Proton exchange membrane fuel cell behavioral model suitable for prognostics

Abstract

Proton exchange membrane fuel cell Prognostics and health management Behavioral model Static Dynamic a b s t r a c t Prognostics and Health Management (PHM) is a discipline that enables the estimation of the Remaining Useful Life (RUL) of a system and is not yet much applied to Proton Exchange Membrane Fuel Cell PEMFC. However it could permit the definition of adequate conditions allowing extending PEMFC's too short life duration. For that purpose, a model that can reproduce the behavior of a PEMFC is needed. This paper presents a model of a PEMFC that could serve for a prognostics purpose. The model is composed of a static part and a dynamic parts that are independent. On one side, the static part is developed thanks to equations describing the physical phenomena and is based on the ButlereVolmer law.On the other side, the dynamic part is an electrical equivalency of physical phenomenon.The models are validated thanks to experimental data gathered in long term tests. For that purpose the parameters are successively updated based on characterization measurements (polarisation curves and EIS (electrochemical impedance spectroscopy)). Then the results of the model are compared to the ageing data in order to evaluate if the model is able to reproduce the behavior of the fuel cell. The usefulness of this model for prognostics is finally discussed.

An ensemble of models for integrating dependent sources of information for the prognosis of the remaining useful life of Proton Exchange Membrane Fuel Cells

“…However, the coexistence of reversible and irreversible degradation phenomena significantly limits its accuracy [6]. Degradation information can also be obtained by estimating the SOH using characterization measurements, such as Electrochemical Impedance Spectroscopy (EIS) [8] and polarization curves [9,10]. Although the SOH typically provides an accurate estimation of the PEMFC degradation, it can only be measured parsimoniously given that this characterization is intrusive to the stack performance and may introduce additional complex degradation phenomena [6].…”

Prognostics of a multistack PEMFC system with multiagent modeling

“…. This simplified model has been verified to describe well the degradation process of a PEMFC . Thus, it is used in Section IV for generating synthetic degradation data for the case study.…”

“…This simplified model has been verified to describe well the degradation process of a PEMFC. 11,12,25 Thus, it is used in Section IV for generating synthetic degradation data for the case study. For a real PEMFC, the value of β can be estimated with maximum likelihood estimation using Equation (5).…”

“…Denoting that x k = k , the state equation can be represented as By Equation (5-7), the observation equation can be expressed as Note that the difference from the previous work using PF is that the uncertain measurements of a PEMFC considered in this work include the voltage and current, as shown in Equations (11) and (12). The state equation and observation equation define a Bayesian tracking system 29 given the initial state distribution p x 0 |V m 0 ,I m 0 = p x 0 and the independent monitored current and voltage values until time k 0 .…”

“…Because of the complexity, multiphysics, multifailure, and multiscale characteristics of the PEMFC system, it is not realistic to build a precise model of degradation and aging process. Empirical and simplified physical models are proposed in Refs for modeling the degradation process. Extended Kalman filtering and Particle Filtering (PF) are two popular approaches for remaining useful life prediction based on the physical models.…”