基于注意力神经网络的燃料电池寿命衰减预测  

Lifetime Decay Prediction of Fuel Cell Based on Attention Neural Network

作　　者：高明[1,2] 刘超[1,3] 唐加福 孙思晶[4] 邹广宇 GAO Ming;LIU Chao;TANG Jia-fu;SUN Si-jing;ZOU Guang-yu(School of Management Science and Engineering,Dongbei University of Finance and Economics,Liaoning Provincial Key Laboratory of Big Data Management and Optimization Decision,Dalian 116025,China;Center for Post-doctoral Studies of Computer Science,Northeastern University,Shenyang 110819,China;Linde Hydrogen FuelTech Dalian Co.,Ltd,Dalian 116100,China;Liaoning Dalien Law Firm,Dalian 116001,China;Department of Electrical Engineering,Dalian University of Technology,Dalian 116024,China)

机构地区：[1]东北财经大学管理科学与工程学院,辽宁省大数据管理与优化决策重点实验室,辽宁大连116025 [2]东北大学计算机科学与技术博士后流动站,辽宁沈阳110819 [3]林德加氢站设备(大连)有限公司,辽宁大连116100 [4]辽宁达练律师事务所,辽宁大连116001 [5]大连理工大学电子信息与电器工程学部,辽宁大连116024

出　　处：《中国管理科学》2023年第3期155-166,共12页Chinese Journal of Management Science

基　　金：国家自然科学基金资助项目(71831003,71772033,71771035);东北财经大学科研平台研究能力提升专项(PT-Y202214);辽宁省自然科学基金资助项目(2020-KF-11-11);大连市科技人才创新支持计划(2022RG17)。

摘　　要：发展氢燃料电池是实现低碳经济的重要途径,然而主流的质子交换膜燃料电池(PEMFC)的安全性、高成本和耐久性制约了其商业化发展,PEMFC的有效寿命预测可望提高其可靠性、可维护性及降低总使用成本,已成为燃料电池行业和学界共同关注的重要问题。PEMFC因其复杂的物理化学过程、环境条件和工况,以及数据存在噪音、高度非线性关系,为寿命预测带来巨大挑战。现有模型驱动的预测方法过于复杂而不实用,而数据驱动的统计分析方法、传统机器学习方法,受制于信息丢失风险和有限拟合能力,预测精度还有待提升。深度学习因其强大的非线性拟合能力和灵活的建模方式,成为该领域主流方法。然而常用的循环神经网络在长序列上的全局学习能力不足,而Transformer模型受制于有限样本量而易于过拟合。因此,本文结合长短期记忆神经网络(LSTM)、1维卷积神经网络(1D-CNN)等方法的特点和局限性,提出了一种新型的复合深度神经网络AACNN-LSTM(attention after CNN-LSTM)。该方法借助1D-CNN进行平滑和滤波,通过LSTM层进行多维向量间的时序关系学习,最后引入注意力机制(Attention)模块,从全局角度对不同时间步的多维向量进行自适应加权。模型最后以PEMFC的输出电压为预测结果,用于寿命评估。本文在某真实PEMFC的寿命测试数据集上,进行了不同寿命阶段数据划分、多种架构组合的消融实验、与不同类型算法比较等对比实验。结果表明,相比其他方法,精度得到了显著提升,并保持了较好的运算效率。同时,在IEEE PHM 2014燃料电池寿命预测挑战赛数据集上也验证了模型的普适性和优越性。此外,还对PEMFC寿命的多步时间序列预测进行了探索,在适中的预测步长(10小时)内能够取得可接受的精度,具备一定的工程实用价值。本文提出的CNN-LSTM组合,验证了CNN归纳偏差学习可与LSTM序列学习�As a green energy source,fuel cells are an important way to achieve a low-carbon economy.However,the safety,high cost and durability of the mainstream PEMFC(Proton Exchange Membrane Fuel Cell)have restricted their commercialization,and effective lifetime prediction can improve reliability,maintainability and reduce the total cost of use,so the lifetime decay prediction of PEMFC has become an important issue of common concern for the fuel cell industry and academia.The effective lifetime prediction of PEMFC faces great challenges due to the complexity of their physicochemical processes,operating states,environmental conditions,and operating conditions.Model-driven prediction methods are constrained by the difficulty of accurately modeling the complex reaction mechanism inside the battery,as well as the subjectivity and one-sidedness of empirical rules,which make the simulation calculation large and the accuracy difficult to improve.The data-driven prediction methods,such as statistical analysis methods,require statistical modelling or stochastic processes related analysis involving parameter estimation and other aspects,which give more ideal assumptions and are prone to the risk of information loss.At the same time,traditional machine learning methods have different model fitting abilities and large model selection workload.In particular,it is difficult to take into account the complex nonlinear relationships between features and time steps in the multidimensional time series prediction,as well as the noise and bias existing in the original data at the same time,and the prediction accuracy is limited.In recent years,deep learning has become an effective method to solve highly nonlinear multidimensional time series prediction due to the powerful nonlinear fitting ability and flexible modeling by artificial neural networks.In contrast,RNN(Recurrent Neural Network),which are commonly used in existing research,mainly focus on short series learning,with insufficient global modeling and learning ability on long series t

关 键 词：寿命预测 注意力机制 时间序列预测 长短期记忆神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]