Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm

被引：146

作者：

Sun, Zhe ^{[1
]}

Wang, Ning ^{[1
]}

Bi, Yunrui ^{[2
]}

Srinivasan, Dipti ^{[3
]}

机构：

[1] Zhejiang Univ, Inst Cyber Syst & Control, Natl Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China

[2] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China

[3] Natl Univ Singapore, Dept Elect Comp Engn, Singapore 117576, Singapore

来源：

ENERGY | 2015年 / 90卷

基金：

中国国家自然科学基金;

关键词：

Proton exchange membrane fuel cell (PEMFC); Parameter identification; Hybrid adaptive DE algorithm; RNA GENETIC ALGORITHM; FUEL-CELL; GLOBAL OPTIMIZATION; STEADY-STATE; POWER-SYSTEM; SIMULATION;

D O I：

10.1016/j.energy.2015.06.081

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

In this paper, a HADE (hybrid adaptive differential evolution) algorithm is proposed for the identification problem of PEMFC (proton exchange membrane fuel cell). Inspired by biological genetic strategy, a novel adaptive scaling factor and a dynamic crossover probability are presented to improve the adaptive and dynamic performance of differential evolution algorithm. Moreover, two kinds of neighborhood search operations based on the bee colony foraging mechanism are introduced for enhancing local search efficiency. Through testing the benchmark functions, the proposed algorithm exhibits better performance in convergent accuracy and speed. Finally, the HADE algorithm is applied to identify the nonlinear parameters of PEMFC stack model. Through experimental comparison with other identified methods, the PEMFC model based on the HADE algorithm shows better performance. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：1334 / 1341

页数：8

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