An Unscented Kalman Filter Based on-line Diagnostic Approach For PEM Fuel Cell Flooding

Poor water management usually leads to various degrees of flooding in the hydrogen type fuel cell, affecting both the instantaneous performance and the long-term durability of the system adversely. While a lot of fuel cell diagnostic tools exist that could be utilized for the flooding diagnostics, most of these approaches are intrusive, requiring special modification to the fuel cell that affects its integrity, or special equipment (e.g. AC spectrometer) that adds to the complexity and cost of the system, and therefore are not considered to be a viable solution for the on-board integration of the diagnostic scheme. This paper proposes a model based approach for the fuel cell flooding diagnostics problem, utilizing only the cell current and voltage, and the inlet pressures of the fuel cell as the input signals of the diagnostic scheme. A diagnosticoriented fuel cell system dynamic model is developed to incorporate the effects of the fault, i.e. the flooding, on the system dynamics. For simplicity, only the cathode channel flooding, the cathode gas diffusion layer (GDL) flooding, and the anode channel flooding are considered while we neglect the mass transport loss through the anode GDL. The cathode channel flooding and the GDL flooding diagnostic problems are decoupled and formulated as standard joint state and parameter estimation problems, with the amounts of the liquid water treated as varying system parameters to be identified. The unscented Kalman Filter technique has been applied to solve these problems. Simulation results validate the applicability of the cascading unscented Kalman filter design for flooding diagnostics.