PEM fuel cell fault diagnosis via a hybrid methodology based on fuzzy and pattern recognition techniques

© IFAC 2014. This work is posted here by permission of IFAC for your personal use. Not for distribution. The original version was published in ifac-papersonline.netIn this work, a fault diagnosis methodology termed VisualBlock-Fuzzy Inductive Reasoning, i.e. VisualBlock-FIR, based on fuzzy and pattern recognition approaches is presented and applied to PEM fuel cell power systems. The innovation of this methodology is based on the hybridization of an artificial intelligence methodology that combines fuzzy approaches with well known pattern recognition techniques. To illustrate the potentiality of VisualBlock-FIR, a non-linear fuel cell simulator that has been proposed in the literature is employed. This simulator includes a set of five fault scenarios with some of the most frequent faults in fuel cell systems. The fault detection and identification results obtained for these scenarios are presented in this paper. It is remarkable that the proposed methodology compares favorably to the model-based methodology based on computing residuals while detecting and identifying all the proposed faults much more rapidly. Moreover, the robustness of the hybrid fault diagnosis methodology is also studied, showing good behavior even with a level of noise of 20 dB.Peer ReviewedPostprint (published version

Advanced reliability analysis of polymer electrolyte membrane fuel cells in automotive applications

Hydrogen fuel cells have the potential to dramatically reduce emissions from the energy sector, particularly when integrated into an automotive application. However, there are three main hurdles to the commercialisation of this promising technology; one of which is reliability. Cur- rent standards require an automotive fuel cell to last around 5000 h of operation (equivalent to around 150,000 miles), which has proven difficult to achieve to date. This hurdle can be overcome through in-depth reliability analysis including techniques such as Failure Mode and Effect Analysis (FMEA), Fault Tree Analysis (FTA) and Petri-net simulation. This research has found that the reliability field regarding hydrogen fuel cells is still in its infancy, and needs development, if the current standards are to be achieved. In this research, a detailed reliability study of a Polymer Electrolyte Membrane Fuel Cell (PEMFC) is undertaken. The results of which are a qualitative and quantitative analysis of a PEMFC. The FMEA and FTA are the most up to date assessments of failure in fuel cells developed using a comprehensive literature review and expert opinion. Advanced modelling of fuel cell degradation logic was developed using Petri-net modelling techniques. 20 failure modules were identfied that represented the interactions of all failure modes and operational parameters in a PEMFC. Petri-net simulation was used to overcome key pitfalls observed in FTA to provide a verfied degradation model of a PEMFC in an automotive application, undergoing a specific drive cycle, however any drive cycle can be input to this model. Overall results show that the modeled fuel cell's lifetime would reach 34 hours before falling below the industry standard degradation rate of more than 5%. The degradation model has the capability to simulate fuel cell degradation under any drive cycle and with any operating parameters. A fuel cell test rig was also developed that was used to verify the simulated degradation. The rig is capable of testing single cells or stacks from 0-470W power. The results from the verification experimentation agreed strongly with the degradation model, giving confidence in the accuracy of the developed Petri-net degradation model. This research contributes greatly to the field of reliability of PEMFCs through the most up-to-date and comprehensive FMEA and FTA presented. Additionally, a degradation model based upon Petri-nets is the first degradation model to encompass a 1D performance model to predict fuel cell life time under specific drive cycles

A Bayesian approach to fault identification in the presence of multi-component degradation

Fault diagnosis typically consists of fault detection, isolation and identification. Fault detection and isolation determine the presence of a fault in a system and the location of the fault. Fault identification then aims at determining the severity level of the fault. In a practical sense, a fault is a conditional interruption of the system ability to achieve a required function under specified operating condition; degradation is the deviation of one or more characteristic parameters of the component from acceptable conditions and is often a main cause for fault generation. A fault occurs when the degradation exceeds an allowable threshold. From the point a new aircraft takes off for the first time all of its components start to degrade, and yet in almost all studies it is presumed that we can identify a single fault in isolation, i.e. without considering multi-component degradation in the system. This paper proposes a probabilistic framework to identify a single fault in an aircraft fuel system with consideration of multi-component degradation. Based on the conditional probabilities of sensor readings for a specific fault, a Bayesian method is presented to integrate distributed sensory information and calculate the likelihood of all possible fault severity levels. The proposed framework is implemented on an experimental aircraft fuel rig which illustrates the applicability of the proposed method

Modelling polymer electrolyte membrane fuel cells for dynamic reliability assessment

Tackling climate change is arguably the biggest challenge humanity faces in the 21st century. Rising average global temperatures threaten to destabilize the fragile ecosystem of the Earth and bring unprecedented changes to human lives if nothing is done to prevent it. This phenomenon is caused by the anthropogenic greenhouse effect due to the increasing atmospheric concentrations of carbon dioxide (CO2). One way to avert the disaster is to drastically reduce the consumption of fossil fuels in all spheres of human activities, including transportation. To do this, research and development of electric vehicles (EVs) to make them more efficient, reliable and accessible is essential. [Continues.

Application of AI in Chemical Engineering

A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields

Performance analysis and dynamics of innovative SOFC hybrid systems based on turbocharger-derived machinery

La crescente consapevolezza su temi quali il cambiamento climatico e l\u2019inquinamento atmosferico ha portato a politiche nazionali ed internazionali mirate allo sviluppo di sistemi energetici innovativi e sostenibili. Tra di essi, le fuel cell sono uno dei pi\uf9 promettenti, essendo caratterizzate da alte efficienze e basse emissioni. In particolare, i sistemi ibridi basati sull\u2019integrazione di fuel cell ad alta temperatura con dispositivi derivati da turbocompressori hanno attirato l\u2019attenzione del mondo accademico e dell\u2019industria negli ultimi decenni. Tuttavia, la complessit\ue0, la fragilit\ue0 e l\u2019alto costo di questi impianti ha rallentato il loro sviluppo, e solo poche grandi aziende sono state in grado di realizzare prototipi completi. Le difficolt\ue0 tecniche affrontate dalla comunit\ue0 scientifica hanno messo in luce l\u2019importanza delle simulazioni per progettare, testare, controllare e analizzare i sistemi ibridi a fuel cell. Sulla base di tale esperienza, questa tesi mira ad espandere la attuale conoscenza sui sistemi ibridi a fuel cell a ossidi solidi, ponendo una particolare attenzione su un innovativo sistema turbocompresso di piccola taglia, alimentato con biogas e recentemente introdotto all\u2019interno del progetto europeo Bio-HyPP. Lo scopo principale della tesi \ue8 determinare se questo tipo di sistema possa essere una valida alternativa ai sistemi basati su microturbine a gas, analizzando il suo comportamento in relazione a diversi scenari, sia stazionari, sia transitori. Per fare ci\uf2, \ue8 necessario definire i vincoli operativi del sistema e sviluppare un sistema di controllo in grado di rispettarli, ottimizzando al tempo stesso le prestazioni dell\u2019impianto. Inoltre, l\u2019affidabilit\ue0 dei sistemi ibridi pu\uf2 essere migliorata grazie all\u2019implementazione di strumenti diagnostici e di procedure per prevenire il pompaggio del compressore. La parte finale della tesi \ue8 mirata allo studio di tali strumenti, al loro sviluppo e alla loro integrazione con il sistema di controllo. Tutte le attivit\ue0 presentate in questa tesi sono state svolte facendo affidamento su strumenti di simulazione. Ci\uf2 \ue8 stato possibile grazie alla collaborazione tra il Laboratorio di Matematica Applicata, Simulazione e Modellistica Matematica e il Thermochemical Power Group dell\u2019Universit\ue0 degli Studi di Genova. Dopo aver presentato il layout del sistema a fuel cell con turbocompressore, un dettagliato modello stazionario dell\u2019impianto sviluppato in Matlab\uae-Simulink\uae \ue8 stato utilizzato per progettare una strategia, basata sul controllo di valvole installate sull\u2019impianto, in grado di rispettare tutti i suoi vincoli operativi. Successivamente, \ue8 stata svolta un\u2019analisi di prestazioni in off-design, considerando allo stesso tempo diverse condizioni di carico di potenza e di temperatura ambiente. Tale analisi \ue8 stata utilizzata per confermare l\u2019efficacia della strategia di controllo proposta, e per valutare le capacit\ue0 del sistema con turbocompressore. Successivamente \ue8 stato creato un modello dinamico utilizzando lo strumento TRANSEO, in modo da studiare il comportamento del sistema durante i transitori. Avendo adottato una strategia di controllo basata sulla valvola di cold bypass, \ue8 stata analizzata la risposta del sistema ad una sua apertura a gradino, al fine di progettare un sistema di controllo efficace e reattivo, in grado di mantenere la massima temperatura di cella costante e, allo stesso tempo, di rispettare i vincoli del sistema. Sono stati progettati quattro diversi controllori, che successivamente sono stati testati su due diversi scenari di variazione di carico e confrontati sulla base di vari parametri operativi. La parte finale della tesi ha riguardato lo sviluppo di innovativi strumenti che possano aumentare l\u2019affidabilit\ue0 dei sistemi ibridi a fuel cell a ossidi solidi, in particolare tecniche di prevenzione del pompaggio e sistemi di diagnostica basati su reti Bayesiane. Un modello semplificato del sistema con turbocompressore \ue8 stato sviluppato in TRANSEO e sono state testate diverse tecniche di prevenzione del pompaggio: condizionamento del flusso d\u2019aria, iniezione di acqua, ricircolo e bleed, installazione di un eiettore all\u2019imbocco del compressore. Le soluzioni pi\uf9 efficaci sono state integrate con il controllore del sistema ibrido e sono state testate durante un transitorio per evitare che il punto operativo del compressore si avvicinasse al pompaggio. Infine, grazie ad una collaborazione tra l\u2019Universit\ue0 degli Studi di Genova e la M\ue4lardalens H\uf6gskola di V\ue4ster\ue5s, in Svezia, sono state sviluppate delle reti Bayesiane per la diagnostica di sistemi ibridi a fuel cell a ossidi solidi con microturbina a gas. Questa attivit\ue0 \ue8 stata svolta simulando il sistema su Matlab\uae-Simulink\uae e creando le reti Bayesiane su Hugin Expert. Due sistemi di diagnostica, uno per la microturbina e uno per la fuel cell, sono stati sviluppati e testati in condizioni stazionarie. Il secondo \ue8 stato anche testato in condizioni dinamiche e integrato con il sistema di controllo per prevenire l\u2019usura della cella. In conclusione, questa tesi ha messo in luce il grande potenziale dei sistemi ibridi SOFC-turbocompressore, mostrando la loro alta efficienza in un ampio intervallo di condizioni operative in termini di carico elettrico e temperatura ambiente. La tesi ha anche dimostrato che \ue8 possibile garantire il corretto funzionamento di questi sistemi durante diversi scenari transitori, implementando controllori a cascata progettati per agire sulla valvola di bypass freddo per controllare la massima temperatura della cella. Per quanto riguarda la possibilit\ue0 di migliorare l\u2019affidabilit\ue0 di tali sistemi, le tecniche basate sul ricircolo del compressore sono risultate essere le pi\uf9 efficaci per allontanare il sistema da una condizione di pompaggio. I risultati delle simulazioni mostrano come la loro integrazione con strumenti di monitoraggio possa prevenire diverse situazioni di pericolo. La parte finale della tesi ha mostrato come il deterioramento dei sistemi ibridi a SOFC possa essere limitato grazie a reti Bayesiane, che sono state utilizzate per diagnosticare accuratamente le condizioni di un sistema SOFC-microturbina a gas, ma potrebbero ugualmente essere applicate su impianti con turbocompressore.The growing awareness on climate change and pollution has brought to national and international policies aimed at promoting the development of innovative and environmentally sustainable energy systems. Among these systems, fuel cells are one of the most promising technologies, characterized by high energy conversion efficiencies and low emissions. In particular, hybrid systems based on the integration of a high temperature fuel cell with turbocharger-derived machinery have drawn the interest of academia and industry over the past decades. However, the complexity, fragility and high cost of these plants have slowed down their development, and only a few big companies were able to build complete prototypes. The technological challenges faced by the scientific community have highlighted the importance of simulations to design, test, control and analyse fuel cell hybrid systems. Based on this experience, this thesis wants to expand the current knowledge on solid oxide fuel cell hybrid systems, with a particular focus on an innovative small-scale biofueled turbocharged layout, which was introduced recently within the Bio-HyPP European project. The main goal of this thesis is to determine if this kind of system can be a viable alternative to micro gas turbine-based systems, analysing its steady-state and transient behaviour in various operating conditions. To do this, it is necessary to define the system operative constraints, and to develop a control system capable of ensuring their compliance, while optimizing the plant performance. The possibility of increasing the reliability of solid oxide fuel cell hybrid systems is finally investigated, considering the implementation of surge prevention techniques and diagnostic tools. All these activities strongly relying on simulation tools. This was possible thanks to the collaboration between the Laboratory of Applied Mathematics, Simulation and Mathematical Modelling with the Thermochemical Power Group of the University of Genoa. After introducing the layout of the turbocharged fuel cell system, a detailed steady-state model of the plant is developed in Matlab\uae-Simulink\uae and used to design a strategy, based on the control of valves installed on the plant, able to comply with its many operative constraints. Then, an off-design performance analysis of the system is performed, considering simultaneously various conditions of power load and ambient temperature. This analysis is used to confirm the effectiveness of the proposed control strategy and to assess the capabilities of the turbocharged system. A dynamic model is created using the TRANSEO tool to study the transient behaviour of the system. Having adopted a control strategy based on the cold bypass valve, the response of the system to a valve opening step change is analysed in order to design an effective and responsive control system, able to keep the fuel cell maximum temperature constant while complying with the system constraints. Four different controllers are designed, tested on two different load variation scenarios and compared on the basis of many parameters. The final part of the thesis regards the development of innovative tools aimed at improving the reliability of solid oxide fuel cell hybrid system, in particular surge prevention techniques and Bayesian belief network-based diagnosis systems. A simplified dynamic model of the turbocharged SOFC system is developed in TRANSEO, and various surge prevention techniques are tested on it: intake air conditioning, water spray at compressor inlet, air bleed and recirculation, and installation of an ejector at the compressor intake. The most effective procedures are integrated with the controller of the hybrid system and tested during a transient scenario to prevent the compressor operative point from approaching a surge condition. Bayesian belief networks aimed at diagnosing the status of SOFC hybrid systems are developed thanks to a collaboration between the University of Genoa and the M\ue4lardalens H\uf6gskola of V\ue4ster\ue5s, Sweden. A micro gas turbine \u2013 solid oxide fuel cell system is considered for this study, but the methodology could be easily extended to turbocharged plants. The activity is carried out simulating the system on Matlab\uae-Simulink\uae and designing the Bayesian networks on Hugin Expert. Two different diagnosis systems, one for the turbomachinery and one for the fuel cell stack, are developed and tested on stationary conditions. The second one is also tested during transients and integrated with the control system to prevent degradation of the fuel cells. In conclusion, this thesis highlighted the great potential of turbocharged SOFC hybrid systems, showing high energy conversion efficiencies in a wide operative range in terms of load and ambient conditions. It also showed that the proper operation of the system is possible during various transient scenarios, implementing cascade controllers designed to act on a cold bypass valve to control the SOFC maximum temperature. Regarding the possibility of improving the reliability of these systems, surge prevention techniques based on compressor recirculation appeared as the most effective ones. Simulation results suggest that their integration with a surge precursors detection tool could avoid the occurrence of many potentially dangerous scenarios. The final part of this thesis showed that the durability of SOFC hybrid systems could be further improved thanks to Bayesian belief networks, which were proved to effectively diagnose the status of SOFC-MGT systems but could be applied to turbocharged plants as well

Dynamic Reliability Assessment of PEM Fuel Cell Systems

In this paper, a novel model for the dynamic reliability analysis of a polymer electrolyte membrane fuel cell system is developed to account for multi-state dynamics and ageing. The modelling approach involves the combination of physical and stochastic sub-models with shared variables. The physical model consists of deterministic calculations of the system state described by variables such as temperature, pressure, mass flow rates and voltage output. Additionally, estimated component degradation rates are also taken into account. The non-deterministic model is implemented with stochastic Petri nets which model the failures of the balance of plant components within the fuel cell system. Using this approach, the effects of the operating conditions on the reliability of the system were investigated. Monte Carlo simulations of the process highlighted a clear influence of both purging and load cycles on the longevity of the fuel cell system

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Electrochemical Impedance Spectroscopy for the on-board diagnosis of PEMFC via on-line identification of Equivalent Circuit Model parameters

2012 - 2013Proton Exchange Membrane, also named Polymer Electrolyte Membrane fuel cells (PEMFC) are interesting devices for energy conversion. Their development is due to the high efficiency, acceptable power density, quick start-up and good environmental compatibility. On the other hand, reliability cost and durability are the main challenges for PEM fuel cell commercialization. In 2010 the American Department of Energy (DoE) sets a target of 40000 hours for stationary and 5000 hours for automotive applications, respectively. Actually, these standards are considered as the mainly reference in fuel cell research. Based on electro-catalytic reactions, the PEMFC operation is influenced by system functioning conditions. In case of system operation in abnormal conditions several chemical, mechanical and thermal degradation mechanisms could take place inside the cell. Among other, improper water, thermal and gas managements can introduce a cell voltage drop, thus reducing the system performance. A long-term exposure to these phenomena causes the PEMFC lifetime reduction. Thus, a good system management is one of the primary targets to ensure suitable PEMFC durability. For this purpose, research activities are oriented towards the development of newest advanced monitoring and diagnostic algorithms. The primary goal is monitoring the system operation ensuring a correct system control. Moreover, the diagnostic tool (i.e. both algorithm and sensors) allows the detection of system component malfunctioning; it can isolate one or more faults that may have occurred causing the abnormal behaviour of the system operation...[edited by author]XII n.s

Contribuciones al modelado y diagnóstico de fallos en PEMFC para mejorar la fiabilidad en sistemas híbridos renovables

[ES] Las pilas de combustibles son dispositivos de un coste elevado y frágiles ante ambientes contaminados o condiciones inadecuadas de operación como: temperaturas extremas o mala gestión del agua producida como residuo de la pila. Para mejorar la fiabilidad de una pila de combustible es necesario diagnosticar de una manera oportuna los fallos y así evitar daños que reduzcan el desempeño del módulo o que lo inhabiliten. Este trabajo busca contribuir al mejoramiento de la fiabilidad de las pilas de combustible de baja temperatura y de esta forma favorecer el uso de hidrógeno en la transición a una energía descarbonizada. Para lograrlo, se realizaron tres actividades principales: modelado de una pila de hidrógeno, ajuste paramétrico del modelo desarrollado y, por último, aplicación de técnicas de diagnóstico de fallos basados en modelos. En el laboratorio de Recursos Energéticos Renovables Distribuidos LabDER de la Universitat Politècnica de València, se estudia el desempeño de sistemas híbridos renovables, incluyendo una línea de hidrógeno, desde la producción, almacenamiento y reconversión en electricidad en una pila de combustible, por tanto, se ha podido validar el modelo. En un primer momento se identificó la necesidad de un modelo que emplee la temperatura como señal de salida y que retroalimente el sistema, y que tuviese en cuenta señales propias del módulo comercial; sin embargo, el uso de la temperatura como señal y la no linealidad de las ecuaciones físicas, químicas, eléctricas y empleadas, generan un modelo altamente complejo. El ajuste paramétrico del modelo se realizó empleando algoritmos de optimización. Tomando como base al algoritmo de Enjambre de Partículas, se desarrolló una nueva propuesta llamada Scout GA, este algoritmo fue utilizado en otras aplicaciones y pruebas de convergencia para verificar su desempeño frente al fenómeno de estancamiento prematuro y logrando mejorar la precisión y velocidad de convergencia de otras propuestas. Como resultado de la validación de este modelo, en una primera simulación usando datos reales de funcionamiento correspondientes a 1500 segundos, el error de simulación fue del 2,21% en la señal de tensión y del 1,97% en la señal de temperatura, obteniendo un error medio del 2,09%. En un segundo conjunto de datos de algo más de 2.500 segundos de funcionamiento, el error de simulación fue del 2,40% y del 1,96% para las señales de tensión y temperatura, respectivamente. Se estima que el error medio de simulación para ambas señales y condiciones de funcionamiento similares es inferior al 2,5%. Buscando mejorar la fiabilidad de la pila, se realizó el trabajo de diagnóstico de fallos, este partió de la simulación de fallos, mediante la modificación de algunas señales de entrada del modelo, los fallos se caracterizaron mediante el tratamiento estadístico de 12 residuos, obteniendo firmas de fallos, que, en su conjunto, formaron una matriz de fallos. Luego, un algoritmo de diagnóstico propuesto permitió identificar y aislar 14 fallos. permitiendo concluir que, el modelo predice eficazmente los fallos de las pilas PEMFC y podría extrapolarse a otras pilas de combustible.[CA] Les piles de combustibles són dispositius d'un cost elevat i fràgils davant ambients contaminats o condicions inadequades d'operació com: temperatures extremes o dolenta gestió de l'aigua produïda com a residu de la pila. Per a millorar la fiabilitat d'una pila de combustible és necessari diagnosticar d'una manera oportuna les fallades i així evitar danys que reduïsquen l'acompliment del mòdul o que l'inhabiliten. Este treball busca contribuir al millorament de la fiabilitat de les piles de combustible de baixa temperatura i d'esta manera afavorir l'ús d'hidrogen en la transició a una energia *descarbonizada. Per a aconseguir-ho, es van realitzar tres activitats principals: modelatge d'una pila d'hidrogen, ajust paramètric del model desenvolupat i, finalment, aplicació de tècniques de diagnòstic de fallades basades en models. En el laboratori de Recursos Energètics Renovables Distribuïts *LabDER de la Universitat Politècnica de València, s'estudia l'acompliment de sistemes híbrids renovables, incloent-hi una línia d'hidrogen, des de la producció, emmagatzematge i reconversió en electricitat en una pila de combustible, per tant, s'ha pogut validar el model. En un primer moment es va identificar la necessitat d'un model que empre la temperatura com a senyal d'eixida i que retroalimente el sistema, i que tinguera en compte senyals propis del mòdul comercial, no obstant això, l'ús de la temperatura i la no linealitat de les equacions físiques, químiques, elèctriques i tèrmiques empleades, deriven en un model altament complex. L'ajust paramètric del model de pila de combustible es va realitzar emprant algorismes d'optimització. Prenent com a base a l'algorisme d'Eixam de Partícules, es va desenvolupar una nova proposta anomenada Scout GA, aquest algorisme va ser utilitzat en altres aplicacions i proves de convergència per a verificar el seu acompliment enfront del fenomen d'estancament prematur i aconseguint millorar la precisió i velocitat de convergència d'altres propostes. La simulació i identificació del model té un cost computacional entre 7 i 20 ms per iteració, on es van aconseguir errors de simulació menors al 2.5% Com a resultat de la validació d'aquest model, en una primera simulació usant dades reals de funcionament corresponents a 1500 segons, l'error de simulació va ser del 2,21% en el senyal de tensió, del 1,97% en el senyal de temperatura i un error mitjà del 2,09%. En un segon conjunt de dades d'una mica més de 2.500 segons de funcionament, l'error de simulació va ser del 2,40% i del 1,96% per als senyals de tensió i temperatura, respectivament. S'estima que l'error mitjà de simulació per a tots dos senyals i condicions de funcionament similars és inferior al 2,5%. Buscant millorar la fiabilitat de la pila, es va fer el treball de diagnòstic de fallades, aquest va partir de la simulació de fallades, mitjançant la modificació d'alguns senyals d'entrada del model, les fallades es van caracteritzar mitjançant el tractament estadístic de 12 residus, obtenint signatures de fallades, que en el seu conjunt, van formar una matriu de fallades. després un algorisme de diagnòstic proposat, va permetre identificar i aïllar 14 fallades. Permetent concloure que, el model prediu eficaçment les fallades de les piles PEMFC i podria extrapolar-se a altres piles de combustible.[EN] Fuel cells are high-cost devices that are fragile in contaminated environments or in inadequate operating conditions, such as extreme temperatures or poor water management, produced as battery waste. To improve the reliability of a fuel cell, it is necessary to diagnose failures promptly and thus avoid damage that reduces the module's performance or disables it. This work seeks to contribute to improving the reliability of low-temperature fuel cells and thus promote the use of hydrogen in the transition to decarbonized energy. To achieve this, three main activities were carried out: modeling a hydrogen fuel cell, parametric adjustment of the developed model, and application of model-based fault diagnosis techniques. In the LabDER Distributed Renewable Energy Resources laboratory of the Polytechnic University of Valencia, the performance of renewable hybrid systems is studied, including a hydrogen line, from production, storage, and reconversion into electricity in a fuel cell, therefore, has been able to validate the model. Initially, a fuel cell model that uses temperature as an in/output signal is required. Also, the model must be able to use the reals signals supplied for the commercial module. However, using temperature and an equation set that includes the non-linearity of the physical, chemical, electrical, and thermal equations resulted in a highly complex model. The parametric adjustment of the fuel cell model was performed using optimization algorithms. Based on the Particle Swarm algorithm, a new proposal called Scout GA was developed. This algorithm was used in other applications and convergence tests to verify its performance against the premature stagnation phenomenon and improved the accuracy and speed of convergence of other proposals. The simulation and identification of the model have a computational cost between 7 and 20 ms per iteration, where simulation errors of less than 2.5% were achieved. As a result of the validation of this model, in a first simulation using real operating data corresponding to 1,500 seconds, the simulation error was 2.21% for the voltage signal, 1.97% for the temperature signal, and an average error of 2.09%. In a second data set for slightly more than 2500 seconds of operation, the simulation error was 2.40% and 1.96% for the voltage and temperature signals, respectively. The average simulation error for both signals and similar operating conditions is estimated to be less than 2.5%. To improve the reliability of the stack, the fault diagnosis work was carried out, starting from the simulation of faults by modifying some input signals of the model; the faults were characterized by the statistical treatment of 12 residuals, obtaining fault signatures, which formed a fault matrix. Then, a proposed diagnostic algorithm allowed to identify and isolate 14 faults. Allowing to conclude that the model effectively predicts the PEMFC stack faults and could be extrapolated to other fuel cells.Ariza Chacón, HE. (2024). Contribuciones al modelado y diagnóstico de fallos en PEMFC para mejorar la fiabilidad en sistemas híbridos renovables [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/20361