基于集成型极限学习机的PEMFC寿命预测研究

doi:10.19595/j.cnki.1000-6753.tces.240123

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Abstract

Data-driven methods can accurately predict the remaining useful lifetime of the proton exchange membrane fuel cell (PEMFC), the improvement of the prediction performance is the current research hotspot in the field of lifetime prediction. Aiming to improve prediction accuracy and robustness in the lifetime prediction field of PEMFC, an ensemble extreme learning machine (ELM) structure is proposed to predict the lifetime of PEMFC in the long term based on the statistical lifetime prediction principle. The ensemble structure contains 50 times repetitive tests, and for each ELM, it is optimized by the partial reinforcement optimizer algorithm, which improves the lifetime prediction accuracy.
Firstly, the aging data were filtered by the moving average filtering method to filter out the noise and spikes to get smooth data. Then an ensemble extreme learning machine (EELM) model is introduced to predict the lifetime of the PEMFC in the long term by independent ELMs in the ensemble structure. The EELM model adopts a multi-input structure and optimizes the input weights and hidden layer bias by Partial Reinforcement Optimizer to improve the model's generalization ability and prediction accuracy. After that, the prediction results are assembled, and the ensemble structure contains 50 independent ELMs, and the 50 times prediction results are statistically analyzed. Assuming the weights of the 50 times predictions are the same, the final prediction value is obtained by averaging the predictions at each time point. In the results of the long-term prediction, the average and 95% confidence interval of the prediction results of the ensemble ELM are given. To verify the validity and feasibility of the proposed method, three sets of aging data sets under steady-state, quasi-dynamic and dynamic conditions are verified. Based on the experiments, it can be obtained that the RMSE of the proposed method is 0.010 44 and the MAPE is 0.221 8% under steady state, 0.023 73 and 0.6296% under quasi-dynamic, and 0.004 861 and 7.716% under dynamic. Comparing the prediction results with the cycle reservoir with jump (CRJ), echo state network (ESN), and group method of data handling (GMDH) in the past literature, the EELM model can obtain more accurate results with less training data. This effectively reflects the fact that EELM has a stronger generalization ability. The robustness of the EELM is also analyzed in these three conditions. For the robustness analysis, the ELM and EELM are subjected to 50 repetitions of experiments, and the RMSE is used as the evaluation index. The experimental results show that the prediction accuracy of the EELM is higher than that of most of the independent ELMs, and the prediction results of EELM are less fluctuating and more centrally distributed. This indicates that the EELM prediction is more stable, and the ensemble structure effectively improves the robustness of the ELM.
The validation results show that: (1) the randomness of input weights and hidden layer bias will directly affect the results of lifetime prediction, which is statistically analyzed by the EELM structure with 95% confidence intervals, and the distribution of the prediction results can be observed. The prediction accuracy can be improved by averaging the prediction results. (2) The robustness is improved. Statistical analysis of the prediction results through the ensemble structure can improve the stability and accuracy of the prediction, which is more obvious in the quasi-dynamic and dynamic conditions. Getting the prediction results through ensemble averaging can improve the prediction accuracy and enhance the system's robustness. (3) Combining ELM with other prediction methods to find methods that can effectively implement online prediction and improve prediction accuracy will be the focus of research in future work.

Key words： Proton exchange membrane fuel cell (PEMFC) extreme learning machine (ELM) ensemble structure partial reinforcement optimizer

Received: 18 January 2024

PACS:

TM911.42

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	Yang Qi
	Chen Jingwen
	Hua Zhiguang
	Li Xianglong
	Zhao Dongdong
	Lan Tianyi
	Dou Manfeng

Cite this article:

Yang Qi,Chen Jingwen,Hua Zhiguang等. Research on PEMFC Lifetime Prediction Based on Ensemble Extreme Learning Machine[J]. Transactions of China Electrotechnical Society, 0, (): 2492905-2492905.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.240123 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/2492905

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