平方根无气味卡尔曼波器基于PEM燃料电池的多模型故障诊断
Abdulrahman Allam1, Michael Mangold1, Ping Zhang2
1Institute of Applied Mathematics, Bingen University of Applied Sciences, 55411 Bingen am Rhein, Germany.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
这项研究引入了一种新型的质子交换膜燃料电池 (PEMFC) 诊断模型,用于监测运行期间的催化降解. 先进的故障诊断方案确保了电动汽车推进系统的可靠性能监测.
科学领域:
- 电化学工程 电化学工程
- 材料科学 材料科学 材料科学
- 汽车工程 汽车工程
背景情况:
- 车辆应用对质子交换膜燃料电池 (PEMFC) 施加了严苛的条件,限制了它们在电力推进中的使用.
- 快速变化的电流需求可能会导致洪水和催化降解等关键故障,影响膜电极组装和性能.
- 由于催化剂层的重要性和成本,监测内部PEMFC状态至关重要.
研究的目的:
- 开发一个以诊断为导向的PEMFC催化降解的多尺度模型.
- 将故障影响纳入细胞动态和整体堆性能.
- 实施故障诊断方案,以实时监控和可靠运行.
主要方法:
- 开发了一个多尺度的PEMFC催化降解模型.
- 嵌入了一个基于无气味卡尔曼波器 (SRUKF) 的平方根多模型故障诊断方案.
- SRUKF估计了内部PEMFC参数,更新了模型选择和故障指示的贝叶斯框架.
主要成果:
- 开发的模型成功地结合了催化降解对细胞动态和堆性能的影响.
- 基于SRUKF的诊断方案提供了在线状态估计和贝叶斯模型选择用于故障指示.
- 使用LA 92和NEDC驾驶周期的模拟验证了拟议的诊断方案的可靠性.
结论:
- 拟议的诊断模型和故障诊断方案有效监测PEMFC的催化降解.
- 这种方法提高了PEMFC在车辆应用中的可靠性和操作安全性.
- 该方法为改善性能管理提供了对燃料电池内部状态的关键见解.
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