基于 CNN-LSTM 的恒定负载和启停负载下 PEM 水电解槽的降解预测

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Boshi Xu , Wenbiao Ma , Wenyan Wu , Yang Wang , Yang Yang , Jun Li , Xun Zhu , Qiang Liao
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引用次数: 0

摘要

性能退化是影响质子交换膜电解槽商业化的关键因素。然而,由于降解的时间和空间尺度不同,很难建立一个包含所有降解类别的机理模型。本文采用数据驱动法,基于卷积神经网络-长短期记忆(CNN-LSTM)模型预测电解槽电压随时间的变化。首先,从耐久性测试中收集了两个数据集,包括持续运行 1140 小时和起停负载 660 小时。其次,通过实验数据训练数据驱动模型,并优化模型超参数。最后,预测电解槽在未来几百小时内的降解情况,并将预测精度与其他时间序列算法进行比较。结果表明,该模型可以精确预测两个数据集的降解情况,R2 均高于 0.98。与传统模型相比,该算法显示出与实验数据更好的拟合特性,尤其是随着预测时间的增加。在恒定运行和启停运行的情况下,电解槽在 1000 小时后的降解率分别为 4.5 % 和 2.5 %。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Degradation prediction of PEM water electrolyzer under constant and start-stop loads based on CNN-LSTM

Degradation prediction of PEM water electrolyzer under constant and start-stop loads based on CNN-LSTM

The performance degradation is a crucial factor affecting the commercialization of proton exchange membrane electrolyzer. However, it is difficult to establish a mechanism model incorporating all degradation categories due to their different time and spatial scales. In this paper, the data-driven method is employed to predict the electrolyzer voltage variation over time based on a convolutional neural network-long short term memory (CNN-LSTM) model. First, two datasets including constant operation for 1140 h and start-stop load for 660 h are collected from the durability tests. Second, the data-driven models are trained through the experimental data and the model hyper-parameters are optimized. Finally, the electrolyzer degradation in the next few hundred hours is predicted, and the prediction accuracy is compared with other time-series algorithms. The results show that the model can predict the degradation precisely on both datasets, with the R2 higher than 0.98. Compared to conventional models, the algorithm shows better fitting characteristic to the experimental data, especially as the prediction time increases. For constant and start-stop operations, the electrolyzers degradate by 4.5 % and 2.5 % respectively after 1000 h. The proposed method shows great potential for real-time monitoring in the electrolyzer system.

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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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