A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters
A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters
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DOI：
10.1631/jzus.c1000355
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发表时间：
2011-08
期刊：
影响因子：
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通讯作者：
A. Askarzadeh;A. Rezazadeh
中科院分区：
文献类型：
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作者：
A. Askarzadeh;A. Rezazadeh
合适的数学模型可以帮助研究人员对质子交换膜燃料电池（PEMFC）电堆系统进行仿真、评估和控制。由于质子交换膜燃料电池是一个非线性和强耦合的系统，许多假设和近似被认为是在建模过程中。因此，模型计算结果与PEMFC的真实的性能存在一定的差异。为了提高模型的精度，使其更好地描述实际性能，优化PEMFC模型参数是必不可少的。本文提出了一种人工蜂群优化算法，称为ABSO，用于优化稳态PEMFC电堆模型的参数，适合于电气工程应用。为了研究所提出的算法的有用性，ABSO为基础的结果进行了比较，从遗传算法（GA）和粒子群优化（PSO）的结果。实验结果表明，ABSO算法的性能优于其他算法。
An appropriate mathematical model can help researchers to simulate, evaluate, and control a proton exchange membrane fuel cell (PEMFC) stack system. Because a PEMFC is a nonlinear and strongly coupled system, many assumptions and approximations are considered during modeling. Therefore, some differences are found between model results and the real performance of PEMFCs. To increase the precision of the models so that they can describe better the actual performance, optimization of PEMFC model parameters is essential. In this paper, an artificial bee swarm optimization algorithm, called ABSO, is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications. For studying the usefulness of the proposed algorithm, ABSO-based results are compared with the results from a genetic algorithm (GA) and particle swarm optimization (PSO). The results show that the ABSO algorithm outperforms the other algorithms.