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Parameter optimization of PEMFC model using backtracking search algorithm

机译:基于回溯搜索算法的PEMFC模型参数优化

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Proton Exchange Membrane Fuel Cell (PEMFC) is one of the promising alternative energy source that can decrease the adverse effect of greenhouse gas emissions. However, predicting the output voltage of PEMFC is difficult because of its non-linear characteristic. Thus, in this paper the modern backtracking search algorithm (BSA) is applied for modeling the PEMFC and extracting model parameters. Initially some experiments are performed on PEMFC system while changing its load linearly. The model parameters are optimized using BSA with root mean square error as an objective function. The final modeled voltage shows that the BSA provide better results than particle swarm optimization (PSO) in modelling PEMFC output voltage.
机译:质子交换膜燃料电池(PEMFC)是可以减少温室气体排放的不利影响的有希望的替代能源之一。但是,由于PEMFC的非线性特性,因此很难预测其输出电压。因此,本文将现代回溯搜索算法(BSA)用于PEMFC建模并提取模型参数。最初在PEMFC系统上进行一些实验,同时线性改变其负载。使用BSA以均方根误差作为目标函数来优化模型参数。最终建模电压表明,在对PEMFC输出电压建模时,BSA比粒子群优化(PSO)提供更好的结果。

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