通过混合式aquila优化器和算术算法优化算法,彻底改变了质子交换膜燃料电池建模
Manish Kumar Singla1,2, S A Muhammed Ali3, Ramesh Kumar4
1Fuel Cell Institute, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia. msingla0509@gmail.com.
Scientific reports
|February 11, 2025
概括
一个新的混合算法,Aquila Optimizer算术算法优化 (AOAAO),准确地识别了质子交换膜燃料电池 (PEMFC) 的参数. 这种方法可以提高燃料电池建模和预测的速度和精度.
科学领域:
- 电化学 电化学 电化学
- 计算建模计算建模
- 优化算法的优化算法
背景情况:
- 精确的质子交换膜燃料电池 (PEMFC) 建模需要识别数据表中没有发现的参数.
- 优化算法对于确定这些未知变量来预测燃料电池性能至关重要.
研究的目的:
- 引入一个新的混合算法,Aquila Optimizer算法算法优化 (AOAAO),用于增强的PEMFC参数识别.
- 提高PEMFC建模的效率和准确性,使用AOAAO算法中的新突变策略.
主要方法:
- 开发了阿奎拉优化算法算法优化 (AOAAO) 混合算法.
- 利用AOAAO通过最小化预测和测量的电池电压之间的总方位误差 (SSE) 来确定七个未知的PEMFC参数.
- 在6个商业PEMFC模型和12个运营案例研究中验证了AOAAO性能.
主要成果:
- AOAAO实现了最低的SSE,在PEMFC参数识别准确性方面超过了其他算法.
- 该算法在预测电流-电压 (I/V) 和电源-电压 (P/V) 特性方面表现出高准确性和稳定性.
- 与现有方法相比,AOAAO显示了大约98%的显著计算效率改善.
结论:
- AOAAO是一个非常准确,强大的,以及时间效率高的算法,用于实时PEMFC建模.
- 新型突变策略增强了复杂参数识别任务的优化能力.
- AOAAO为燃料电池研发的计算效率提供了显著的进步.
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