基于搜寻者优化算法的燃料电池建模

Fuel cell modeling based on seeker optimization algorithm

基于连续空间群体智能的搜寻者优化算法(SOA),提出了一种新的优化燃料电池模型的方法,并将其用于质子交换膜燃料电池(PEMFC)的极化曲线模型,以得到最优参数。实验结果表明,这种优化建模方法使数学模型和实验数据之间达到了较高的拟合精度,对于优化建模具有明显的优越性。

A novel optimization modeling strategy of fuel cell was proposed by adopting seeker optimization algorithm （SOA） in application to continuous space of swarm intelligence. For the demonstration purpose, SOA was used to find a set of optimized coefficients in the proton exchange membrane fuel cell （PEMFC） polar curve model. The experimental results show that the optimized model can fit the experiment data with higher precision. The advantage of SOA is manifest for optimized model.