An Unscented Kalman Filter Based Approach for the HealthMonitoring and Prognostics of a Polymer Electrolyte Membrane Fuel Cel
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Abstract
Poor long-term performance and durability combined with high production and maintenance costs remain the main obstacles for the commercialization of the polymer electrolyte membrane fuel cell (PEMFC). While on-line diagnosis and operating condition optimization play an important role in addressing the durability issue of the fuel cell, health-monitoring and prognosis (or PHM) techniques are of equally great significance in terms of scheduling condition-based maintenance (CBM) to minimize repair and maintenance costs, the associated operational disruptions, and also the risk of unscheduled downtime for the fuel cell systems.
In this paper, an unscented Kalman filter (UKF) approach is proposed for the purpose of damage tracking and remaining useful life (RUL) prediction of a PEMFC. To implement this model-based PHM framework, a physics-based, prognostic-oriented catalyst degradation model is developed to characterize the fuel cell damage that establishes the relationship between the operating conditions and the degradation rate of the electro-chemical surface area (ECSA). The model complexity is kept minimal for on-line prognostic purpose. Simulation is carried out for validation of the proposed algorithm, using a more detailed catalyst degradation model.
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PHM
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