Residual Life Prediction of Proton Exchange Membrane Fuel Cell Based on Improved ESN

doi:10.11930/j.issn.1004-9649.202402054

Abstract

Abstract:

Aiming at the problem that the current residual effective life prediction (RUL) technique for proton exchange membrane fuel cells (PEMFCs) has poor prediction effect in the medium and long term, a residual life prediction method based on the Improved Gray Wolf Optimization algorithm (IGWO) and Echo State Network (ESN) is proposed, in which the voltage of the electric stack is firstly selected as a health indicator, and the PEMFC dataset is processed by using convolutional smoothing filtering method to carry out data Smoothing and normalization are used to effectively reduce the interference of outliers on the subsequent model training. Then the reserve pool parameters of the ESN are optimized using the local and global optimization search capability of IGWO, and the IGWO-ESN network model is constructed, and the processed dataset is used for the training of the remaining life prediction model of the PEMFC, and finally it is compared with the traditional ESN for verification. The results show that the improved ESN model predicts the root mean square error and average absolute percentage error of 0.0342 and 0.9315%, respectively, and the prediction accuracy is significantly improved compared with the ordinary ESN model, and the prediction accuracy of the medium- and long-term RUL is also higher.

Key words: proton exchange membrane fuel cell, echo state network, gray wolf optimization algorithm, remaining life prediction

YUAN Tiejiang, LI Rongsheng, KANG Jiandong, YAN Huaguang. Residual Life Prediction of Proton Exchange Membrane Fuel Cell Based on Improved ESN[J]. Electric Power, 2025, 58(5): 102-109.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202402054

https://www.electricpower.com.cn/EN/Y2025/V58/I5/102

Figures/Tables 11

Fig.1 Echo state network structure

Fig.2 IGWO-ESN algorithm flow

Table 1 Table 1 PEMFC key parameters

物理意义		参数
U1单电池电压/V		V₁
U2单电池电压/V		V₂
U3单电池电压/V		V₃
U4单电池电压/V		V₄
U5单电池电压/V		V₅
电堆输出电压/V		U
燃料电池电流/A		I
氢气入口温度/℃		T_H2
空气入口湿度/%		H_r

Fig.3 Part of the sensor monitoring parameters

Fig.4 Output smooth voltage of FC1 stack

Table 2 Specific parameters of ESN and improved ESN model

模型	M	N	L	W_in、W	S_D	S_R
ESN	1	500	1	[–0.5, 0.5]	0.05	0.7
改进ESN	1	[100, 1000]	[–0.5, 0.5]	[–0.5, 0.5]	[0.01, 0.1]	[0.5, 1.5]

Fig.5 Relationship between ESN spectral radius and training error

Fig.6 IGWO optimizes ESN iterative error gradient

Fig.7 Comparison of short-term prediction results

Fig.8 Comparison of long-term prediction results

Table 3 ESN and improved ESN model evaluation

模型	时段	R_MSE	M_APE/%	S_core
ESN	1 h	0.0008	0.0212	1165
	10 h	0.0336	0.7731
	500 h	0.7563	21.0353
改进ESN	1 h	0.0006	0.0154	123
	10 h	0.0018	0.0495
	500 h	0.0342	0.9315

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