基于自适应聚焦粒子群算法的质子交换膜燃料电池机理建模  

Mechanism Modeling of Proton Exchange Membrane Fuel Cell Based on Adaptive Focusing Particle Swarm Optimization

作　　者：李奇[1] 陈维荣[1] 刘述奎[1] 林川[1] 贾俊波[2] 

机构地区：[1]西南交通大学,四川省成都市610031 [2]南洋理工大学电力电子工程学院

出　　处：《中国电机工程学报》2009年第20期119-124,共6页Proceedings of the CSEE

基　　金：国家重点实验室自主研究课题项目(2008TPL_Z01)

摘　　要：自适应聚焦粒子群算法(adaptive focusing particle swarm optimization,AFPSO)是根据粒子群(particle swarm optimization,PSO)算法的全局搜索与局部搜索平衡特性,改进得到的一种具有较好全局搜索能力和寻优速度的自适应群体智能优化算法。根据质子交换膜燃料电池(proton exchange membrane fuel cell,PEMFC)模型的建模原理,利用AFPSO算法进行参数估计,得到一组机理模型的最优参数。通过仿真结果与实验结果的对比分析,证明AFPSO算法能够使仿真结果和实验测试数据之间达到很高的拟合精度,对于模型参数估计具有明显的优越性。因此,AFPSO算法对于改善PEMFC机理模型的输出性能将起到重要的作用,并有望成为模型参数优化领域的一种新的有效工具。Adaptive focusing particle swarm optimization （AFPSO） based on the balance characteristic between global search and local search of particle swarm optimization（PSO） was an adaptive swarm intelligence optimization algorithm with preferable ability of global search and search rate. According to the modeling principle of proton exchange membrane fuel cell （PEMFC）, AFPSO was proposed to research a set of optimized parameters in the mechanism model. The comprehensive comparison between simulation results and experimental results demonstrated that AFPSO could make the simulation results fitted the experiment data with higher precision and have manifest superiority for estimating the model parameters. Therefore, AFPSO makes important effect for improving the output performance of PEMFC mechanism model and becomes a new effective tool in the fields of model parameters optimization.

关 键 词：自适应聚焦粒子群算法 质子交换膜燃料电池 机理建模 参数优化 自适应参数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]