质子交换膜燃料电池基于老化特征的剩余使用寿命混合预测方法

doi:10.19595/j.cnki.1000-6753.tces.241401

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Abstract For the life prediction issue of Proton Exchange Membrane Fuel Cells (PEMFC), a hybrid prediction method based on aging characteristics was proposed. The aging characteristics of PEMFC throughout its lifecycle could be decomposed into overall aging characteristics, voltage fluctuation characteristics, and voltage recovery characteristics. For the overall aging characteristics, the Square Root Unscented Kalman Filter (SRUKF) was utilized for prediction; for the voltage recovery characteristics, a second-order RQ-RLC equivalent circuit model was used to fit the electrochemical impedance spectroscopy data, obtaining an indirect relationship between recovery voltage and time; for the voltage fluctuation characteristics, the Xception-Long Short Term Memory (LSTM) network was employed for training and prediction. Finally, the prediction results of the three aging characteristics were superimposed to calculate the RUL prediction result for PEMFC. Based on the verification of two sets of operating condition data, the results show that compared to independent predictions by SRUKF and Xception-LSTM, the accuracy of the hybrid prediction method is improved by about 20%.
Certainly, here is the translation with the sequence numbers replaced by ordinal terms:
Firstly, the collection and preprocessing of voltage aging data. Preprocess the collected PEMFC data, which includes removing monitoring noise, outlier processing, and timestamp reconstruction; Secondly, voltage aging feature prediction. Utilize the PEMFC empirical model to represent the degradation trend and apply SRUKF to identify the hidden state of the voltage. The output of this module is the prediction of future voltage aging trends under the overall trend; Thirdly, voltage fluctuation feature prediction. This module is designed to analyze the periodic fluctuations of PEMFC voltage during long-term durability operation. The Xception-LSTM model is extensively trained with voltage fluctuation feature datasets for long-term prediction of these characteristics. Fourthly, EIS test data collection. Gather EIS experimental data of the stack at specific time points during long-term durability experiments at regular intervals. Fifthly, recovery voltage feature prediction. Based on EIS data, identify the parameters of the second-order RQ-RLC model and further establish the relationship between resistance parameters and recovery voltage. Lastly, overlay the results of the three feature predictions to obtain the long-term prediction of the aging voltage characteristic values, and calculate the RUL prediction value according to the aging voltage threshold.
This paper proposes a hybrid prediction method based on aging characteristics, which decomposes the aging characteristics into overall aging features, voltage fluctuation features, and voltage recovery features, and carries out long-term predictions for the three types of features, finally overlaying the prediction results. The method was validated using data from two sets of operating conditions, FC1 and FC2, and the results indicate: (1) The short-term prediction results show that SRUKF has a smaller prediction error compared to UKF, and after incorporating the Xception network for feature extraction, the Xception-LSTM model exhibits better prediction performance; (2) Under both operating conditions, the hybrid prediction method has a high RUL prediction accuracy. For FC1, most prediction starting points can achieve an accuracy of over 95%, while for FC2, the accuracy can reach over 90%; (3) A comparison among the SRUKF, Xception-LSTM, and hybrid prediction methods shows that, compared to predicting a single aging feature, the RUL prediction accuracy is higher when combining multiple aging features.

Key words： Proton exchange membrane fuel cell remaining useful life aging characteristics hybrid prediction second-order RQ-RLC equivalent circuit model Xception-long short term memory (LSTM)

Received: 06 August 2024

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TM911.42

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	Xu Bing
	Zhang Min
	Yang Wentao
	Zhang Lingxian

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Xu Bing,Zhang Min,Yang Wentao等. PEMFC Hybrid Prediction Method for Remaining Useful Life Based on Aging Characteristics[J]. Transactions of China Electrotechnical Society, 2025, 40(17): 5703-5714.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.241401 OR https://dgjsxb.ces-transaction.com/EN/Y2025/V40/I17/5703

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