A long life time and safe operation are important issues when polymer electrolyte membrane fuel cell (PEMFC) stacks are used as power supply in technical systems. Therefore, methods are needed to detect deviations from the chosen operating point before any damage to the stack or the environment occurs. In applications like vehicles, the fuel cell operation is highly dynamic, and special diagnosis cycles can not be used during operation. Thus, a diagnosis system is needed which uses the high dynamic data from operation. However, due to limitations of computational power, this diagnosis system must be as simple as possible. In this paper, the linear canonical variate analysis (CVA), which is a subspace identification method, is used as a means for modelling the non-linear PEMFC stack. The linear state-space models can be shown to represent well the input-output behavior of the stack. Additionally, two concepts are proposed using state-space models from linear CVA for diagnosis purposes.
['Michael Buchholz', 'Mathias Eswein', 'Volker Krebs']
Modelling PEM fuel cell stacks for FDI using linear subspace identification