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CLC number: TM911.4

On-line Access: 2014-10-08

Received: 2014-01-06

Revision Accepted: 2014-05-07

Crosschecked: 2014-09-29

Cited: 1

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2014 Vol.15 No.10 P.829-839


Control-oriented dynamic identification modeling of a planar SOFC stack based on genetic algorithm-least squares support vector regression*

Author(s):  Hai-bo Huo1, Yi Ji1, Xin-jian Zhu2, Xing-hong Kuang1, Yu-qing Liu1

Affiliation(s):  1. Department of Electrical Engineering, Shanghai Ocean University, Shanghai 201306, China; more

Corresponding email(s):   hbhuo@shou.edu.cn

Key Words:  Solid oxide fuel cell (SOFC), Control-oriented, Dynamic modeling, Least squares support vector regression (LSSVR)

Hai-bo Huo, Yi Ji, Xin-jian Zhu, Xing-hong Kuang, Yu-qing Liu. Control-oriented dynamic identification modeling of a planar SOFC stack based on genetic algorithm-least squares support vector regression[J]. Journal of Zhejiang University Science A, 2014, 15(10): 829-839.

@article{title="Control-oriented dynamic identification modeling of a planar SOFC stack based on genetic algorithm-least squares support vector regression",
author="Hai-bo Huo, Yi Ji, Xin-jian Zhu, Xing-hong Kuang, Yu-qing Liu",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Control-oriented dynamic identification modeling of a planar SOFC stack based on genetic algorithm-least squares support vector regression
%A Hai-bo Huo
%A Yi Ji
%A Xin-jian Zhu
%A Xing-hong Kuang
%A Yu-qing Liu
%J Journal of Zhejiang University SCIENCE A
%V 15
%N 10
%P 829-839
%@ 1673-565X
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1400011

T1 - Control-oriented dynamic identification modeling of a planar SOFC stack based on genetic algorithm-least squares support vector regression
A1 - Hai-bo Huo
A1 - Yi Ji
A1 - Xin-jian Zhu
A1 - Xing-hong Kuang
A1 - Yu-qing Liu
J0 - Journal of Zhejiang University Science A
VL - 15
IS - 10
SP - 829
EP - 839
%@ 1673-565X
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1400011

For predicting the voltage and temperature dynamics synchronously and designing a controller, a control-oriented dynamic modeling study of the solid oxide fuel cell (SOFC) derived from physical conservation laws is reported, which considers both the electrochemical and thermal aspects of the SOFC. Here, the least squares support vector regression (LSSVR) is employed to model the nonlinear dynamic characteristics of the SOFC. In addition, a genetic algorithm (GA), through comparing a simulated annealing algorithm (SAA) with a 5-fold cross-validation (5FCV) method, is preferably chosen to optimize the LSSVR’s parameters. The validity of the proposed LSSVR with GA (GA-LSSVR) model is verified by comparing the results with those obtained from the physical model. Simulation studies further indicate that the GA-LSSVR model has a higher modeling accuracy than the LSSVR with SAA (SAA-LSSVR) and the LSSVR with 5FCV (5FCV-LSSVR) models in predicting the voltage and temperature transient behaviors of the SOFC. Furthermore, the convergence speed of the GA-LSSVR model is relatively fast. The availability of this GA-LSSVR identification model can aid in evaluating the dynamic performance of the SOFC under different conditions and can be used for designing valid multivariable control schemes.


研究方法: 1. 分析SOFC的电化学和能量平衡子模型。2. 利用所选择的最优LSSVR参数,建立了SOFC的GA-LSSVR动态辨识模型。通过仿真分析和比较,验证了所建模型的有效性 (图3和4)。3. 利用所建模型的预测结果,与模拟退火算法优化最小二乘支持向量回归机(SAA-LSSVR)和5折交叉验证最小二乘支持向量回归机(5FCV-LSSVR)模型的预测结果进行了比较,表明所建立的GA-LSSVR模型具有较高的预测精度(表3和4)。

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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