Aging prognosis model of proton exchange membrane fuel cell in different operating conditions

被引：54
作者：
Chen, Kui [1 ,2 ]
Laghrouche, Salah [1 ,2 ]
Djerdir, Abdesslem [1 ,2 ]
机构：
[1] CNRS, UMR 6174, FEMTO ST, F-90000 Paris, France
[2] Univ Bourgogne Franche Comte, CNRS, FR 3539, FCLAB,Belfort UTBM, F-90000 Paris, France
来源：
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY | 2020年 / 45卷 / 20期
关键词：
Proton exchange membrane fuel cell; Aging prognosis model; Mind evolutionary algorithm; Particle swarm optimization; Genetic algorithm; Backpropagation neural network; ARTIFICIAL NEURAL-NETWORK; LIFE-PREDICTION; KALMAN FILTER; PERFORMANCE; PEMFC; DEGRADATION; DURABILITY; PARAMETERS; IDENTIFICATION; MANAGEMENT;
D O I：
10.1016/j.ijhydene.2020.02.085
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
The aging prognosis model of Proton Exchange Membrane Fuel Cell (PEMFC) can predict the aging state of PEMFC to develop an effective prognostic maintenance plan. This paper proposes an aging prognosis model of PEMFC in different operating conditions based on the Backpropagation (BP) neural network and evolutionary algorithm. The influence of PEMFC current, hydrogen pressure, temperature, and relative humidity on the aging of PEMFC can be considered by the proposed method. Firstly, the aging prognosis model of PEMFC is built by the BP neural network. Then, the evolutionary algorithm including Mind Evolutionary Algorithm (MEA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) is used to optimize the parameters of the established aging prognosis model of PEMFC. Finally, the accuracy of the proposed aging prognosis model is validated by 3 PEMFC aging experiments in different operating conditions. The results show that MEA, GA, and PSO can greatly improve the accuracy of the aging prognosis model of PEMFC. The MEA improves the accuracy by 10 times, while the computing time increases by 0.085s. The proposed MEA-BP that has a very short computing time can be applied to online applications. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：11761 / 11772
页数：12
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