Diagnosis of MEA degradation for health management of polymer electrolyte fuel cells

Diagnostics and health management are fundamental components in a strategy to improve durability and lifetime of polymer electrolyte fuel cells. Fuel cells require a range of operating conditions to be well managed for achieving performance or durability objectives. So far, water management issues and single parameter diagnostics for individual degradation modes have been the focus of research in the literature. However, there has been minimal research on the application of fuzzy inference systems for online, multiple parameter diagnosis of fuel cells. This research presents an advanced fuzzy inference system for diagnostics and health management of a membrane electrode assembly (MEA) for polymer electrolyte fuel cells. The fuzzy inference system facilitates simplified connections of the complex relationships between numerous operating conditions and subsequent degradation modes. The approach utilises the most important operating parameters for diagnosis of high priority degradation modes using multiple health sensors. The developed fuzzy inference system classifies the fuel cell input data into simple linguistic categories for example ‘cell voltage is very high’ or ‘stack temperature is low’ through a fuzzification process. Based on a set of antecedent-consequent (if-then) rules, an inference calculation is performed without necessity for complex mathematical models. This enables a fast diagnosis with fuel cell parameters classified on a scale of inclusion to the linguistic categories. The linguistic classification of a degradation mode is converted back into a numerical value through a defuzzification process. The output data can be used to inform the user on the fuel cell state of health. The investigation has focused on the diagnosis of MEA degradation as it has been identified as having critical impact on fuel cell performance and lifetime. A single cell with a 25cm2 active area was used for testing under numerous moderate to extreme operating conditions known to cause membrane and electro-catalyst degradation. A database of if-then rules was initially developed based on knowledge in the literature and refined with experimental testing. Results so far have supported validation of the fuzzy inference system membership functions and the rule base for diagnosing the consequential degradation modes based on fuel cell operating conditions. This diagnostic and health management approach facilitates proactive decision making for mitigation strategies to be employed according to performance or lifetime targets and can increase fuel cell availability and lifetime therefore improving the overall value of the system.</div

Enhancing fuel cell lifetime performance through effective health management

Hydrogen fuel cells, and notably the polymer electrolyte fuel cell (PEFC), present an important opportunity to reduce greenhouse gas emissions within a range of sectors of society, particularly for transportation and portable products. Despite several decades of research and development, there exist three main hurdles to full commercialisation; namely infrastructure, costs, and durability. This thesis considers the latter of these. The lifetime target for an automotive fuel cell power plant is to survive 5000 hours of usage before significant performance loss; current demonstration projects have only accomplished half of this target, often due to PEFC stack component degradation. Health management techniques have been identified as an opportunity to overcome the durability limitations. By monitoring the PEFC for faulty operation, it is hoped that control actions can be made to restore or maintain performance, and achieve the desired lifetime durability. This thesis presents fault detection and diagnosis approaches with the goal of isolating a range of component degradation modes from within the PEFC construction. Fault detection is achieved through residual analysis against an electrochemical model of healthy stack condition. An expert knowledge-based diagnostic approach is developed for fault isolation. This analysis is enabled through fuzzy logic calculations, which allows for computational reasoning against linguistic terminology and expert understanding of degradation phenomena. An experimental test bench has been utilised to test the health management processes, and demonstrate functionality. Through different steady-state and dynamic loading conditions, including a simulation of automotive application, diagnosis results can be observed for PEFC degradation cases. This research contributes to the areas of reliability analysis and health management of PEFC fuel cells. Established PEFC models have been updated to represent more accurately an application PEFC. The fuzzy logic knowledge-based diagnostic is the greatest novel contribution, with no examples of this application in the literature

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

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

Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications

PHM of Proton-Exchange Membrane Fuel Cells - A review.

International audienceFuel Cell (FC) systems are promising power-generation sources that are more and more presented as a good alternative to current energy converters such as internal combustion engines. They suffer however from insufficient durability for stationary and transport applications, and lifetime may be improved. A greater understanding of underlying wearing processes is needed in order to improve this technology. However, FCs are in essence multi-physics and multi-scales systems (from the cells to the whole power system), which makes a modeling step of behaviors and degradation very difficult, even impossible. Thereby, data-driven Prognostic and Health Management (PHM) principles (as defined in condition-basedmaintenance scheme CBM) appear to be of great interest to face with the problems of health assessment and life prediction of FCs. According to all this, the aim of this paper is to present the current state of the art on PHM for FCs. Developments emphasize on PHM of the Proton-Exchange Membrane Fuel Cells (PEMFC) stack. The paper is organized so that important aspects like "behavior and losses FCs", "observation techniques", and "advanced PHM techniques" are addressed. Also, a taxonomy of existing works on PHM of PEMFC is given accordingly to the processing layers of CBM. The whole enables PHM practitioners as well as FCs experts to get a better understanding of remaining challenging issues

Expert diagnosis of polymer electrolyte fuel cells

Diagnosing faulty conditions of engineering systems is a highly desirable process within control structures, such that control systems may operate effectively and degrading operational states may be mitigated. The goal herein is to enhance lifetime performance and extend system availability. Difficulty arises in developing a mathematical model which can describe all working and failure modes of complex systems. However the expert's knowledge of correct and faulty operation is powerful for detecting degradation, and such knowledge can be represented through fuzzy logic. This paper presents a diagnostic system based on fuzzy logic and expert knowledge, attained from experts and experimental findings. The diagnosis is applied specifically to degradation modes in a polymer electrolyte fuel cell. The defined rules produced for the fuzzy logic model connect observed operational modes and symptoms to component degradation. The diagnosis is then tested against common automotive stress conditions to assess functionality

Recent advances in acoustic diagnostics for electrochemical power systems

Over the last decade, acoustic methods, such as acoustic emission and ultrasonic testing, have been increasingly deployed for process diagnostics and health monitoring of electrochemical power devices including batteries, fuel cells, and water electrolysers. These acoustic are non-invasive, highly sensitive, and low cost, while also providing a high level of spatial and temporal resolution, and practicality. The application of these tools in electrochemical devices is based on identifying changes in acoustic signals due to physical, structural, and electrochemical properties change within the material which are then correlated to critical processes and the health status of the devices. This review discusses recent progress in the use of acoustic methods for process and health-monitoring of major electrochemical energy conversion and storage devices. First, the fundamental concepts and principles of acoustic emission and ultrasonic testing are introduced, followed by a discussion of the range of electrochemical energy conversion and storage systems, and how acoustic techniques are being used to study relevant materials and devices. Conclusions and future perspectives highlighting some of the unique challenges and potential commercial and academic applications of the devices are also discussed. It is expected that, with further developments, acoustic techniques will form a key part of the suite of diagnostic techniques routinely used to monitor electrochemical devices across various processes including fabrication, on-board maintenance, post-mortem examination and second life or recycle decision support to aid the deployment of these devices in increasingly demanding applications

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