Abstract
Today, 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 (PEM) fuel cells (PEMFCs). 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.The two essential components of a PHM scheme for a general engineering system are 1) an accurate aging model that is capable of capturing the system’s gradual health deterioration, and 2) an algorithm for damage estimation and prognostics. In this paper, a physics-based, prognostic-oriented fuel cell catalyst degradation model is developed to characterize 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. An unscented Kalman filter (UKF) approach is then proposed for the purpose of damage tracking and remaining useful life prediction of a PEMFC.
Highlights
To date long-term performance and durability of the fuel cells are difficult to quantify because not all degradation mechanisms of the various fuel cell components are completely understood
The rest of the paper is organized as follows: Section 2 presents the derivation process of a prognostic-oriented fuel cell catalyst degradation model; Section 3 gives a brief introduction to the unscented Kalman filter (UKF) framework; and in Section 4, we apply the UKF approach to the damage tracking and RUL prediction of the fuel cell, and discuss the results obtained from the simulation tests
Various filtering techniques can be implemented in this general recursive estimation framework, including the most widely used extended Kalman filter (EKF), particle filtering (PF), and unscented Kalman filter (UKF)
Summary
To date long-term performance and durability of the fuel cells are difficult to quantify because not all degradation mechanisms of the various fuel cell components are completely understood. The fuel cell’s performance degrades irreversibly throughout its lifetime mainly due to the following components’ degradations: (1) catalyst degradation (catalyst particle coarsening); (2) carbon support degradation (carbon corrosion); and (3) membrane degradation (chemical deterioration and dehydration) (Okada, 2003; Schmittinger & Vahidi, 2008) Factors affecting these degradation processes include temperature, high potentials, heat cycle but most of all water management, contaminants, and impurities. Sheng, Shao-Horn, & Morgan, 2009) investigated the influence of particle size distribution (PSD) and crossover hydrogen on the Pt nanoparticle stability in PEM fuel cells by extending the previous degradation model of Darling and Meyers The rest of the paper is organized as follows: Section 2 presents the derivation process of a prognostic-oriented fuel cell catalyst degradation model; Section 3 gives a brief introduction to the UKF framework; and, we apply the UKF approach to the damage tracking and RUL prediction of the fuel cell, and discuss the results obtained from the simulation tests The rest of the paper is organized as follows: Section 2 presents the derivation process of a prognostic-oriented fuel cell catalyst degradation model; Section 3 gives a brief introduction to the UKF framework; and in Section 4, we apply the UKF approach to the damage tracking and RUL prediction of the fuel cell, and discuss the results obtained from the simulation tests
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Prognostics and Health Management
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
