增强的饥饿游戏搜索算法结合了海洋掠食者优化算法,以最佳地提取PEM燃料电池中的参数
Mohamed Issa1,2, Mohamed Abd Elaziz3, Sameh I Selem4
1Computer Science and Information Technology Program, Egypt-Japan University of Science and Technology, New Borg El Arab, Egypt. Mohamed.Issa@ejust.edu.eg.
Scientific reports
|February 6, 2025
概括
一种新的HGS-MPA方法改善了质子交换膜燃料电池 (PEMFC) 参数估计. 这种混合优化方法为建模这些关键能源设备提供了更高的准确性和更快的融合.
科学领域:
- 能源系统工程 能源系统工程
- 计算优化计算优化
- 电化学工程 电化学工程
背景情况:
- 质子交换膜燃料电池 (PEMFC) 对于清洁能源至关重要,但由于复杂的非线性动态,很难建模.
- 准确的参数估计对于优化PEMFC性能和可靠性在各种应用中至关重要.
- 现有的建模方法经常在这些非线性系统的收速度和精度上扎.
研究的目的:
- 引入一种新的混合优化算法,HGS-MPA,以改进PEMFC参数估计.
- 增强饥饿游戏搜索 (HGS) 算法的利用能力和融合率.
- 在商业PEMFC数据集上验证拟议方法的有效性.
主要方法:
- 通过将海洋捕食者算法 (MPA) 操作员集成到饥饿游戏搜索 (HGS) 框架中,开发了HGS-MPA算法.
- 应用了HGS-MPA方法来估计三个不同的PEMFC数据集的参数:250-W堆,BCS 500-W和NedStack PS6.
- 使用总平方误差 (SSE) 度量来评估参数估计的性能和准确性.
主要成果:
- HGS-MPA算法实现了卓越的性能,在测试的PEMFC数据集中产生了0.33770,1.31620和0.01174的最低适应性值.
- 与其他最先进的优化算法相比,在融合率和利用能力方面取得了显著的改进.
- 提出的方法在对不同数据集的PEMFC参数进行准确估计方面始终优于现有的技术.
结论:
- 混合HGS-MPA优化方法为准确的PEMFC参数估计提供了强大的和有效的解决方案.
- 这种新的方法为建模非线性燃料电池系统提供了更高的性能和可靠性.
- 研究结果表明,HGS-MPA具有强大的潜力,可以通过改进的建模精度来推进PEMFC技术.
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