Degradation Prediction Method of Proton Exchange Membrane Fuel Cell
Wang Jianfeng1, Wang Rongjie1,2, Lin Anhui1, Wang Yichun1, Zhang Bo1
1. School of Marine Engineering Jimei University Xiamen 361021 China; 2. State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong University Xi’an 710049 China
Abstract: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 maximum error of the proposed method is 0.016 3 V, and the error interval is within 0.5%. The prediction performance increases with the training length, and the best prediction performance is obtained at the training length of 1 000 h, when the RMSE and MAPE are 0.000 91 and 0.000 22%, respectively. Under dynamic conditions, the maximum error is 0.006 4 V and the error interval is within 0.2%. The best performance was achieved when the training length was 550 h, when the RMSE and MAPE are 0.000 75 and 0.000 20%, respectively. According to the above experimental results and the comparison with the existing traditional algorithms, the following conclusions are drawn: (1) the proposed method can make more accurate PEMFC degradation prediction under different working conditions and different training lengths, and has stronger generalization ability; (2) Comparing the prediction accuracy of the two conditions under different training lengths, it is found that the prediction of PEMFC degradation under dynamic conditions by the proposed method is better than that under steady-state conditions. Therefore, the proposed method has stronger prediction ability under dynamic conditions. (3) The proposed method has a simple structure, easy to deploy and is suitable for online application; (4) The aging of PEMFC under dynamic conditions will produce more randomness, which will have a great impact on the stability of the prediction algorithm.
[1] 陈锦洲, 林飞, 何洪文, 等. 质子交换膜燃料电池/电解槽系统建模及负荷追踪策略[J]. 电工技术学报, 2020, 35(增刊2): 636-643. Chen Jinzhou, Lin Fei, He Hongwen, et al.Proton exchange membrane fuel cell/electrolyzer hybrid power system modeling and load tracking strategy[J]. Transactions of China Electrotechnical Society, 2020, 35(S2): 636-643. [2] 李梓丘, 乔颖, 鲁宗相. 海上风电-氢能系统运行模式分析及配置优化[J]. 电力系统自动化, 2022, 46(8): 104-112. Li Ziqiu, Qiao Ying, Lu Zongxiang, et al.Operation mode analysis and configuration optimization of offshore wind-hydrogen system[J]. Automation of Electric Power Systems, 2022, 46(8): 104-112. [3] Song Yuxi, Zhang Caizhi, Ling Chunyu, et al.Review on current research of materials fabrication and application for bipolar plate in proton exchange membrane fuel cell[J]. International Journal of Hydrogen Energy, 2020, 45(54): 29832-29847. [4] 张雪霞, 黄平, 蒋宇, 等. 动态机车工况下质子交换膜燃料电池电堆衰退性能分析[J]. 电工技术学报, 2022, 37(18): 4798-4806. Zhang Xuexia, Huang Ping, Jiang Yu, et al.Degradation performance analysis of proton exchange membrane fuel cell stack under dynamic locomotive conditions[J]. Transactions of China Electrotechnical Society, 2022, 37(18): 4798-4806. [5] 高锋阳, 高翾宇, 张浩然, 等. 全局与瞬时特性兼优的燃料电池有轨电车能量管理策略[J]. 电工技术学报, 2023, 38(21): 5923-5938. Gao Fengyang, Gao Xuanyu, Zhang Haoran, et al.Management strategy for fuel cell trams with both global and transient characteristics[J]. Transactions of China Electrotechnical Society. 2023, 38(21): 5923-5938. [6] 马小勇, 王议锋, 王萍, 等. 燃料电池用交错并联型Boost变换器参数综合设计方法[J]. 电工技术学报, 2022, 37(2): 397-408. Ma Xiaoyong, Wang Yifeng, Wang Ping, et al.Comprehensive parameter design method of interleaved Boost converter for fuel cell applications[J]. Transactions of China Electrotechnical Society, 2022, 37(2): 397-408. [7] 唐钧涛, 戚志东, 裴进, 等. 基于电荷泵的燃料电池有源网络升压变换器[J]. 电工技术学报, 2022, 37(4): 905-917. Tang Juntao, Qi Zhidong, Pei Jin, et al.An active network DC-DC Boost converter with a charge pump employed in fuel cells[J]. Transactions of China Electrotechnical Society, 2022, 37(4): 905-917. [8] Taghiabadi M M, Zhiani M, Silva V, et al.Effect of MEA activation method on the long-term performance of PEM fuel cell[J]. Applied Energy, 2019, 242: 602-611. [9] Jouin M, Gouriveau R, Pera M, et al.Prognostics and health management of PEMFC-state of the art and remaining challenges[J]. International Journal of Hydrogen Energy, 2013, 38(35): 15307-15317. [10] Liu Hao, Chen Jian, Hissel D, et al.Prognostics methods and degradation indexes of proton exchange membrane fuel cells: a review[J]. Renewable and Sustainable Energy Reviews, 2020, 123: 109721. [11] Pei Pucheng, Chen Dongfang, Wu Zhiyao, et al.Nonlinear methods for evaluating and online predicting the lifetime of fuel cells[J]. Applied Energy, 2019, 254: 113730. [12] Ou Mingyang, Zhang Ruofan, Shao Zhifang, et al.A novel approach based on semi-empirical model for degradation prediction of fuel cells[J]. Journal Of Power Sources, 2020, 488: 229435-229445. [13] Zhang Xinfeng, Yang Daijun, Luo Minghui, et al.Load profile based empirical model for the lifetime prediction of an automotive PEM fuel cell[J]. International Journal of Hydrogen Energy, 2017, 42(16): 11868-11878. [14] Ibrahim M, Steiner Y, Jemei S, et al.Wavelet-based approach for online fuel cell remaining useful lifetime prediction[J]. IEEE Transactions Industrial Electronics, 2016, 63(8): 5057-5068. [15] Liu Hui, Liu Zhenyu, Jia Weiqiang, et al.Remaining useful life prediction using a novel feature-attention-based end-to-end approach[J]. IEEE Transactions on Industrial Informatics, 2016, 17(2): 1197-1207. [16] Cheng Yujie, Zerhouni N, et al.A hybrid remaining useful life prognostic method for proton exchange membrane fuel cell[J]. International Journal of Hydrogen Energy, 2018, 43(27): 12314-12327. [17] Ma Jian, Liu Xue, Zou Xinyu, et al.Degradation prognosis for proton exchange membrane fuel cell based on hybrid transfer learning and intercell differences[J]. ISA Transactions, 2021, 113: 149-165. [18] Zhou Daming, Gao Fei, Elena B, et al.Degradation prediction of PEM fuel cell using a moving window based hybrid prognostic approach[J]. Energy, 2017, 138: 1175-1186. [19] Chen K, Laghrouche S.Fuel cell health prognosis using unscented Kalman filter: Postal fuel cell electric vehicles case study[J]. International Journal of Hydrogen Energy, 2019, 44(3): 1930-1939. [20] Bressel M, Hilairet M, Hissel D, et al.Extended Kalman filter for prognostic of proton exchange membrane fuel cell[J]. Applied Energy, 2016, 164: 220-227. [21] Zhou Daming, Al-Durra A, Zhang Ke, et al.Online remaining useful lifetime prediction of proton exchange membrane fuel cells using a novel robust methodology[J]. Journal of Power Sources, 2018, 399: 314-328. [22] Yin Shen, Xie Xiaochen, Lam J, et al.An improved incre-mental learning approach for KPI prognosis of dynamic fuel cell system[J]. IEEE Transactions on Cybernetics, 2016, 46(12): 3135-3144. [23] Ma Rui, Yang Tao, Elena B, et al.Data-driven proton exchange membrane fuel cell degradation predication through deep learning method[J]. Applied Energy, 2018, 231: 102-115. [24] Deng Zhihua, Chen Qihong, Zhang Liyan, et al.Degradation prediction of PEMFCs using stacked echo state network based on genetic algorithm optimization[J]. IEEE Transactions on Transportation Electrification, 2022, 8(1): 1454-1466. [25] Fu-Kwun W, Cheng Xiaobin, Kai-Chun H, et al.Stacked long short-term memory model for proton exchange membrane fuel cell systems degradation[J]. Power Sourses, 2020, 488(22): 7591-7598. [26] Xie Yucen, Zou Jianxiao, Li Zhongliang, et al.A novel deep belief network and extreme learning machine based performance degradation prediction method for proton exchange membrane fuel cell[J]. IEEE Access, 2020, 17: 6661-6675. [27] He Kai, Mao L, Yu Jianbo, et al.Long-term performance prediction of PEMFC based on LASSO-ESN[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-11. [28] Liu Jiawei, Li Qi.Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks[J]. International Journal of Hydrogen Energy, 2018, 44(11): 5470-5480. [29] Jouin M, Gouriveau R, Hissel D, et al.Prognostics of PEM fuel cell in a particle filtering framework[J]. International Journal of Hydrogen Energy, 2014, 39: 481-494. [30] FCLAB Research.IEEE PHM 2014 Data challenge [Z/OL]. 2014. [31] Wang Fuyu, Cen Jian, Yu Zongwei, et al.Research on a hybrid model for cooling load prediction based on wavelet threshold denoising and deep learning: a study in China[J]. Energy Reports, 2022, 8: 10950-10962. [32] Peng Shanbi, Chen Ruolei, Yu Bin, et al.Daily natural gas load forecasting based on the combination of long short term memory, local mean decomposition, and wavelet threshold denoising algorithm[J]. Journal of Natural Gas Science and Engineering, 2021, 95: 104175. [33] Ma Rui, Xie Renyou, Xu Liangcai, et al.A hybrid prognostic method for PEMFC with aging parameter prediction[J]. IEEE Transactions on Transportation Electrification, 2021, 7(4): 2318-2331.