Prognostics and Health Management of PEMFC – State of the art and remaining challenges

Abstract

Fuel Cell systems (FC) represent a promising alternative energy source. However, even if this technology is close to being competitive, it is not ready for large scale industrial deployment: FC still must be optimized, particularly by increasing their limited lifespan. This involves a better understanding of wearing processes and requires emulating the behavior of the whole system. Furthermore, a new area of science and technology emerges: Prognostics and Health Management (PHM) appears to be of great interest to face the problems of health assessment and life prediction of FCs. According to this, the aim of this paper is to present the current state of the art on PHM of FCs, more precisely of Proton-Exchange Membrane Fuel Cells (PEMFC) stack. PHM discipline is described in order to depict the processing layers that allow early deviations detection, avoiding faults, deciding mitigation actions, and thereby increasing the useful life of FCs. On this basis, a taxonomy of existing works on PHM of PEMFC is given, highlighting open problems to be addressed. The whole enables getting a better understanding of remaining challenging issues in this area.

Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

“…Number of fuel cells 54 Active electrode area of single cell (cm 2 The performance of the developed fuel cell model is evaluated with test data from the fuel cell system in a previous study [24]. With the fuel cell parameters listed in Table 1, the fuel cell model can be developed, and by determining the model parameters, including internal and exchange current densities, mass transport coefficients, membrane resistance, etc., the losses in Equation (1) can be calculated, and fuel cell voltage can then be determined with Equation (1).…”

“…In the analysis, a unit change (1%) is applied to the model parameters, and the variations in fuel cell responses (sensor measurements) can be obtained, which can be expressed with Equation (2).…”

“…From previous studies [1][2][3], PHM analysis requires several stages, including data collection, data processing, condition monitoring, diagnostics, prognostics, and decision support. From a literature review, it is observed that most fuel cell PHM studies focus on the diagnostic stage, which can be loosely divided into two groups: model-based methods and data-driven techniques.…”

PEMFC for aeronautic applications: A review on the durability aspects

“…Despite the complexities, methods are developed to predict Fuel Cell State of Health (SoH) in terms of performance loss, degradation and, fault detection and isolation (FDI) [45,63,[74][75][76][77][78][79]. Diagnostic methods available are model or non-model based [71,[80][81][82][83][84]. Model-based methods are further classified as white, grey or black box depending on the nature of input and output.…”

“…Non-model based methods are further grouped as artificial intelligence, statistical method or signal processing [82]. An emerging area of science called Prognostics and Health Management (PHM) focuses on methods that assess State of Health (SoH), predict Remaining Useful Lifetime (RUL) and decide mission achievement from mitigation actions [80,85]. PHM can be classified as a signal processing method since it involves active data acquisition to identify and isolate faults (fault diagnosis) that accelerate ageing of a fuel cell.…”