华南理工大学学报（自然科学版）2025，Vol.53Issue(7)：21-30,10.DOI:10.12141/j.issn.1000-565X.240320
基于深度学习的质子交换膜燃料电池故障预测方法
Proton Exchange Membrane Fuel Cell Fault Prediction Method Based on Deep Learning
摘要
Abstract
Proton exchange membrane fuel cells(PEMFCs)have attracted significant attention in the fields of transportation,marine engineering,and aerospace due to their advantages of pollution-free operation,high effi-ciency,and low noise.However,reliability issues hinder their large-scale commercialization.To further enhance fuel cell reliability,this paper proposed a fault prediction method based on deep learning.First,for operational monitoring data including voltage,current,humidity,and temperature,feature parameters for fault diagnosis were selected based on fuel cell failure mechanisms.This approach reduces data dimensionality,suppresses redundant information,and improves the computational efficiency of the prediction model.Additionally,pre-processing tech-niques such as normalization and sliding time windows were employed to eliminate the effects of differing dimen-sions among monitoring parameters.Then,a fuel cell state prediction model based on the long short-term memory(LSTM)network was constructed.Its inputs were preprocessed multidimensional feature sequences,and its output predicts the fuel cell state for the next T time steps.Finally,the predicted state data was fed into a convolutional neural network(CNN)-based fault identification model to achieve fuel cell fault state prediction.The proposed method was validated using experimental fault data from fuel cell tests,and the results show that the model can pre-dict failures in advance.By virtue of effective data preprocessing,future state prediction via LSTM,and fault recog-nition through CNN,this deep learning-based approach enables early prediction of operational anomalies in proton exchange membrane fuel cells.关键词
燃料电池/深度学习/故障预测/长短时记忆网络/卷积神经网络Key words
fuel cell/deep learning/fault prediction/long short-term memory network/convolutional neural network分类
能源科技引用本文复制引用
左彬,董天航,张泽辉,王华珺,霍为炜,宫文峰,程军圣..基于深度学习的质子交换膜燃料电池故障预测方法[J].华南理工大学学报（自然科学版）,2025,53(7):21-30,10.基金项目
国家自然科学基金项目(52401376) （52401376）
国家重点研发计划项目(2022YFE0210700) （2022YFE0210700）
浙江省"尖兵""领雁"研发攻关计划项目(2024C03254) （2024C03254）
浙江省自然科学基金项目(LTGG24F030004) （LTGG24F030004）
国家水运安全工程技术研究中心开放基金项目(A202403) （A202403）
宁东能源化工基地本级重点支持领域科技计划项目(2023NDKJXMLX059)Supported by the National Natural Science Foundation of China(52401376),the National Key R&D Pro-gram of China(2022YFE0210700),the"Pioneer"and"Leading Goose"R&D Program of Zhejiang Province(2024C03254)and the Natural Science Foundation of Zhejiang Province(LTGG24F030004) （2023NDKJXMLX059）
