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Batteries and Fuel Cells03:12

Batteries and Fuel Cells

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A battery is a galvanic cell that is used as a source of electrical power for specific applications. Modern batteries exist in a multitude of forms to accommodate various applications, from tiny button batteries such as those that power wristwatches to the very large batteries used to supply backup energy to municipal power grids. Some batteries are designed for single-use applications and cannot be recharged (primary cells), while others are based on conveniently reversible cell reactions that...
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Updated: Jan 16, 2026

A Guide to Concentration Alternating Frequency Response Analysis of Fuel Cells
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A Guide to Concentration Alternating Frequency Response Analysis of Fuel Cells

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根据修改的河马优化算法提取PEM燃料电池变量.

Eman Abdullah Aldakheel1, Alaa A K Ismaeel2, Ali M El-Rifaie3

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

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|September 29, 2025
PubMed
概括

一个修改后的河马优化 (MHO) 算法准确地识别了质子交换膜燃料电池 (PEMFC) 的关键参数,改善了性能预测,并使数字双胞胎开发成为可能.

关键词:
燃料电池燃料电池的使用情况.修改后的河马优化优化参数识别 参数识别

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科学领域:

  • 电化学 电化学 电化学
  • 计算优化计算优化

背景情况:

  • 质子交换膜燃料电池 (PEMFCs) 需要精确的参数识别来准确的性能建模.
  • 像海马优化 (HO) 这样的现有优化算法遭受了局部最佳和缓慢的融合.
  • 制造商数据表往往缺乏燃料电池性能预测所需的关键参数.

研究的目的:

  • 开发一个修改后的河马优化 (MHO) 算法,以克服传统HO的局限性.
  • 准确识别PEMFC绩效预测模型的未知参数.
  • 将MHO的有效性与其他优化算法进行比较.

主要方法:

  • 一种新的利用机制和增强解决方案质量方法被整合到HO算法中,以创建MHO.
  • 使用五种优化算法 (MHO,GWO,HO,CHOA,SCA) 来确定六个未知的PEMFC参数.
  • 估计和测量电池电压之间的总方位误差 (SSE) 作为最小化的适应性函数.

主要成果:

  • 该MHO算法实现了1.748996055.5的最低总平方误差 (SSE).
  • 与HO,GWO,CHOA和SCA相比,MHO在参数识别方面表现优越.
  • MHO比其他测试的优化技术表现出更快的收率.

结论:

  • 对于PEMFC中的参数识别,MHO算法非常有效,从而可以准确地预测性能.
  • 由于MHO的准确性和速度,它非常适合在汽车行业开发数字双胞胎和控制系统.
  • 拟议的MHO算法为燃料电池应用提供了与现有的基于群集的优化方法相比的显著进步.