Abstract
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 diagnostic-oriented 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.
Highlights
Humidity management is a critical, yet delicate, issue in fuel cell system
Fuel cell flooding diagnosis is, critically important since the information it provides can be utilized by other on-line supervisory systems to address both the instantaneous performance degradation and the long-term durability issue of the fuel cell systems (Zhang, 2012), e.g. the channel flooding information can be used by some fault-tolerant control system to optimize the purging procedures and mitigate the flooding problem, while the gas diffusion layer (GDL) flooding information can be fed to an on-line prognostic and healthmonitoring scheme for system damage tracking and remaining useful life prediction
We focus on the unscented Kalman Filter (UKF) approach since we believe it gives a nice tradeoff between particle filtering (PF) and extended Kalman filter (EKF)
Summary
As pointed out in the work by Kumbur et al (Kumbur, Sharp, & Mench, 2006), according to different locations of water accumulation, three types of flooding can be identified in PEM fuel cells, namely i) catalyst layer flooding, ii) gas diffusion layer (GDL) flooding, and iii) flow field flooding. The DM flooding hinders the reactant transport to the catalyst layers where the reaction takes place and results in a higher mass concentration loss, it aggravates the corrosion and the degradation of various of the fuel cell components including the catalyst layer, the GDL, and even the membrane (Zhang, 2012).
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