重庆理工大学学报2024，Vol.38Issue(17)：106-112,7.DOI:10.3969/j.issn.1674-8425(z).2024.09.013
融合注意力机制的CNN-GRU燃料电池老化趋势预测
Aging trend prediction of fuel cells based on CNN-GRU with attention mechanism
周雅夫 1李瑞洁 1侯代峥1
作者信息
- 1. 大连理工大学 机械工程学院,辽宁 大连 116081
- 折叠
摘要
Abstract
Accurate prediction of aging trend of fuel cells not only provides a reliable foundation for Prognostics Health Management and estimation of remaining useful life,but also plays an important role in enhancing safety.This paper proposes a CNN-GRU prediction model with attention mechanism.According to aging mechanism and Pearson correlation coefficient,an aging index comprising voltage,maximum voltage deviation rate and current is built as input.Then,the weight of CNN convolutional features is evaluated based on the attention mechanism to highlight important features and weaken minor features.Meanwhile,the impact of three attention mechanism modules (the squeeze and excitation module,efficient channel attention module and convolutional block attention module) on prediction performance is investigated.Our experimental results show the aging index proposed effectively enhances prediction accuracy.Compared with the baseline GRU model,the incorporation of the attention mechanism leads to a marked reduction in MAE and RMSE by at least 30.01％ and 29.39％.Notably,the CBAM-Block module performs the best with MAE and RMSE down by 72.72％ and 63.14％.关键词
燃料电池/老化趋势预测/注意力机制/老化指标/门口循环单元Key words
fuel cells/aging trend prediction/attention mechanism/aging index/Gated Recurrent Unit分类
信息技术与安全科学引用本文复制引用
周雅夫,李瑞洁,侯代峥..融合注意力机制的CNN-GRU燃料电池老化趋势预测[J].重庆理工大学学报,2024,38(17):106-112,7.
