Model-based fault detection using analytical redundancy for automotive proton exchange membrane fuel cell

In this paper, we present a model-based approach for fault detection and isolation of faulty operating conditions of proton exchange membrane fuel cells, and we analyse experimental results of fault detection obtained on a 20 cells fuel cell stack. The system is modelled using MEPHYSTO-FC, a 2D + 0D multi-physics fuel cell model based on lumped and bond-graph approach. Parameters of the model are identified on the 20 cells stack. For the experiments, the fuel cell is operated in nominal condi- tions and in seven different faulty conditions. The model computes the estimated fuel cell voltage and the real part of the high frequency impedance. This model-based estimation is compared to the measured data to generate two residual signals used for the fault detection. The detection algorithm is finally veri- fied during the time evolution of operating conditions, creating faults in the fuel cell and observing the residuals.