基于改进混沌粒子群的PEMFC模型参数辨识

被引：11

作者：

张领先 ^{[1
]}

谢长君 ^{[1
,2
]}

杨扬 ^{[1
,2
]}

刘相万 ^{[1
]}

朱文超 ^{[2
]}

机构：

[1] 武汉理工大学自动化学院

[2] 现代汽车零部件技术湖北省重点实验室(武汉理工大学)

来源：

电工电能新技术 | 2023年 / 42卷 / 01期

基金：

国家重点研发计划;

关键词：

质子交换膜燃料电池; 输出特性模型; 改进的混沌粒子群优化; 参数辨识;

D O I：

暂无

中图分类号：

TM911.4 [燃料电池];

学科分类号：

080811 [新能源发电与电能存储];

摘要：

基于质子交换膜燃料电池(PEMFC)电堆的输出特性及相关电化学反应建立输出特性模型，提出改进混沌粒子群优化(CPSO)算法来优化PEMFC输出特性模型参数辨识问题。首先采用6种标准测试函数验证了CPSO算法的寻优性能，然后针对两种参数不同的电堆进行了输出特性模型参数辨识。结果表明，相较于遗传算法(GA)、粒子群优化(PSO)算法、受约束粒子群优化(B-PSO)算法、具有收缩系数的粒子群优化(PSO-χ)算法、引力粒子群优化(GSAPSO)算法以及差分进化算法(DE),CPSO算法辨识精度最高且收敛速度最快。静态工况下电堆1的均方根误差为0.213,平均相对误差为2.339%;电堆2的均方根误差为0.481,平均相对误差为1.243%,充分说明CPSO算法在PEMFC输出特性模型参数辨识方面的优越性。

引用

页码：29 / 39

页数：11

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