Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (9): 2192-2202.doi: 10.13229/j.cnki.jdxbgxb20220419

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Degradation trend prediction of proton exchange membrane fuel cell based on PSO⁃LSTM

Jin-wu GAO1,2(),Zhi-huan JIA1,2,Xiang-yang WANG1,2,Hao XING3,4   

  1. 1.State Key Laboratory of Automotive Simulation and Control，Jilin University，Changchun 130022，China
    2.College of Communication Engineering，Jilin University，Changchun 130022，China
    3.School of Automation，Hangzhou Dianzi University，Hangzhou 310018，China
    4.Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education，Dalian University of Technology，Dalian 116024，China
  • Received:2022-04-15 Online:2022-09-01 Published:2022-09-13

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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）

CLC Number: 

  • TK91

Fig.1

Structure of LSTM"

Fig.2

Flow chart of PSO"

Fig.3

Schematic diagram of Dropout"

Fig.4

Flow chart of PSO-LSTM"

Table 1

Comparison of parameter settings between LSTM and PSO-LSTM"

参数方法
LSTMPSO-LSTM
隐层神经元/个100100
求解器AdamAdam
迭代次数/次100100
学习率衰减代数/代5050
学习率衰减率0.20.2
学习率0.01自适应优化
Dropout概率0.5自适应优化

Fig.5

Comparison of prediction effect between the LSTM and the PSO-LSTM"

Fig.6

Comparison of prediction error between the LSTM and the PSO-LSTM"

Table 2

RMSE of prediction by LSTM and PSO-LSTM"

数据比例/%方法RMSE
LSTMPSO-LSTM

30

40

0.056 52

0.046 51

0.036 08

0.029 96

600.044 350.013 61
800.045 990.012 17

Table 3

Best learning rate and Dropout probabilityof PSO-LSTM"

数据比例/%学习率Dropout概率

30

40

0.0099

0.0075

0.0024

0.0032

600.00860.0907
800.00950.0407
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