中国电机工程学报2025，Vol.45Issue(10)：3892-3901,中插17,11.DOI:10.13334/j.0258-8013.pcsee.232064
基于混合概率数据驱动模型的燃料电池性能衰减预测方法
Fuel Cell Performance Degradation Prediction Method Based on Hybrid Probabilistic Data-driven Model
摘要
Abstract
Accurately predicting the degradation characteristics of fuel cells can provide a solid basis for control and diagnosis decisions.However,mainstream data-driven methods often do not take into account uncertainty factors such as measurement errors caused by experimental conditions and the model's dependence on the data during the modeling phase.Therefore,only a single point estimate can be provided,resulting in a lack of sufficient credibility in the performance degradation results.This paper proposes a mixed-probability data-driven model(MPDD)that combines the characteristics of multiple data-driven models using Bayesian theory,which could quantify the uncertainty of the model's dependence on the data and provide prediction results for fuel cell performance degradation trends in both point estimates and interval estimates.Based on the full operating condition data in fuel cell dynamic load cycle(FC-DLC),the point estimate results of the MPDD model outperform those of a single data-driven model.Furthermore,based on the steady-state data in FC-DLC,the MPDD model achieves up to a 33％improvement in the concentration rate of interval estimates compared to Gaussian process regression(GPR).The prediction results indicate that this forecasting method possesses excellent uncertainty quantification capabilities and could provide more practical decision recommendations for the operation of electro-hydrogen coupling devices.关键词
质子交换膜燃料电池/性能衰减预测/数据驱动模型/不确定性量化/高斯过程回归Key words
proton-exchange membrane fuel cell(PEMFC)/performance degradation prediction/data-driven model/uncertainty quantification/Gaussian process regression分类
动力与电气工程引用本文复制引用
郭冰新,谢长君,朱文超,杨扬,杜帮华..基于混合概率数据驱动模型的燃料电池性能衰减预测方法[J].中国电机工程学报,2025,45(10):3892-3901,中插17,11.基金项目
国家重点研发计划项目(2020YFB1506802) （2020YFB1506802）
广东省重点领域研发计划项目(2020B0909040004).National Key R&D Program of China(2020YFB1506802) （2020B0909040004）
Key R&D Project of Guangdong Province(2020B0909040004). （2020B0909040004）
