基于动态特征参数和改进GOA-BP神经网络的车用PEMFC退化趋势预测

doi:10.19562/j.chinasae.qcgc.2025.11.009

Abstract

Abstract:

For the problems of insufficient characterization of dynamic operating conditions and the tendency of traditional optimization algorithms to fall into local optima in the prediction of remaining useful life （RUL） for proton exchange membrane fuel cell （PEMFC）， in this paper a novel prediction method is proposed that combines dynamic feature parameters with an improved grasshopper optimization algorithm （IGOA） and back propagation neural network. Firstly， the seasonal component of voltage data is extracted through seasonal-trend decomposition method， while the power fluctuation rate during operating cycles is quantified. Then， key feature parameters are selected using grey relational analysis. Subsequently， the IGOA is employed to optimize the hyper parameters of the back propagation neural network and construct the IGOA-BP neural network prediction model. Finally， the model performance is validated by real-world vehicle data and laboratory test datasets. The results demonstrate that the proposed method achieves higher prediction accuracy with the mean absolute percentage error below 0.06%， which enables more accurate RUL prediction for PEMFC.

Key words: fuel cells, life prediction, dynamic feature parameters, improved grasshopper optimization algorithm, BP neural network

Fajun Xue,Jibin Yang,Pengyi Deng,Xiaohua Wu,Li Chen,Wenlong Wang,Huaixiang Hu. Degradation Trend Prediction of Vehicular PEMFC Based on Dynamic Feature Parameters and Improved GOA-BP Neural Network[J].Automotive Engineering, 2025, 47(11): 2150-2158.

Figures/Tables 11

References 15

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评价项	关联度	排名
功率波动率	0.706 8	1
季节性分量	0.696 5	2
电堆温度	0.676 3	3
输出电流	0.605 8	4
输出功率	0.572 9	5

方法	RMSE	MAPE/%	R²
LSTM	0.342 9	0.092 1	0.995 7
BP	0.199 5	0.063 3	0.998 6
PSO-BP	0.156 8	0.057 5	0.999 1
GA-BP	0.153 9	0.052 1	0.999 2
GOA-BP	0.085 6	0.042 2	0.999 7
IGOA-BP	0.026 7	0.012 8	0.999 9
IGOA-BP*	0.018 5	0.008 9	0.999 9

方法	RMSE	MAPE/%	R²
IGOA-BP	0.003 0	0.080 9	0.987 0
IGOA-BP*	0.001 6	0.026 2	0.996 4

方法	RMSE	MAPE/%	R²
IGOA-BP	0.002 7	0.055 8	0.987 9
IGOA-BP*	0.002 6	0.052 7	0.990 0

Degradation Trend Prediction of Vehicular PEMFC Based on Dynamic Feature Parameters and Improved GOA-BP Neural Network

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