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优化器具有多个搜索策略的参数估计质子交换膜燃料电池模型

Lakhdar Chaib¹, Fatima Zahra Khemili¹, Mohammed Tadj¹

¹Energy and Materials Laboratory, University of Tamanghasset, P.O. Box 10034, 11001, Sersouf, Tamanghasset, Algeria.

Scientific reports

|March 15, 2025

概括

一个新的Parrot优化器 (PO) 改进了对质子交换膜燃料电池 (PEMFC) 模型的参数识别. 这种增强的方法,IPO,提高了PEMFC绩效建模的效率和准确性.

科学领域:

能源系统工程能源系统工程
计算科学计算科学
电化学工程电化学工程

背景情况:

质子交换膜燃料电池 (PEMFC) 是主要的可再生能源,因为它们的高效率和耐用性.
准确的PEMFC电化学模型需要精确估计七个未知的参数.
在PEMFC模型中参数识别是一个复杂的,非线性优化挑战.

研究的目的:

引入一个改进的优化算法,即改进的优化器 (IPO),用于PEMFC模型中准确的参数识别.
通过基于对立的学习和本地逃生运营商来增强基本的Parrot Optimizer (PO),以提高搜索效率和融合.
为了验证IPO在基准函数和现实世界的PEMFC堆模型上的有效性.

主要方法:

通过将基于对立的学习和本地逃跑运营商集成到基本的Parrot Optimizer (PO) 中,开发了改进的Parrot Optimizer (IPO).
使用标准数学基准函数对IPO进行验证,以评估其优化能力.
在三个不同的PEMFC堆 (NedStackPS6,BCS堆,Ballard Mark V) 上对IPO进行实证测试,以优化其电化学模型.

主要成果:

与基本PO相比,IPO表现优越,在最佳指标中取得了12.87%的改善,标准偏差降低了88.37%.
首次公开募股成功优化了PEMFC模型用于NedStackPS6,BCS Stack和Ballard Mark V,产生了较低的平方误差总和 (SQE) 值 (分别为2.065816 V,0.012457 V和0.814325 V).
敏感性分析证实IPO能够准确地描述PEMFC模型,显示一致的趋同轨迹.

结论:

IPO是一种强大而有效的优化技术,用于识别PEMFC电化学模型中的未知参数.
增强的PO算法显著提高了PEMFC性能建模的准确性和可靠性.
首次公开募股能够实现最低SQE值和稳定的收,这突显了其在推进PEMFC技术方面的潜力.

关键词:

改进了优化算法.在PEM燃料电池模型中.参数估计的参数估计.

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