控制理论与应用2025，Vol.42Issue(8)：1578-1586,9.DOI:10.7641/CTA.2025.40411
基于ENSACO-LSTM的PEMFCs退化预测
PEMFCs degradation prediction based on ENSACO-LSTM
摘要
Abstract
In this paper,a fusion model based on a long short-term memory(LSTM)neural network and enhanced search ant colony optimization(ENSACO)is proposed to predict the power degradation trend of proton exchange membrane fuel cells(PEMFC).Firstly,the Shapley additive explanations(SHAP)value method is used to select external characteristic parameters with high contributions as inputs for the data-driven approach.Next,a novel swarm optimization algorithm,the enhanced search ant colony optimization,is proposed.This algorithm improves the ant colony optimization(ACO)algo-rithm based on a reinforcement factor to avoid premature convergence and accelerate the convergence speed.Comparative experiments are set up to compare the performance differences between particle swarm optimization(PSO),ACO,and ENSACO.Finally,a data-driven method based on ENSACO-LSTM is proposed to predict the power degradation trend of PEMFCs.And actual aging data is used to validate the method.The results show that,within a limited number of iterations,the optimization capability of ENSACO is significantly stronger than that of PSO and ACO.Additionally,the prediction accuracy of the ENSACO-LSTM method is greatly improved,with an average increase of approximately 50.58％compared to LSTM,PSO-LSTM,and ACO-LSTM.关键词
质子交换膜燃料电池/群体优化算法/性能老化预测/强化搜索蚁群优化算法/数据驱动方法/深度学习Key words
proton exchange membrane fuel cells/swarm optimization algorithm/performance aging prediction/en-hanced search ant colony algorithm/data-driven approach/deep learning引用本文复制引用
贾志桓,陈林,邵奥利,王宇鹏,高金武..基于ENSACO-LSTM的PEMFCs退化预测[J].控制理论与应用,2025,42(8):1578-1586,9.基金项目
Supported by the Major Science and Technology Project of Jilin Province(20220301010GX)and the International Scientific and Technological Cooperation(20240402071GH). （20220301010GX）
