This study proposes a fault detection and isolation tool for proton exchange membrane fuel cell (PEMFC) operating in embedded applications. A model‐based approach, taking partially into account degradation phenomena, is proposed in order to increase the robustness of the tool regarding transient operations and stack ageing. The considered faults are the abnormal operating conditions that can decrease the fuel cell lifetime. The fault detection approach is based on residual generation using both voltage and high frequency resistance measurements and thus combining the advantages of knowledge‐based model and electrochemical impedance spectroscopy (EIS) diagnosis approaches. To that end, a multi‐physics fuel cell model has been used. This model computes not only the stack voltage but also the high frequency resistance in dynamic conditions. Additionally, the model is modified to take into account the ageing of the fuel cell. Validation is carried out on experimental characterizations during 1,000 h ageing. The results on a new fuel cell stack show a score of 91% for fault isolation. However, without any adaptation, this score drops dramatically as the stack ages. Finally, thanks to ageing modeling and to the proposed adaptation of the detection/isolation procedure, the diagnosis performance remains reliable during fuel cell stack ageing.

中文翻译：

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阻抗测量和多物理场建模的质子交换膜燃料电池故障检测与隔离

这项研究提出了一种用于嵌入式应用中的质子交换膜燃料电池（PEMFC）的故障检测和隔离工具。提出了一种基于模型的方法，部分考虑了退化现象，以提高工具在瞬态操作和烟囱老化方面的稳健性。所考虑的故障是会缩短燃料电池寿命的异常工况。故障检测方法基于使用电压和高频电阻测量的残余物生成，因此结合了基于知识的模型和电化学阻抗谱（EIS）诊断方法的优势。为此，使用了多物理场燃料电池模型。该模型不仅计算堆电压，还计算动态条件下的高频电阻。另外，修改模型以考虑燃料电池的老化。验证是在1000小时的老化过程中对实验特性进行的。在新的燃料电池堆上的结果显示，故障隔离得分为91％。但是，没有任何适应性，该分数会随着堆栈的老化而急剧下降。最后，由于老化模型和建议的检测/隔离程序的调整，在燃料电池堆老化期间，诊断性能仍然可靠。