen, Yuepeng
Xie, Changjun
Zhu, Wenchao
Wu, Fen
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2021, 46 (80) : 40001 - 40013
[7] Remaining useful life prediction of PEMFC systems under dynamic operating conditions
Hua, Zhiguang
Zheng, Zhixue
Pahon, Elodie
Pera, Marie-Cecile
Gao, Fei
ENERGY CONVERSION AND MANAGEMENT, 2021, 231
[8] A hybrid remaining useful life prediction method for cutting tool considering the wear state
Yifan Li
Yongyong Xiang
Baisong Pan
Luojie Shi
The International Journal of Advanced Manufacturing Technology, 2022, 121 : 3583 - 3596
[9] A hybrid remaining useful life prediction method for cutting tool considering the wear state
Li, Yifan
Xiang, Yongyong
Pan, Baisong
Shi, Luojie
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2022, 121 (5-6): : 3583 - 3596
[10] Remaining Useful Life Prediction Method of PEM Fuel Cells Based on a Hybrid Model
Tian, Qiancheng
Chen, Haitao
Ding, Shuai
Shu, Lei
Wang, Lei
Huang, Jun
ELECTRONICS, 2023, 12 (18)

← 1 2 3 4 5 →