基于PSO-LSTM的质子交换膜燃料电池退化趋势预测

doi:10.13229/j.cnki.jdxbgxb20220419

摘要/Abstract

摘要：

提出了一种基于粒子群优化（PSO）算法的长短期记忆网络（LSTM）方法，对质子交换膜燃料电池（PEMFC）的电堆电压进行了退化预测。首先，分析了PEMFC的退化机理。然后，应用LSTM建立了电压退化预测模型，并采用Dropout层来防止过拟合以提高模型的泛化能力。此外，使用PSO算法优化LSTM方法中的初始学习率和Dropout概率以提升预测效果。最后，使用IEEE 2014 Data Challenge Data的燃料电池实际老化数据进行验证。结果表明，本文方法可以精确地预测燃料电池的退化，相比于传统的LSTM方法，预测精度提升了50%。

关键词: 自动控制技术, 退化预测, 燃料电池, 深度学习, 长短期记忆网络, 粒子群优化

Abstract:

A long-term and short-term memory neural network （LSTM） based on particle swarm optimization （PSO） was proposed to predict the life of PEMFC. First， the degradation mechanism of PEMFC was analyzed. Then， the voltage degradation prediction model was established by using LSTM neural network and the Dropout layer was used to prevent overfitting to improve the generalization ability of the model. In addition， PSO was used to optimize the learning rate and Dropout rate in LSTM to improve the prediction effect. Finally,the actual aging data of IEEE 2014 Data Challenge Data fuel cell were used to verify. The results show that this method can accurately predict the degradation of fuel cells， and the prediction accuracy is improved by 50% compared with the traditional LSTM.

Key words: automatic control technology, degradation prediction, fuel cell, deep learning, long short-term memory（LSTM） network, particle swarm optimization（PSO）

中图分类号:

TK91

高金武,贾志桓,王向阳,邢浩. 基于PSO-LSTM的质子交换膜燃料电池退化趋势预测[J]. 吉林大学学报(工学版), 2022, 52(9): 2192-2202.

Jin-wu GAO,Zhi-huan JIA,Xiang-yang WANG,Hao XING. Degradation trend prediction of proton exchange membrane fuel cell based on PSO⁃LSTM[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2192-2202.

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数据比例/%	方法RMSE
数据比例/%	LSTM	PSO-LSTM
30 40	0.056 52 0.046 51	0.036 08 0.029 96
60	0.044 35	0.013 61
80	0.045 99	0.012 17