论文:2023,Vol:41,Issue(3):464-470
引用本文:
张钰凡, 李玉忍, 马睿, 张宏宇, 梁波. 基于ARIMA-LSTM深度学习混合模型的PEMFC老化预测方法[J]. 西北工业大学学报
ZHANG Yufan, LI Yuren, MA Rui, ZHANG Hongyu, LIANG Bo. Degradation prediction method of PEMFC based on deep learning hybrid model integrating ARIMA and LSTM[J]. Journal of Northwestern Polytechnical University

基于ARIMA-LSTM深度学习混合模型的PEMFC老化预测方法
张钰凡, 李玉忍, 马睿, 张宏宇, 梁波
西北工业大学 自动化学院, 陕西 西安 710072
摘要:
燃料电池涉及电学、机械、电化学、热力学等诸多学科,其性能衰减过程复杂,涉及多物理、多尺度、多部件、多因素,单一模型在燃料电池老化预测中难以同时对其各类特征进行捕获。为在确保预测精度的同时更好地对数据进行线性和非线性拟合,提出一种差分移动平均自回归结合长短期记忆神经网络的预测模型。通过ARIMA (autoregressive integrated moving average model)与LSTM (long short-term memory)对电压衰退数据线性及非线性部分进行预测后,将预测结果与残差作为特征用于LSTM预测工作。将混合模型与单一ARIMA模型、NAR模型、支持向量回归学习对比发现,混合模型在预测精确度和预测性能方面均有较好表现。
关键词:    燃料电池    老化预测    长短期记忆神经网络    差分移动平均自回归   
Degradation prediction method of PEMFC based on deep learning hybrid model integrating ARIMA and LSTM
ZHANG Yufan, LI Yuren, MA Rui, ZHANG Hongyu, LIANG Bo
School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
Abstract:
Fuel cell involves many disciplines such as electricity, mechanics, electrochemistry, and thermodynamics, and its performance degradation process is complex, involving multi-physics, multi-scale, multi-parts, and multi-factors. Thus, it is difficult for a single model to capture all kinds of characteristics of fuel cell simultaneously in degradation prediction. To ensure the prediction accuracy while better fitting the data linearly and nonlinearly, a prediction model of ARIMA combined with LSTM neural network is proposed in this study. The prediction results with residuals are used as features for LSTM prediction work after first predicting the voltage decay data by ARIMA and LSTM. Comparing the hybrid model with the single ARIMA model and the NAR model with support vector regression learning, it is found that the hybrid model performs better in terms of prediction accuracy and prediction performance.
Key words:    fuel cell    degradation prediction    LSTM    ARIMA   
收稿日期: 2022-08-08     修回日期:
DOI: 10.1051/jnwpu/20234130464
通讯作者: 李玉忍(1962—),西北工业大学教授,主要从事飞机配电系统及刹车控制技术等研究。e-mail:liyuren@nwpu.edu.cn     Email:liyuren@nwpu.edu.cn
作者简介: 张钰凡(1996—),西北工业大学博士研究生,主要从事燃料电池健康管理及燃料电池混合系统能量管理研究。
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