A review on prognostics and health monitoring of proton exchange membrane fuel cell

Fuel cell technology can be traced back to 1839 when British scientist Sir William Grove discovered that it was possible to generate electricity by the reaction between hydrogen and oxygen gases. However, fuel cell still cannot compete with internal combustion engines although they have many advantages including zero carbon emissions. Fossil fuels are cheaper and present very high volumetric energy densities compared with the hydrogen gas. Furthermore, hydrogen storage as a liquid is still a huge challenge. Another important disadvantage is the lifespan of the fuel cell because of their durability, reliability and maintainability. Prognostics is an emerging technology in sustainability of engineering systems through failure prevention, reliability assessment and remaining useful lifetime estimation. Prognostics and health monitoring can play a critical role in enhancing the durability, reliability and maintainability of the fuel cell system. This paper presents a review on the current state-of-the-art in prognostics and health monitoring of Proton Exchange Membrane Fuel Cell (PEMFC), aiming at identifying research and development opportunities in these fields. This paper also highlights the importance of incorporating prognostics and failure modes, mechanisms and effects analysis (FMMEA) in PEMFC to give them sustainable competitive advantage when compared with other non-clean energy solutions

Prognostics of PEM fuel cell in a particle filtering framework.

International audienceProton Exchange Membrane Fuel Cells (PEMFC) suffer from a limited lifespan, which impedes their uses at a large scale. From this point of view, prognostics appears to be a promising activity since the estimation of the Remaining Useful Life (RUL) before a failure occurs allows deciding from mitigation actions at the right time when needed. Prognostics is however not a trivial task: 1) underlying degradation mechanisms cannot be easily measured and modeled, 2) health prediction must be performed with a long enough time horizon to allow reaction. The aim of this paper is to face these problems by proposing a prognostics framework that enables avoiding assumptions on the PEMFC behavior, while ensuring good accuracy on RUL estimates. Developments are based on a particle filtering approach that enables including non-observable states (degradation through time) into physical models. RUL estimates are obtained by considering successive probability distributions of degrading states. The method is applied on 2 data sets, where 3 models of the voltage drop are tested to compare predictions. Results are obtained with an accuracy of 90 hours around the real RUL value (for a 1000 hours lifespan), clearly showing the significance of the proposed approach

Prognostics and Health Management of PEMFC - state of the art and remaining challenges.

International audienceFuel Cell systems (FC) represent a promising alternative energy source. However, even if this technology is close to being dustrial 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

Dynamic semiempirical PEMFC model for prognostics and fault diagnosis

This article introduces a dynamic semiempirical model that predicts the degradation of a proton exchange membrane fuel cell (PEMFC) by introducing time-based terms in the model. The concentration voltage drop is calculated using a new statistical equation based on the load current and working time, whereas the ohmic and activation voltage drops are updated using time-based equations borrowed from the existing literature. Furthermore, the developed model calculates the membrane water content in the PEMFC, which indicates the membrane hydration state and indirectly diagnoses the flooding and drying faults. Moreover, the model parameters are optimized using a recently developed butterfly optimization algorithm. The model is simple and has a short runtime; therefore, it is suitable for monitoring. Voltage degradation under various loading currents was observed for long working hours. The obtained results indicate a significant degradation in PEMFC performance. Therefore, the proposed model is also useful for prognostics and fault diagnosis

Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell

In this paper, sensor selection algorithms are investigated based on a sensitivity analysis, and the capability of optimal sensors in predicting PEM fuel cell performance is also studied using test data. The fuel cell model is developed for generating the sensitivity matrix relating sensor measurements and fuel cell health parameters. From the sensitivity matrix, two sensor selection approaches, including the largest gap method, and exhaustive brute force searching technique, are applied to find the optimal sensors providing reliable predictions. Based on the results, a sensor selection approach considering both sensor sensitivity and noise resistance is proposed to find the optimal sensor set with minimum size. Furthermore, the performance of the optimal sensor set is studied to predict fuel cell performance using test data from a PEM fuel cell system. Results demonstrate that with optimal sensors, the performance of PEM fuel cell can be predicted with good quality

Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems .

International audienceThis paper studies the prediction of the output voltage reduction caused by degradation during nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) which use as input the measures of the fuel cell output voltage during operation. The paper presents the architecture of the ANFIS and studies the selection of its parameters. As the output voltage cannot be represented as a periodical signal, the paper proposes to predict its temporal variation which is then used to construct the prediction of the output voltage. The paper also proposes to split this signal in two components: normal operation and external perturbations. The second component cannot be predicted and then it is not used to train the ANFIS. The performance of the prediction is evaluated on the output voltage of two fuel cells during a long term operation (1000 hours). Validation results suggest that the proposed technique is well adapted to predict degradation in fuel cell systems

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavy-duty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling approaches for the FCS, battery, ultracapacitor, and further relevant subsystems are briefly described. Afterward, common methodologies for EMS are structured, presenting a new taxonomy for dynamic optimization-based EMS from a control engineering perspective. Finally, the findings lead to a guideline toward holistic EMS, encouraging the co-optimization of system design, and EMS development for FCHEVs. For the EMS, we propose a layered model predictive control (MPC) approach, which takes velocity planning, the mitigation of degradation effects, and the auxiliaries into account simultaneously

Factors influencing fuel cell life and a method of assessment for state of health

Philosophiae Doctor - PhDProton exchange membrane fuel cells (PEMFC) converts chemical energy from the electrochemical reaction of oxygen and hydrogen into electrical while emitting heat, oxygen depleted air (ODA) and water as by-products. The by-products have useful functions in aircrafts, such as heat that can be used for ice prevention, deoxygenated air for fire retardation and drinkable water for use on board. Consequently, the PEMFC is also studied to optimize recovery of the useful products. Despite the progress made, durability and reliability remain key challenges to the fuel cell technology. One of the reasons for this is the limited understanding of PEMFC behaviour in the aeronautic environment. The aim of this thesis was to define a comprehensive non-intrusive diagnostic technique that provides real time diagnostics on the PEMFC State of Health (SoH). The framework of the study involved determining factors that have direct influence on fuel cell life in aeronautic environment through a literature survey, examining the effects of the factors by subjecting the PEMFC to simulated conditions, establishing measurable parameters reflective of the factors and defining the diagnostic tool based on literature review and this thesis finding

PROGNOSTIC AND HEALTH-MANAGEMENT ORIENTED FUEL CELL MODELING AND ON-LINE SUPERVISORY SYSTEM DEVELOPMENT

Of the fuel cells being studied, the proton exchange membrane fuel cell (PEMFC) is viewed as the most promising for transportation. Yet until today, the commercialization of the PEMFC has not been widespread in spite of its large expectation. Poor long term performances or durability, and high production and maintenance costs are the main reasons. For the final commercialization of fuel cells in the transportation field, durability issues must be addressed, while costs should be further brought down. At the same time, health-monitoring and prognosis techniques are of 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. This dissertation presents a comprehensive on-line supervisory system to address the important issues related to the PEMFC durability, including: 1) diagnosis of critical operating conditions, 2) optimization of the operating conditions, and 3) health monitoring (or damage tracking) and remaining useful life (RUL) prediction. In order to design and implement this supervisory system, a comprehensive fuel cell model is developed that integrates a control/diagnostic oriented dynamic fuel cell model and a prognostic oriented fuel cell degradation model, due to a lack of such models in the existing literature. To address the first issue, a model-based on-line diagnostics system is developed for fuel cell flooding and drying diagnosis, thanks to the incorporation of the diagnostic feature in the dynamic fuel cell model. The channel flooding diagnostic problem is decoupled from the gas diffusion layer (GDL) flooding and membrane drying diagnostic problem. Simultaneous state and parameter estimation problems are formulated for both cases. Dual extended Kalman filter (EKF) and dual unscented Kalman filter (UKF) techniques are applied respectively to solve the problems. The second issue is addressed by a diagnostic based control design for the air supply of the fuel cell system. The design concept allows selection of the most suitable controller in a controller bank that delivers the best performance under specific operating conditions and that mitigates the faulty condition based on the feedback of the diagnosis results. The control problem is reformulated as an H-infinity robust control problem, the objective of which is to minimize the difference between the desired and actual excess O2 ratio, thus preventing and minimizing oxidant starvation at the cathode. Finally, an UKF-based health-monitoring and prognostic scheme is proposed and applied to the damage tracking and RUL prediction for the fuel cell. The developed aging model is employed as the kernel for this scheme, which utilizes the fuel cell output voltage as the only feature for the prognostic and health management task