Contribution to prognostics of PEM fuel cells: approaches based on degradation information at multiple levels

In the context of the energy transition, fuel cell becomes one of the promising alternative energy sources. Recently the spotlight is on fuel cell systems research, and more particularly on Proton Exchange Membrane Fuel Cell (PEMFCs) which is one of the best candidates for both stationary and transportation applications. Even if this technology is close to being competitive, it is not yet ready to be considered for a large scale industrial deployment because of its limited durability and reliability. Prognostics and Health Management (PHM) is a recent approach to manage and possibly extend life duration of technological systems. Prognostic techniques can provide an estimation of fuel cell State Of Health (SOH) and a prediction for their Remaining Useful Life (RUL) to help the manufacturers improving fuel cell performance and managing its lifespan. The objective of this work is to develop prognostic methodologies for the RUL prognosis adapted to the complexity of PEMFCs. Indeed, the PEMFC is a multi-scale and multi-physics system, and various challenges are faced: 1. The definition of SOH to build a degradation indicator. 2. The coexistence of both reversible and irreversible degradation phenomena. 3. Taking into account different deterioration causes and effects of operating conditions. In the first part of our work, we conduct a state of the art analysis on PHM for PEMFCs, with the aim of proposing a SOH definition and building a degradation indicator for PEMFC prog- nosis purposes. Moreover, since PEMFC measurements are scarce, state of the art on Lithium batteries, other electrochemical cells, is also explored. In the second part, we develop a particle filtering based prognostic algorithm for PEMFC, based on output power measurements. The first results show that the prognosis algorithm is disturbed by the existing reversible degradation. However, the irreversible degradation can be estimated thanks to characterization tests, such as Electrochemical Impedance Spectroscopy (EIS), which is applied from time to time. We propose thus an adapted & extended prognostic algorithm to take into account both health indicators: the output power degradation and the SOH degradation estimated from EIS characterization. The performance of the proposed algorithm is evaluated by different prognostic performance metrics, and the results show the interest of this approach. In the third part, the problem is addressed from a more theoretical point of view. Indeed, a system’s degradation behavior is often correlated with internal and external covariates which are usually difficult to access owing to expensive measurement cost. Therefore, we first developed a prognostic approach with online inspections on the degradation covariate at a different level, and then we propose an approach for RUL prognosis based on an ensemble of models using different sources at different levels. The RUL predictions of both models are dynamically aggregated on the basis of prognostic performance evaluated on a set of historical data. Consequently, the prediction accuracy is improved by overcoming both models’ drawbacks and leveraging their strengths. In the last part, we extend the problem to multi-level prognostics and explore new possibilities, which open new aspects for future research on PEMFC lifetime prognosis and management.