OUTPUT VOLTAGE PREDICTION METHOD OF FUEL CELL STACK BASED ON KMO-PCA-BP

Hu Bing; Wang Xiaojuan; Xu Lijun; Su Xin

doi:10.19912/j.0254-0096.tynxb.2022-0066

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (3) : 12-19. DOI: 10.19912/j.0254-0096.tynxb.2022-0066

OUTPUT VOLTAGE PREDICTION METHOD OF FUEL CELL STACK BASED ON KMO-PCA-BP

Hu Bing¹, Wang Xiaojuan², Xu Lijun³, Su Xin^3,4

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Abstract

The output voltage of the proton exchange membrane fuel cell is an important indicator of the health status and fault diagnosis of fuel cells. It is difficult to accurately predict the effect of the output voltage by parameters such as single cell voltage, operating temperature, gas flow and material flow, etc. For this reason, according to the data set collected by the fuel cell experimental platform, KMO correlation analysis is carried out to verify that the data set is suitable for principal component analysis. We use principal component analysis to reduce the dimensionality of the original data, and determine the 12 principal components that affect the output voltage of the fuel cell, as well as establish a fuel cell output voltage prediction model based on KMO measure （Kaiser Meyer Olkin, KMO）-Principal Component Analysis （PCA）-BP neural network （Back Propagation, BP）. And we compared with the BP prediction model to verify the superiority of the algorithm. We use the data sets with an interval of 1 hour and 0.5 hours for comparison and verification to verify the generalization ability of the algorithm. The results of prediction show that the KMO-PCA-BP prediction model can accurately predict the output voltage of the fuel cell stack, and has high prediction accuracy and speed and strong generalization ability. It provides reference for fuel cell stack output voltage prediction, health management and fault diagnosis.

Key words

fuel cells / proton exchange membrane / output voltage / prediction method / PCA / BP

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Hu Bing, Wang Xiaojuan, Xu Lijun, Su Xin. OUTPUT VOLTAGE PREDICTION METHOD OF FUEL CELL STACK BASED ON KMO-PCA-BP[J]. Acta Energiae Solaris Sinica. 2022, 43(3): 12-19 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0066

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