基于改进的灰色滞后子算法,优化机车的混合能源系统
Xiaogui Gou1, Jialing Li2, Bayram Yazdani3,4
1College of intelligent manufacturing, Chongoing Institute Of Engineering, Chongqing, 400056, China.
Scientific reports
|September 29, 2025
概括
改进的灰色滞后优化 (IGLGO) 算法显著降低了混合机车能源系统的成本. 这种新方法优化了聚合物电解质膜燃料电池和离子电池,以实现可持续的铁路推进.
科学领域:
- 工程 工程师 工程师 工程师
- 优化算法 优化算法
- 可持续能源 可持续能源
背景情况:
- 混合机车能源系统需要高效的成本优化.
- 整合聚合物电解质膜 (PEM) 燃料电池和离子电池带来了复杂的设计挑战.
- 现有的优化算法可能很难在这样的系统中避免局部最佳.
研究的目的:
- 引入一个改进的灰色滞后优化 (IGLGO) 算法.
- 为了最大限度地降低混合机车能源系统的总成本.
- 在优化中提高勘探-开发平衡,以获得更好的结果.
主要方法:
- 开发了改进的灰色滞后优化 (IGLGO) 算法.
- 在IGLGO内纳入了一个动态分组机制.
- 利用微积分计算来完善优化过程.
- 将算法应用于PEM燃料电池和离子电池的混合系统.
主要成果:
- 与标准的灰色滞后优化 (GLGO) 和其他元启发算法相比,IGLGO算法实现了更具成本效益的系统设计.
- 对于2%的轨道斜率,IGLGO优化系统的成本为378万美元,远低于GLGO (441万美元) 和矮人蒙古斯优化器 (484万美元).
- 通过平衡的勘探开发战略,证明了避免局部最佳情况.
结论:
- IGLGO算法为混合动力机车能源系统提供了一个强大的和经济的优化框架.
- 这种方法对可持续的铁路推进非常有效.
- 在复杂的能源系统设计中,IGLGO为最大限度地降低成本提供了优质的替代方案.
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