ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Lifespan prediction of hydrogen fuel cell based on decomposition optimization parallel ESN
Received date: 2024-05-17
Revised date: 2024-06-11
Accepted date: 2024-07-24
Online published: 2024-08-20
Supported by
National Natural Science Foundation of China(52307251);China Postdoctoral Science Foundation(2023TQ0277)
Aiming at the problem of low voltage prediction accuracy caused by multi-time-scale aging characteristics of Proton Exchange Membrane Fuel Cell （PEMFC）， a Parallel Echo State Network （PESN） structure which based on Ensemble Empirical Mode Decomposition （EEMD） and Circulatory System based Optimization （CSBO） is proposed to improve the lifespan prediction accuracy of PEMFC. By utilizing EEMD to conduct modal decomposition on the original voltage signal， the historical data from various time points and the decomposed signals with distinct frequencies are taken as the parallel inputs for the different sub-reservoirs of ESN， thereby establishing a parallel ESN structure that allocates and superimposes outputs based on weights. The CSBO is then leveraged to optimize the relevant parameters of the parallel ESN structure. Subsequently， utilizing the optimized EPESN model， the prediction of the output voltage of PEMFC for the next several hundred hours is achieved. Specifically， under the steady-state and quasi-dynamic data training sets at 70%， the Root Mean Square Error （RMSE） of EPESN is reduced by 34.25% and 47.41% respectively， compared to that of ESN. Furthermore， when the dynamic 1 training duration is set at 300 h， the RMSE of EPESN is decreased by 15.30% compared to ESN. The results explicitly demonstrate that the EPESN structure is capable of enhancing the prediction accuracy for the lifespan of PEMFC.
Zhiguang HUA , Shiyuan PAN , Dongdong ZHAO , Xianglong LI , Manfeng DOU . Lifespan prediction of hydrogen fuel cell based on decomposition optimization parallel ESN[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(2) : 330696 -330696 . DOI: 10.7527/S1000-6893.2024.30696
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