摘要
针对质子交换膜燃料电池(PEMFC)寿命预测方法中PEMFC特征对其寿命的影响程度未知和模型预测精度低的问题,提出一种基于XGBoost-RFECV算法和长短期记忆(LSTM)神经网络的PEMFC剩余寿命预测方法。首先通过等间隔采样和SG卷积平滑法对PEMFC原始数据进行重构和平滑处理,有效提取PEMFC退化趋势。然后利用XGBoost-RFECV算法计算PEMFC不同特征的重要度,并选择平均交叉验证均方误差最小的10个PEMFC特征组成最优特征子集。最后将最优特征子集输入构建的双层LSTM神经网络实现PEMFC的剩余寿命预测。实验结果表明,该方法的平均绝对误差和均方根误差分别为0.0019和0.0025,决定系数R^(2)为0.974,与XGBoost-RNN、XGBoost-LSTM和XGBoost-RFECV-RNN方法相比预测精度更高,能够有效地预测PEMFC剩余寿命。
Aiming at the problem that the influence of PEMFC characteristics on the life prediction method of the proton exchange membrane fuel cell(PEMFC)is unknown and the low prediction accuracy of the model,a PEMFC remaining life prediction method based on XGBoost-RFECV algorithm and LSTM neural network is proposed.First of all,the PEMFC original data is reconstructed and smoothed by equal interval sampling and SG convolution smoothing method,which effectively retains the original data degradation trend.Then the XGBoost-RFECV algorithm is used to calculate the importance of different PEMFC features,and the 10 PEMFC features with the smallest mean square error of average cross-validation are selected to form the optimal feature subset.Finally,the optimal feature subset is input into the constructed two-layer LSTM neural network to realize the remaining life prediction of PEMFC.The experimental results show that the average absolute error and root mean square error of the method are 0.0019 and 0.0025,respectively,and the coefficient of determination R^(2)is 0.974.Compared with the XGBoost-RNN,XGBoost-LSTM and XGBoost-RFECV-RNN model,the prediction accuracy is higher and it can effectively predict the remaining life of PEMFC.
作者
常家康
吕宁
詹跃东
Chang Jiakang;Lyu Ning;Zhan Yuedong(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;Computer Center,Kunming University of Science and Technology,Kunming 650500,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2022年第1期126-133,共8页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(51667012)项目资助
