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PEMFC aging modeling for prognostics and health assessment

机译:PEMFC老化模型用于预测和健康评估

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When a system suffers from a too short lifetime, applying prognostics is a good solution to help taking actions extending its life duration. This solution is applied to Proton Exchange Membrane Fuel Cell (PEMFC) stacks in this paper. An important requirement for prognostics of a PEMFC stack is a well-defined framework as well as a great understanding of the degradation mechanisms and failures occurring within the stack. These requirements are addressed here and allow building an efficient model integrating the different levels (stack - cells - components) as well as the multiple causes leading to degradation. Such a model enables then health assessment and remaining useful life predictions. This work proposes a model built based on a selection of critical degradations and to validate it for both state of health estimations and prognostics. The results show that the stack's state of health during aging can be followed accurately with coefficients of correlation greater than 0.9. Also, the behavior of the system can be assessed with a coefficient of correlation greater than 0.9 showing the great predictive capabilities of the model.
机译:当系统寿命过短时,应用预测方法是一种很好的解决方案,有助于采取措施延长其使用寿命。本文将该解决方案应用于质子交换膜燃料电池(PEMFC)电池组。 PEMFC堆栈预测的一项重要要求是定义明确的框架以及对堆栈内部发生的降级机制和故障的充分理解。这些要求在此得到解决,并允许构建一个有效的模型,该模型集成了不同级别(堆栈-电池-组件)以及导致退化的多种原因。这样的模型可以进行健康评估和剩余使用寿命的预测。这项工作提出了一个基于对关键退化的选择建立的模型,并对其进行健康评估和预测的状态进行了验证。结果表明,在相关系数大于0.9的情况下,可以准确地跟踪电池组在老化过程中的健康状态。同样,可以用大于0.9的相关系数评估系统的行为,从而显示出模型的强大预测能力。

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