使用Rüppell的狐优化器和Sobol指标对PEM燃料电池进行混合参数估计和灵敏度分析
Abdelmonem Draz1, Mohammed H Alqahtani2, Ali S Aljumah3
1Electrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt.
Scientific reports
|December 1, 2025
概括
这项研究介绍了Rüppell的狐优化器 (RFO),用于在质子交换膜燃料电池 (PEMFC) 中准确的参数估计. RFO算法有效地模拟了PEMFC在各种条件中的性能,提供了可靠的优化方法.
科学领域:
- * 能源系统工程 * 能源系统工程
- * 电化学工程 电化学工程
- * 计算优化的优化
背景情况:
- * 质子交换膜燃料电池 (PEMFC) 对清洁能源至关重要,但准确的建模需要精确的参数估计.
- *现有的优化方法在处理PEMFC模型的非线性和多参数性质方面面临挑战.
- * 强大的参数识别对于改善PEMFC性能预测和控制至关重要.
研究的目的:
- * 介绍和评估用于优化PEMFC模型的Rüppell's Fox Optimizer (RFO) 小说.
- *通过将实验数据和建模数据之间的电压误差最小化来估计PEMFCs的未知参数.
- *在不同运行条件下对已确定的PEMFC基准案例验证RFO的有效性.
主要方法:
- * 应用鲁佩尔的狐优化器 (RFO) 算法.
- *使用实验数据和建模数据最小化电压错误的平方和.
- *在不同温度和压力下与三个PEMFC基准模型 (巴拉德Mark V,Horizon H-12 Stack,Temasek 1 kW) 进行验证.
- * 与现有的优化技术进行比较分析.
主要成果:
- * RFO在不同操作条件下估计PEMFC参数时表现出高准确度.
- *与其他方法相比,该算法显示出强大的收行为和可靠的性能.
- * 实现了实验数据的精确建模,证实了RFO对非线性多参数系统的有效性.
结论:
- * 鲁佩尔的狐优化器 (RFO) 是用于PEMFC参数估计的高效和高效的工具.
- * RFO提供了PEMFC的准确和稳定的建模,适合各种应用.
- *这种先进的优化方法为复杂的燃料电池建模挑战提供了有希望的替代方案.
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