基于ISAO-CNN-GRU的质子交换膜燃料电池寿命预测

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中图分类号：TM911.4 文献标志码：A DOI：10.20104/j.cnki.1674-6546.20240429

LifePrediction of Proton Exchange Membrane Fuel Cell Based onISAO-CNN-GRU

Xiong Jianyu1，Kuang Yazhoul，Peng Yiqiangl，2

（1.ScholofAutomobileand Transportation，Xihua University，Chengdu 610o39;2.Vehicle MeasurementControlandSafety KeyLaboratoryofSichuanProvince，Chengdu61Oo39;3.ProvincialEngineering Research CenterforNewEnergy Vehicle Intelligent Control and Simulation Test Technology of Sichuan， Chengdu 610039）

【Abstract]To predict the Remaining Useful Life（RUL）of Proton Exchange Membrane Fuel Cell （PEMFC）precisely，the paper proposes a method for predicting the RUL based onneural network optimized by Improved Snow Ablation Optimizer （ISAO）.Firstlytheoriginaldataarepreprocessedbyusing Pautacriterionandwavelets，thenthePearson’scorrelation coeficients areused toselect parameters which have strong corelation with voltageas input variables.ISAOisused to optimize hyperparametersof Convolutional Neural Network-GatedRecurent Unit（CNN-GRU） model.Thenthe CNN-GRU model isusedtopredicttheoutputvoltageof the PEMFC.Testresults show that whenthetraining setratio is 30%，the mean absoluteerroris 1.6mV ，theroot mean square erroris 2.2mV ，therelativeerroris 0.41% ，and theR-squared of themethod is 99.20%，whicharethe bestresults theof six models.Compared with the Sparow Search Algorithm （SSA），Snow Ablation Optimizer （SAO）and Whale Optimization Algorithm（WOA），the ISAO hasfasteroptimization speed and beterresult，proving that the prediction model and the improved algorithm are effective.

Keywords：Proton Exchange Membrane Fuel Cell（PEMFC），Remaining Useful Life （RUL）. SnowAblation Optimizer （SAO），Gauss-Cauchy mutation

【引用格式】熊健宇，匡亚洲，彭忆强.基于ISAO-CNN-GRU的质子交换膜燃料电池寿命预测[J].汽车工程师，2025（7）： 36-43. XIONGJY，KUANGYZ，PENGYQ.LifePredictionof Proton Exchange MembraneFuel CellBasedon ISAOCNN-GRU[J]. Automotive Engineer， 2025（7）： 36-43.

*基金项目：四川省科技厅重大科技项目（2019ZDZX0002）;四川省区域创新合作项目（2020YFQ0037）;四川省重点研发计划项目（2021YFG0071）。（剩余11972字）