Predictive virtual modelling framework for performance and platinum degradation modelling of high temperature PEM fuel cells. (February 2019)

• Record Type: Journal Article
• Authors: Kregar, Ambrož; Tavčar, Gregor; Kravos, Andraž; Katrašnik, Tomaž
• Is Part Of: Energy procedia. Volume 158(2019)
• Page Start: 1817
• Page End: 1822
• Publication Date: 2019-02
• Subjects: Fuel cell -- Proton exchange membrane -- High temperature -- Modelling -- Predictive -- Platinum degradation; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2019.01.426
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: High temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) are a promising and emerging technology that allow for highly efficient low emission small scale electricity and heat generation. Simultaneous reduction of production costs and prolongation of the service life is considered as a significant challenge towards their wider market adoptions, which calls for application of predictive virtual tools during the development process of HT-PEMFC systems. To present a significant progress in the addressed area, this paper presents an innovative modelling framework based on: a) mechanistically based spatially and temporally resolved HT-PEMFC performance model and b) modular degradation modelling framework based on interacting partial platinum degradation mechanisms. Proposed innovative tool chain thus allows for - compared to the current state of the art – more efficient and systematic model supported design of FCs and in-depth understanding of cause and effect chain from FC operation to its degradation. This merit of the proposed modelling framework arises from systematic reflection of FC control parameters in operational parameters of the FC, which are inputs to degradation modelling framework that considers in an interacting manner carbon and Pt oxidation phenomena and Pt dissolution, redeposition, detachment and agglomeration phenomena thereby adequately modelling the causal chain.