[1]靳方圆,周海峰,熊超.基于ABC-SVM固体氧化物燃料电池电堆建模与仿真[J].集美大学学报(自然版),2020,25(4):293-298.
 JIN Fangyuan,ZHOU Haifeng,XIONG Chao.Modeling and Simulation of Solid Oxide Fuel Cell Stack Based on ABC-SVM[J].Journal of Jimei University,2020,25(4):293-298.
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基于ABC-SVM固体氧化物燃料电池电堆建模与仿真()
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《集美大学学报(自然版)》[ISSN:1007-7405/CN:35-1186/N]

卷:
第25卷
期数:
2020年第4期
页码:
293-298
栏目:
船舶与机械工程
出版日期:
2020-07-28

文章信息/Info

Title:
Modeling and Simulation of Solid Oxide Fuel Cell Stack Based on ABC-SVM
作者:
靳方圆12周海峰12熊超12
(1.集美大学轮机工程学院,福建 厦门 361021 ;2.福建省船舶与海洋工程重点实验室,福建 厦门 361021 )
Author(s):
JIN Fangyuan12ZHOU Haifeng12 XIONG Chao12
(1.School Marine Engineering,Jimei University,Xiamen 361021,China;2.Key Laboratory of Naval Architecture and Ocean Marine Engineering of Fujian Province,Xiamen 361021,China)
关键词:
固体氧化物燃料电池(SOFCs)人工蜂群算法(ABC)支持向量机(SVM)电堆建模
Keywords:
solid oxide fuel cells (SOFCs)artificial bee colony algorithm (ABC)support vector machine (SVM)modeling
摘要:
为了更好地满足工程上对SOFC(solid oxide fuel cell)性能预测和控制方案设计要求,提出利用人工蜂群算法(ABC)优化支持向量机(SVM)来建立SOFC电堆模型。通过利用ABC算法优化SVM参数(核函数值宽度和惩罚因子),采用优化后的参数作为SVM的初始参数建立模型,与SVM、GA-SVM和PSO-SVM模型进行对比。实验结果表明:ABC-SVM模型平均平方误差小,说明该算法可以很好的预测在不同氢气流速下SOFC的电压/电流特性曲线。该模型对SOFC预测和控制方案设计有一定价值。
Abstract:
In order to better meet the engineering requirements for SOFC performance prediction and control scheme design,an artificial bee colony algorithm (ABC) optimization support vector machine (SVM) is proposed to establish the SOFC stack model.By using the ABC algorithm to optimize the SVM parameters (the kernel function with and the penalty coefficient),the optimized parameters are used as the initial parameters of the SVM.The model is compared with the SVM,GA-SVM and PSO-SVM models.The experimental results show that the ABC-SVM model’s average squared error is small,which indicates that the algorithm can predict the voltage/current characteristic curve of SOFC under different hydrogen flow rates.The model has certain value for SOFC prediction and control scheme design.
更新日期/Last Update: 2020-09-16