使用基于直角学习的GOOSE算法对PEM燃料电池数学模型进行参数表征
Premkumar Manoharan1,2, Sowmya Ravichandran3, S Kavitha4
1Department of Electrical and Electronics Engineering, College of Engineering, Institute of Power Engineering (IPE), Universiti Tenaga Nasional (UNITEN), Putrajaya, 43000, Kajang, Selangor, Malaysia.
Scientific reports
|September 9, 2024
概括
一个新的GOOSE算法准确地估计了质子交换膜燃料电池 (PEMFC) 的参数. 这种方法改善了燃料电池建模和仿真,以提高性能和技术应用.
科学领域:
- 能源系统 能源系统
- 电化学工程 电化学工程
- 计算建模 计算建模
背景情况:
- 质子交换膜燃料电池 (PEMFCs) 需要精确的建模以获得最佳性能和模拟.
- 燃料电池中复杂的非线性行为需要精确的参数确定.
- 有效的燃料电池设计对于各种技术应用至关重要.
研究的目的:
- 为PEMFCs开发一个改进的参数估计方法.
- 为了提高燃料电池建模和仿真的准确性和稳定性.
- 引入一种由自然适应性行为启发的新算法.
主要方法:
- 提出了一种增强的GOOSE算法,具有直角学习机制.
- 使用根平均平方误差作为参数优化的目标函数.
- 通过使用各种数据集和与最先进的方法进行比较,通过实验验证算法.
主要成果:
- 与现有算法相比,增强的GOOSE算法表现出优越的性能.
- 提出的方法在估计PEMFC参数方面取得了有希望的结果.
- 该算法有效模拟复杂的系统,提高模拟工具的适应性.
结论:
- 增强的GOOSE算法为PEMFC参数估计提供了强大的和可适应的方法.
- 这种方法促进了燃料电池技术的更准确和更有效的进步.
- 这项研究强调了生物灵感算法在复杂系统建模中的潜力.
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