基于PCC-ISSA-BP燃料电池剩余寿命预测

Residual life prediction of fuel cell based on PCC-ISSA-BP

在质子交换膜燃料电池(PEMFC)寿命预测中,针对燃料电池中的特征对其寿命的影响程度未知问题,使预测燃料电池的剩余寿命问题变得相对复杂,为了更加准确的预测燃料电池的剩余使用寿命。本文首先通过小波分析对原始堆栈电压进行去噪处理,滤除噪声数据,利用皮尔逊相关系数(PCC)对影响因素进行降维,提取关键影响因素,简化模型结构;然后利用改进的麻雀优化算法(ISSA)优化BP神经网络,找到网络最优的权值和阈值,并建立ISSABP模型;最后将处理好的数据输入ISSA-BP模型,实现PEMFC的剩余寿命预测。实验结果表明,PCC-ISSA-BP的平均绝对误差百分比、平均绝对误差、均方根误差分别为0.125%、0.00397、0.00568,优于其它模型,能够更有效地预测燃料电池的剩余寿命。

In proton exchange membrane fuel cell(PEMFC)life prediction,the unknown degree of influence of the characteristics in the fuel cell on its life makes the problem of predicting the remaining life of the fuel cell relatively complex.In order to more accurately predict the remaining service life of the fuel cell.In this paper,the original stack voltage was de-noised by wavelet analysis to filter the noisy data.pearson correlation coefficient(PCC)was used to reduce the dimension of influencing factors,extract key influencing factors,and simplify the model structure.Then,the improved sparrow search algorithm(ISSA)is used to optimize the BP neural network,find the optimal weights and thresholds of the network,and establish the ISSA-BP model.Finally,the processed data is input into the ISSA-BP model to predict the remaining life of PEMFC.The experimental results show that the average absolute error percentage,average absolute error,and root mean square error of PCC-ISSA-BP are 0.125%,0.00397,and 0.00568,respectively,which are better than other models and can more effectively predict the remaining life of fuel cells.