重庆理工大学学报2026，Vol.40Issue(15)：87-95,9.DOI:10.3969/j.issn.1674-8425(z).2026.08.010
联合G-WD与优化TCN-Transformer混合特征输入的PEMFC老化趋势预测
Aging trend prediction of PEMFC by integrating G-WD and optimizing TCN-Transformer hybrid feature inputs
摘要
Abstract
The aging trend prediction of PEMFCs(proton exchange membrane fuel cells)isimportant for predicting the lifespan of PEMFCs.To improve the prediction accuracy in the voltage aging trend of PEMFC stacks,this paper proposes a hybrid feature-input aging trend prediction model that combines G-WD and optimized TCN-Transformer.The original stack voltage data were processed through data down-sampling and smoothing.Correlation analysis was performed to identify strongly correlated physical parameters as input features.After decomposition and smoothing processing with G-WD,the voltage data is used as strong feature input,and time-domain feature extraction is performed as auxiliary feature input.In the TCN-Transformer model,an attention mechanism is inserted into the TCN block,constructing the TCN-SE-Transformer model.The input features are fed into the model for training,and the DE(differential evolution)algorithm is used to optimize different hyperparameters of the TCN-Transformer.The DE-TCN-SE-Transformer model's prediction results are obtained when the prediction error is minimized.Compared with other models and conditions,this model achieves higher prediction accuracy in the aging prediction of PEMFC stack voltage.关键词
质子交换膜燃料电池/老化趋势预测/预测精度/混合特征输入/优化小波分解/优化算法Key words
PEMFCs/aging trend prediction/prediction accuracy/mixed feature input/optimization of wavelet decomposition/optimization algorithm分类
信息技术与安全科学引用本文复制引用
冯仁华,赵宇..联合G-WD与优化TCN-Transformer混合特征输入的PEMFC老化趋势预测[J].重庆理工大学学报,2026,40(15):87-95,9.基金项目
重庆市自然科学基金创新发展联合基金项目(CSTB2023NSCQ-LZX0065) （CSTB2023NSCQ-LZX0065）
