使用简化模型精确模拟PEM燃料电池的稳定状态和动态性能
Hossam Ashraf1, Mahmoud M Elkholy2, Sameh O Abdellatif3
1Electrical Engineering Department, Faculty of Engineering and FabLab in the Centre for Emerging Learning Technologies (CELT), The British University in Egypt (BUE), Cairo, Egypt.
Scientific reports
|November 9, 2023
概括
这项研究引入了一种新的开普勒优化算法 (KOA),以准确确定质子交换膜燃料电池 (PEMFC) 堆的关键参数. 该KOA方法在优化PEMFC模型以提高效率方面表现出卓越的性能.
科学领域:
- 电化学 电化学 电化学
- 可再生能源系统可再生能源系统
- 计算机建模 计算建模
背景情况:
- 质子交换膜燃料电池 (PEMFCs) 对清洁能源至关重要.
- 精确的参数提取对于优化PEMFC性能和效率至关重要.
- 现有的模型往往需要精确的参数化才能进行可靠的模拟.
研究的目的:
- 开发和验证一种用于在PEMFC模型中提取七个未知参数的新方法.
- 评估开普勒优化算法 (KOA) 与其他优化技术的性能.
- 通过结合电动动态响应来增强PEMFC建模.
主要方法:
- 使用平方偏差总和 (SSD) 作为参数优化的成本函数.
- 采用开普勒优化算法 (KOA) 来确定最佳参数值.
- 在不同操作条件下对四种不同的PEMFC堆模型验证了KOA方法.
主要成果:
- 在Ballard Mark,BCS 0.5kW,NedStack PS6和Temasek 1kW PEMFC中,获得了0.810578 V2,0.0116952 V2,2.10847 V2和0.590467 V2的最低SSD值,分别为0.810578 V2,0.0116952 V2和0.590467 V2的值.
- 通过与四个具有挑战性的和最近报告的算法进行公平比较,证明了KOA的优势.
- 成功引入了一种简化模型升级,用于捕获PEMFCs的电动态反应.
结论:
- 开普勒优化算法 (KOA) 是用于PEMFC参数提取的可行和有效工具.
- 拟议的方法对实时应用,包括稳定状态和过渡条件具有前景.
- 建议进一步扩展KOA以实时监测和控制PEMFC.
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