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您的检索式：作者名="Bingfeng Zu"
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| 1 | Degradation prediction of proton exchange membrane fuel cell stack using semi-empirical and data-driven methods显示文摘Degradation prediction of proton exchange membrane fuel cell(PEMFC)stack is of great significance for improving the rest useful life.In this study,a PEMFC system including a stack of 300 cells and subsystems has been tested under semi-steady operations for about 931 h.Then,two different models are respectively established based on semi-empirical method and data-driven method to investigate the degradation of stack performance.It is found that the root mean square error(RMSE)of the semi-empirical model in predicting the stack voltage is around 1.0 V,while the predicted voltage has no local dynamic characteristics,which can only reflect the overall degradation trend of stack performance.The RMSE of short-term voltage degradation predicted by the DDM can be less than 1.0 V,and the predicted voltage has accurate local variation characteristics.However,for the long-term prediction,the error will accumulate with the iterations and the deviation of the predicted voltage begins to fluctuate gradually,and the RMSE for the long-term predictions can increase to 1.63 V.Based on the above characteristics of the two models,a hybrid prediction model is further developed.The prediction results of the semi-empirical model are used to modify the input of the data-driven model,which can effectively improve the oscillation of prediction results of the data-driven model during the long-term degradation.It is found that the hybrid model has good error distribution(RSEM=0.8144 V,R2=0.8258)and local performance dynamic characteristics which can be used to predict the process of long-term stack performance degradation. | Yupeng Wang Kangcheng Wu Honghui Zhao Jincheng Li Xia Sheng Yan Yin Qing Du Bingfeng Zu Linghai Han Kui Jiao | 2023 | Energy and AI2023，11，1： | 0 |
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