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Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system
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Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system A. Benmouna a,*, M. Becherif a, D. Depernet a, F. Gustin b, H.S. Ramadan a,c, S. Fukuhara b a
FCLAB FR CNRS 3539, FEMTO-ST UMR CNRS 6174, Univ. Bourgogne Franche-Compte/UTBM, France FCLAB FR CNRS 3539, FEMTO-ST Univ. Bourgogne Franche-Comte/Univ. Franche-Comte, France c Zagazig University, Faculty of Engineering, 44519 Zagazig, Egypt b
article info
abstract
Article history:
In the field of alternative electrical power generation, the durability of the energy presents
Received 23 February 2016
a great challenge to science. Fuel Cells (FCs) are considered as one of the future promising
Received in revised form
energy sources. The Proton Exchange Membrane (PEM) is more suitable for implementation
9 July 2016
into daily-use applications. To bring FCs into the market, such technology should have an
Accepted 21 July 2016
adequate operation reliability, sufficient lifetime and acceptable cost. Therefore, different
Available online xxx
problems associated with the faults of FC various components influence the reliability of the system. The issues can be successfully overcome using the diagnostic tools as part of
Keywords:
the advanced control system. In this paper, after a short presentation of the FC and its
Proton Exchange Membrane Fuel
components, the paper emphasizes different faults that may occur. Using the literature,
Cell
the root causes of these faults are identified. From this point, a state of the art on the recent
Fuel cell stack system
studies concerning the fault diagnosis method is presented. In order to illustrate the state
Fuel cell faults
of the art, a brief experimental study of the impact of anode flooding is given and a signal-
Fault diagnosis methods
based diagnostic method is suggested.
Model based method
© 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Non-model based method
Introduction Recently, the expected increasing of the global energy demand is foreseen to have a huge impact on the pollution rate and all the issues related to global warming. Therefore, it is crucial to develop the research on non-pollutant energy sources. Among different solutions, the use of hydrogen as an energy vector is a serious candidate to participate in the energy mix. To convert hydrogen into electricity, a Fuel Cell (FC) system is a natural system. Therefore, alternative energy source research issues become of great importance. One of the green energy
sources is the hydrogen energy. The FC system is an electrochemical device, which converts chemical energy of the fuel into electrical energy. Water and heat are also generated as only by-products during the FC operation. Therefore, the FC technology represents a promising energy source for future, owing to its free green-house gases produced and high conversion efficiency. There are several type of FC according to the employed electrolyte type. The Proton Exchange Membrane Fuel Cell (PEMFC) shows sufficient performances for everyday transportation and stationary applications up to 100 kW of power [1]. This FC type is used in wide range of applications, in regard of the low temperature of operation,
* Corresponding author. E-mail address: [email protected] (A. Benmouna). http://dx.doi.org/10.1016/j.ijhydene.2016.07.181 0360-3199/© 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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the ability to use oxygen from air, short start-up time and high power density. The successful FC introduction into daily applications should have a durable and reliable technology and a reduced cost of production, operation and maintenance [2,3]. According to the American study in [4], the US Department of Energy has announced a set of requirements for automotive applications to reach 5000 h FCs durability at a cost of $30/kW, and stationary FC systems of about 60 000 to 80 000-h durability at a cost of about $1000e1500/kW [4]. The necessity of this technology in the near horizon paves the way to the research community to focus mainly on the FC faults detection and the relevant different diagnosis methods in order to ensure durability and disponibility too. These faults must be detected early and sometimes needed to be estimated and accommodated. Therefore, FCs are vulnerable to faults that can cause its stop or permanent damage. To guarantee the safe operation of the FC systems, it is necessary to use systematic techniques to detect and isolate faults. This article presents a state of the art on the various methods of diagnostic faults of PEMFC. The focus is on the failure of the flooding of the dead-ended PEMFC because it is critical point for the performance of this technology. Thereafter, an experimental study that treats flooding problem at the anode chamber is presented. Therefore, the test bench behavior shown. The short-term impact of the anode flooding is presented in term of voltage versus time. In the experimental work, a difference in the harmonic content is used as the new method to detect the flooding fault.
Fuel cell system FCs are the devices discovered by William Grove in 1839. They are based on the electrochemical reaction that allows the electricity generation from hydrogen and oxygen of the air. These devices, that convert chemical energy, are ideal electrical power sources with either zero or lower emissions, clean, and high efficiency. The FC, as an attractive technology produces electricity, water, and heat only. Thereby, the pollution at the energy conversion can be eliminated. The FC is constructed like a sandwich constituted with an electrolyte between anode (negative electrode) and cathode (positive electrode) as shown in Fig. 1 [5]. According to the diverse parameters such as electrolytic, operating temperature and applications, different FC types can be distinguished: PEMFC, Solid Oxide Fuel Cell (SOFC), Molte Carbonate Acid Fuel Cell
Fig. 1 e Principle of fuel cell.
(MCFC), etc. In the study, PEMFC system that is one of the most promising alternatives to traditional systems and it represents an interesting and attractive application not only in the transport field but also in stationary and portable systems up to 200 kW [6]. This is due to the diverse characteristics such as higher power density, start-up ability, suitability for discontinuous operation and lower operating temperature compared to other types of FC [7e9]. In the last decade, the FC research has become of considerable interest for the diverse applications particularly in Electrical Vehicle (EV). The development of different diagnostic tools for FC systems become a must in order to ensure safety, security, and availability during faults. Consequently, these faults should be detected as early as possible. Although the studies on FCs comprises different disciplines, including materials science, transport phenomena, electro-chemistry and catalysis science, it is always a major challenge to deeply understand the thermodynamics, the fluid mechanics, the fuel cell dynamics and the inherent FC electrochemical processes. Furthermore, FC developed nowadays in industries do not manifest a perfectly constant behavior, still manifesting a certain variation in the model for any similar FCs produced at different times. For all these reasons, the energy generation systems based on fuel cell is complex. The FC system consists of four circuits of matter and energy, as depicted in Fig. 2, such as air, hydrogen, humidification and electrical circuit. Hydrogen valve is used for controlling the flow of gas H2. The air filter permits to eliminate solid particles like dust, molds and bacteria while the motor compressor favorites increasing of the air pressure by reducing its volume. The humidifier is a device that increases the moisture in the compressed air and allows filtering through the humidification circuit. The fluid manifold consists of one input and several outputs. It allows distributing the gas uniformly to guarantee the supply of fuel gas of each cell of the stack. The FC, the heart of the system, constitutes of several cells depending on the power. The cooling group includes electric fans. One is placed beside the compressor and the humidifier for cooling, and the other fan assures the stack low temperature in normal operating conditions [10].
Fuel cell faults Considering the pre-established optimal operating point, the FC systems need a set of auxiliary elements such as valves, compressor, sensors, controllers…etc. For this reason, they are vulnerable to faults that can cause the FC stop or permanent damage. These faults should be taken into account to guarantee the FC system safe operation. Furthermore, the use of the systematic techniques is required for fault diagnosis (detection and isolation of faults). In fact, the diagnostic tools allow distinguishing the structure-property-performance relationships between the FC and its components [11]. There are several FC faults, the most of them are cited in the literature as faults tree such illustrated in Fig. 3 [11,12]. Failures of hydrogen circuit may be either at the level of the hydrogen source due to blockage or leakage of hydrogen from the tank, or at the level of the hydrogen regulator valve,
Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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Fig. 2 e Fuel cell stack system (FCSS).
Fig. 3 e Fault trees of FCS system [12].
caused by the blockage or hydrogen leakage from the valve. The faults of air compressor failures are from diverse nature, namely electrical faults caused for example by short-circuit, over-voltage; mechanical faults principally due to the Crank shaft's stall; hydraulic faults caused by the decrease of compressibility effect. Moreover, the fault of the Controller's that due to its breakdown. Inside the FC, the water distribution is the principal parameter that affects the channel of membrane and the electrode and consequently FC performance is affected. Hence, the water available inside of the FC must be purged appropriately [13]. The accumulation of water in the channels can block the diffusion of gases. Thus, the FC operation may be disturbed, and may result in the FC flooding. The lack of water causes dehydration of the membrane. Therefore, the bad water management in the FC system causes the faults in Catalyst support oxidation, Catalyst agglomeration and migration [12]. Faults tree is an important architecture that allows identifying the various breakdowns that the FC may encounter. These defects cause the disturbance of the operation and the degradate this component,
which necessarily leads to the decrease in overall system performance [14,15]. According to [12], the fault tree could be completed using a fourth level that defines the principal causes of the detailed failures. Generally, the PEMFC flooding is linked into three parameters: current density, gas flow rate and temperature. At high current density operation, the water-produced rate is greater than the water quantity leaving the cathode. The increased water quantity inside the FC significantly depends on the operating condition's interactions particularly the low gas flow/temperature levels [16]. Moreover, the flooding can be caused at low current densities under low temperature and high gas relative humidity that result in gas phase saturation by water vapor [16]. On the opposite of the flooding phenomenon, unsuitable operating conditions may lead to drying faults. The high temperature accelerates the water evaporation, which produces the drying membrane. Therefore, the use of inlet dry gas, or with a low relative humidity, can result in undesired dry membrane. Dysfunction of humidity system decreases the
Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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relative humidity and the relevant drying. The produced current is an important parameter to be considered that affects the water produced quantity. Less water quantity at the cathode can be explained by lower current [17]. Concerning the FC poisoning due to H2 produced by a reforming process or air gas quality. Obviously, the gas contamination causes FC degradation [18]. The efficiency of the air compressor in FC system is estimated by its capability to supply the desired value of stoichiometry with an acceptable time constant regardless of the FC current variation. The faults at air compressor level have been explained in [12]. In electrical systems, the short circuit causes the change of the internal resistance of the compressor destroying the moto-compressor group. The stall of crack shaft and friction decrease mechanical and hydraulic efficiency of the moto-compressor. In the controller subsystem, the main fault cause is the failure of the regulator [12]. The hydrogen subsystem is of the utmost importance and its misbehavior may lead to dangerous consequences, such as application of a high pressure. Mishandling can cause the destruction of the valves, leading to hazardous operating conditions. As the hydrogen is the lightest molecule, its leakage is a critical issue as hydrogen is highly inflammable [19]. Several researchers are interested in the study of FCs tree faults. In [20] the authors have established a tree model of failure of the FC considering four types of failure symptoms: hardware, software failures besides the environmental and human factors. In [21], the degradation of PEMFC based on fault tree has been studied. This fault tree is a power representation to establish a deductive analysis, which assesses the internal state of the fuel cell and its lifetime. The authors in [22] have developed the fault tree in order to discriminate the different failures and to understand the origin of each fault and the relation between causes and symptoms. To understand in depth the reliability of the PEMFC and to improve modeling accuracy of this technology, the authors in [23] have proposed to use the fault tree of fuel cell. In the work [14], the authors have proposed using the fault tree because it helps to model clearly and intuitively the different causal relations of the degradation mechanisms and fault tree allows quantifying components specific degradations, and their effects on the global degradation of the cell. Based on the data extracted from the literature, the histogram shown in Fig. 4 presents the percentage of most common PEMFC faults. The water management, namely the flooding and drying that represent 33% and 19%, respectively. Followed by the durability, the leakage and other faults, that can be of CO poising, membrane degradation. Several studies have resorted to the transparent cell design based on optical diagnostics as demonstrated in Fig. 5 [2] in order to define the origin and the development of flood phenomena with high spatial and temporal accuracy in FC systems. The cell is constituted of two transparent acrylic cover plates and two flow field brass plates that are anode and cathode [24] have proposed on transparent PEMFC to study the distribution of water and its flooding inside the cathode gas channels. The system is used for explaining the phenomena of the membrane dehydration [25] have considered the same device for H2 fueled PEMFC in order to visualize CO2 gas bubble behavior in direct methanol FCs [26] have used a transparent cell to visualize the liquid water and ice formation during
Fig. 4 e The percentage of different FCs faults.
Fig. 5 e Transparent FC [2]. startup of the FC at subzero temperatures [27,28] have developed a number of transparent FCs with different flow fields each in order to study the water flood, besides the two-phase flow of the reactant, products and pressure drop in the cathode flow channels. In addition to the water, the FC temperature can affect on both the drying and the flooding faults. The third fault of part FC concerns gas contamination, where different contamination of feed gas by N2, CO or CO2 are possible. Because of the existence of parasitic reaction, different contamination of the feed gas can be detected. This case especially appears in stack systems, which use hydrogen produced from a fuel reformer or for systems operating in a polluted environment [11].
Fault diagnosis methods for FC systems The definition of fault diagnosis constitute several concepts such as: the detection of the occurrence of faults in the system, the localization of faults and the identification or analysis to provide the essential information of fault determination, such as: type, magnitude and causes,…etc. [15,29].
Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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The fault diagnosis approach is considered efficient if the FC system is monitored in continuous and automated manner. This allows reducing the system downtime and improving the system performance considering the maintenance cost. There are essentially two approaches of FC fault diagnostics as illustrated in Fig. 6: model based approaches and non-model based approaches.
Model based method This model is considered as an analytical approach that requires a comprehensive understanding of the cell and its inner phenomena. A series of key relations of the different natures (electrochemical, thermodynamic, thermal, electrical and fluidic) are necessary to develop this model. This approach is defined as an on-line comparison between the real behavior of the monitored systems obtained by means of sensors and the dynamic model of the same simulated system [6]. In case of detecting a significant discrepancy or residual between the model and the obtained sensor measurement, the existence of a fault is assumed. If a set of measurements is available, it is possible to generate a set of residuals (indicators) that present different sensitivities that correspond to the possible faults. The relive by analysing in real-time how the faults affect the residuals, it is possible in most cases to isolate the fault and to determine its magnitude [30]. These approaches are divided in two principal categories, as illustrated in Fig. 6, either is qualitative-or quantitativebased model. The former approach, it includes the abstraction hierarchy (functional and structural analysis), causal models (signed direct graphs [31], fault tree analysis [32], qualitative physics). However, the quantitative model based approaches include Analytical Redundancy Relations (ARR), observers, Kalman filters and statistic methods [22]. The performances of the quantitative model-based methods depend mainly on the model accuracy.
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The model of FC system is very complex. It contains nonlinearities and has interactions with several energy areas of different nature such as electrical, thermodynamic and electrochemical. The model based diagnostic techniques have been comprehensively discussed in [33] and [34]. In [29], six faults of interest in the PEMFC system have been diagnosed by the model based method: 1) the increase of the friction in the compressor motor, 2) its overheating, 3) the block of the channels in the diffusion layer of the FC, 4) the leakage in the air supply manifold, 5) the compressor motor control failure and 6) the stack temperature control failure. In order to characterize these faults, several variables have been used such as the oxygen excess ratio, the compressor's current density and its speed as well as the stack voltage. The sensitivity of the residual to the fault has been studied in order to distinguish the faults. A theoretical relative fault sensitivity matrix with the residual sensitivity in the row and the faults in columns has been considered for these fault types identification.
Non-model based method Concerning the non-model based methods, the availability of large amount of historical process data has been supposed in [15]. The objective of this method is to obtain fault information based on heuristic knowledge or signal processing or a combination of both [9]. In model based method, fault features, produced either by the residual generation or by relying on signal decomposition play a predominant role in the fault diagnosis task based on two sequential of faults procedures: detection and identification of the faults. However, in non-model approach, the diagnosis concept is different because these procedures are merged into a single diagnostic step by means of simulating human reasoning activities. This type of diagnosis refers to a decision-making process that can be automated by an expert system based on Artificial Intelligence (AI) technology [15].
Fig. 6 e Different fault diagnosis approaches for FCSS [9,12]. Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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Table 1 e The relevant recent studies on the PEMFC fault diagnosis. Diagnosis method Wavelet Transform (WT)
Fault Water management
Air stoichiometry
Residual method
Anodic flooding
Artificial Neural Network (ANN) ensemble method
Linked to the stack cooling system, Increasing of fuel crossover. Fault in air delivery system. Fault in hydrogen delivery system
Signal Processing (SP) method
Water management
Double fuzzy þ EIS
Water management
Pressure drop
Water management
EIS
Hydrogen leak, oxygen concentration
Infrared thermography þ a reactive flow
Catalyst loading defects
Neural networks
Air leakage, sensor fault, actuator fault
Description This WT has been employed to the PEMFC system reached the dysfunctions caused by inappropriate humidity level inside the cell, to diagnose the drying or flooding faults. The WT is used for detecting the defaults of the stoichiometry of air in the PEMFC. The energy contained in each detail of the wavelet decomposition is employed to diagnose the excess quantity of air in the system A difference in harmonic content is used as a new method to detect the flooding fault of a deadended PEMFC. The ANN ensemble method based on BP-ANN and the Lagrange multiplier method. A combination of four ANNs have been employed to detect four faults types: the stack cooling system, increasing of fuel crossover, fault in air delivery system, fault in hydrogen delivery system. The parameters characterizing these faults have been evolved as function of the time: voltage, current, temperature, airflow and H2, O2 pressures. The simulation results of this method has shown fault diagnosis accuracy between 75.24% and 85.62%. The SP method based on the empirical mode decomposition has been employed. Decomposing the output voltage of the FC has been used for detecting and isolating the fault flooding or drying. The SP provides high accuracy to diagnose the fault of poor management of water. The diagnostic methodology that concerns the faults of water management, discriminating different degrees of flooding or drying inside the FC system. The EIS has been used as the basis tool besides the fuzzy clustering and fuzzy logic. Through this method, five health states of the stack have been discriminated with easiness. The diagnosis has been performed but the faulty cell has not been located. A review of diagnosis method based on pressure drop has been introduced. This method has been considered as online water fault diagnosis. This method has been used for the diagnosis of the water fault because it has a significant variable which reflects the water content of the GDL or the flow channels. The identification of important parameters to detect the existence of the defect leakage inside the PEMFC has been studied. Among those parameters, the potential reverse fault has been cited which can be detected using EIS methods. The combination of infrared thermography together with the reactive flow for detecting catalyst loading defects in the PEMFC with high spatial and temporal resolutions has been considered. Experimental results and model prediction of thermal response has provided adequate agreement. The detection and isolation of faults using the neural network have been presented. Concerning different faults in the PEMFC stack: One component fault, one actuator fault and three sensor faults.
Reference Ibrahim et al. [41]
Pahon et al. [42]
Fukuhara et al. [43]
Shao et al. [44]
Damour et al. [45]
Zhixue et al. [46]
Pei et al. [47]
Ghassan et al. [48]
Das et al. [49]
Kamal et al. [10]
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Table 1 e (continued ) Diagnosis method Magnetic field measurement
Fault Membrane degradation
Description The approach is based on the control of the current density distribution inside the PEMFC. The proposed method has been used for diagnosing the failure of the membrane degradation of PEMFC due to long-term running with low stoichiometric air ratio.
This method is expected to be able, both to interpret realtime signals and to deliver the required control action, in order to recommend diagnosis procedures [35]. These methods have a adequate nonlinear approximation capability, and more computational efficiency [36]. In Fig. 6, the main non-model based approaches are illustrated: (i) the signal processing approaches: the magnetic resonance imaging, acoustic emission, magnetic field, neutron radiography. (ii) the AI based approaches: fuzzy logic, neural network, expert system; (iii) the experimental methods: voltage measurement, impedance spectroscopy, polarization curve interpretation, spatial current density distribution, pressure drop and gas chromatography. All these approaches are grouped into the non-model method type since there is no detailed mathematical modeling of all components and their interactions. It can be considered as a black box of inputeoutput behavior mentioned in this approach [35]. Several studies based on this method have been proposed in order to determine if the phenomena related to water management are acoustically active. Legros et al. [37] have used the acoustic emission technique. According to [38], the magnetic resonance imaging technique has been used for obtaining the liquid water distribution in the FC flow. In [39] the AI neural networks approach has been used for alleviating the task of on-board FC diagnostic.
Reference Hamaz et al. [50]
The main approach used in the AI is the fuzzy logic which needs a fuzzy model. In [40], a diagnosis-oriented model in Sugeno type of a FC power generator dedicated to automotive applications has been proposed. The Genetic Algorithm (GA) has been used for tuning of the fuzzy diagnosis model. Through this method, the accumulation of water and nitrogen in the anode compartment in case of a dead-end mode use of the FC has been diagnosed as well as the drying of the PEMFC localized by the configuration of threshold [40]. The chemical phenomena inside the FC can be studied with the electrochemical impedance spectroscopy by applying a very small amplitude of current signal over a wide range of frequency and to register the response. The ratio of the variation voltage and current gives the magnitude of the impedance and the phase shift. According to the literature, the majority of AC impedance studies of PEMFC involve in situ measurements due to the most PEMFC pertinent data [40]. To minimize the performance degradation of PEMFC, several studies have been performed the degradation mechanisms of the components and the different methods of diagnosing the appropriate FC system breakdown that must be detected, isolated and corrected to prevent serious damages overall system. Table 1 cites the relevant recent studies on the PEMFC fault diagnosis that have been treated:
Fig. 7 e Test bench. Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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From Table 1, the most critical fault in FC stacks is the poor water management that may cause flooding or drying of the system. Therefore, the experimental study will focus on one of this critical fault type, namely the flooding in dead end anode PEMFC.
An experimental case study: anode flooding of a dead-ended PEMFC An emphasis on the anode flood phenomenon is developed. Using the previous experimental observations, an idea of the new method for anode flooding diagnostic is introduced. First, the test bench is presented, the FC is an open cathode, dead end anode PEMFC from the H-500 Horizon FC series composed of 36 cells with a nominal active surface area of 57 cm2. The air is forced through two fans, which are used for cooling down the PEMFC. The test bench includes a DC/DC converter, a 12 V 6 batteries, a programmable load and sensors for flow rate, currents and voltages. All the measured quantities are acquired through a measuring/control device (CompactRIO of National Instrument®) which controls the current provided by the FC through the DC/DC converter. Fig. 7 describes how the components of the FCS are interconnected. As explained in [43], anode flooding has led to the carbon corrosion of the cathode catalytic support and decreasing irreversibly the electrical performances of the PEMFC. At short-term, the liquid water has prevented the fuel to access to the catalytic sites. Fig. 8 shows the short-term impact at static conditions over a period of time of 10 min. To mitigate these long and short terms degradation, it is necessary to detect the anode flooding. Fukuhara et al. [43] have shown that, during an anode flooding, the hydrogen inlet mass flow peaks appear during the purge process of the dead-ended anode tends to decrease drastically when compared to the healthy state. The main disadvantage of the work of Fukuhara et al. [43] was the establishment of a 0D model where two parameters are determined by ad-hoc experiments. The idea of the experimental part is to detect an anode flooding by designing threshold on the Fast-Fourier Transform (FFT) of the inlet mass flow. From Fig. 9, the difference in the harmonic content can be used as a new diagnostic method for anode flooding.
Fig. 9 e Frequency spectrum of the hydrogen inlet mass flow for healthy and flooding conditions.
Conclusion and perspectives This paper presents the basics of the fault diagnosis of PEMFC to improve its durability and reliability which are crucial issues to be widely commercialized. These failures are mainly due to the faults on different FC system components, each failure is presented in this paper as a fault tree. Two diagnostic methods are explained: the non-model based methods and the model based methods. The model based method require an accurate behavior model to allow the analysis and faults diagnosis. Therefore, concerning FC, it is necessary to understand different domains, such as: electricity, mechanic, thermodynamic, electrochemical. Moreover, the determination of the internal parameters of FC systems is difficult because it requires a large number of sensors or some parameter are nonobservable. All of these reasons explain the complexity and difficulty of using the model based method for fault diagnosis. For the non-model based approach, an experimental database is necessary for the fault diagnosis. The information of fault is directly extracted from database for its classification. The characteristic of the fault can be obtained by signal processing. The AI methods provided the efficiency solution for isolation of the fault and further analyses. In conclusion, the non-model based approach is more interesting for the fault diagnosis due to the less knowledge requirement of the system and its relative simplicity and efficiency for on-line fault diagnosis applications. From the last studies, it is remarkable that the critical fault in the PEMFC system is mainly the bad water management. For this, an experimental study is proposed which study the flooding fault of dead-ended anode PEMFC. It has been noticed that the difference in the harmonic content can diagnose the anode flooding. The future work is planned to design of a fault detection and isolation (FDI) algorithm based on this brief investigation.
references
Fig. 8 e Short-term impact of the anode flooding.
[1] Orf RICD. PEM fuel cells: theory and practice. Academic Press Series series editor. Elsevier Academic Press; 2005.
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Glossary FCs: Fuel Cells PEM: Proton Exchange Membrane PEMFC: Proton Exchange Membrane Fuel Cell FCSS: Fuel Cell Stack System SOFC: Solid Oxide Fuel Cell MCAFC: Molte Carbonate Acid Fuel Cell EV: Electrical Vehicle AI: Artificial Intelligence EIS: Electrochemical Impedance Spectroscopy ANN: Artificial Neural Network BP: Black Propagation GDL: Gas Diffusion Layer WT: Wavelet Transform GA: Genetic Algorithm SP: Signal Processing
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Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system A. Benmouna a,*, M. Becherif a, D. Depernet a, F. Gustin b, H.S. Ramadan a,c, S. Fukuhara b a
FCLAB FR CNRS 3539, FEMTO-ST UMR CNRS 6174, Univ. Bourgogne Franche-Compte/UTBM, France FCLAB FR CNRS 3539, FEMTO-ST Univ. Bourgogne Franche-Comte/Univ. Franche-Comte, France c Zagazig University, Faculty of Engineering, 44519 Zagazig, Egypt b
article info
abstract
Article history:
In the field of alternative electrical power generation, the durability of the energy presents
Received 23 February 2016
a great challenge to science. Fuel Cells (FCs) are considered as one of the future promising
Received in revised form
energy sources. The Proton Exchange Membrane (PEM) is more suitable for implementation
9 July 2016
into daily-use applications. To bring FCs into the market, such technology should have an
Accepted 21 July 2016
adequate operation reliability, sufficient lifetime and acceptable cost. Therefore, different
Available online xxx
problems associated with the faults of FC various components influence the reliability of the system. The issues can be successfully overcome using the diagnostic tools as part of
Keywords:
the advanced control system. In this paper, after a short presentation of the FC and its
Proton Exchange Membrane Fuel
components, the paper emphasizes different faults that may occur. Using the literature,
Cell
the root causes of these faults are identified. From this point, a state of the art on the recent
Fuel cell stack system
studies concerning the fault diagnosis method is presented. In order to illustrate the state
Fuel cell faults
of the art, a brief experimental study of the impact of anode flooding is given and a signal-
Fault diagnosis methods
based diagnostic method is suggested.
Model based method
© 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Non-model based method
Introduction Recently, the expected increasing of the global energy demand is foreseen to have a huge impact on the pollution rate and all the issues related to global warming. Therefore, it is crucial to develop the research on non-pollutant energy sources. Among different solutions, the use of hydrogen as an energy vector is a serious candidate to participate in the energy mix. To convert hydrogen into electricity, a Fuel Cell (FC) system is a natural system. Therefore, alternative energy source research issues become of great importance. One of the green energy
sources is the hydrogen energy. The FC system is an electrochemical device, which converts chemical energy of the fuel into electrical energy. Water and heat are also generated as only by-products during the FC operation. Therefore, the FC technology represents a promising energy source for future, owing to its free green-house gases produced and high conversion efficiency. There are several type of FC according to the employed electrolyte type. The Proton Exchange Membrane Fuel Cell (PEMFC) shows sufficient performances for everyday transportation and stationary applications up to 100 kW of power [1]. This FC type is used in wide range of applications, in regard of the low temperature of operation,
* Corresponding author. E-mail address: [email protected] (A. Benmouna). http://dx.doi.org/10.1016/j.ijhydene.2016.07.181 0360-3199/© 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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the ability to use oxygen from air, short start-up time and high power density. The successful FC introduction into daily applications should have a durable and reliable technology and a reduced cost of production, operation and maintenance [2,3]. According to the American study in [4], the US Department of Energy has announced a set of requirements for automotive applications to reach 5000 h FCs durability at a cost of $30/kW, and stationary FC systems of about 60 000 to 80 000-h durability at a cost of about $1000e1500/kW [4]. The necessity of this technology in the near horizon paves the way to the research community to focus mainly on the FC faults detection and the relevant different diagnosis methods in order to ensure durability and disponibility too. These faults must be detected early and sometimes needed to be estimated and accommodated. Therefore, FCs are vulnerable to faults that can cause its stop or permanent damage. To guarantee the safe operation of the FC systems, it is necessary to use systematic techniques to detect and isolate faults. This article presents a state of the art on the various methods of diagnostic faults of PEMFC. The focus is on the failure of the flooding of the dead-ended PEMFC because it is critical point for the performance of this technology. Thereafter, an experimental study that treats flooding problem at the anode chamber is presented. Therefore, the test bench behavior shown. The short-term impact of the anode flooding is presented in term of voltage versus time. In the experimental work, a difference in the harmonic content is used as the new method to detect the flooding fault.
Fuel cell system FCs are the devices discovered by William Grove in 1839. They are based on the electrochemical reaction that allows the electricity generation from hydrogen and oxygen of the air. These devices, that convert chemical energy, are ideal electrical power sources with either zero or lower emissions, clean, and high efficiency. The FC, as an attractive technology produces electricity, water, and heat only. Thereby, the pollution at the energy conversion can be eliminated. The FC is constructed like a sandwich constituted with an electrolyte between anode (negative electrode) and cathode (positive electrode) as shown in Fig. 1 [5]. According to the diverse parameters such as electrolytic, operating temperature and applications, different FC types can be distinguished: PEMFC, Solid Oxide Fuel Cell (SOFC), Molte Carbonate Acid Fuel Cell
Fig. 1 e Principle of fuel cell.
(MCFC), etc. In the study, PEMFC system that is one of the most promising alternatives to traditional systems and it represents an interesting and attractive application not only in the transport field but also in stationary and portable systems up to 200 kW [6]. This is due to the diverse characteristics such as higher power density, start-up ability, suitability for discontinuous operation and lower operating temperature compared to other types of FC [7e9]. In the last decade, the FC research has become of considerable interest for the diverse applications particularly in Electrical Vehicle (EV). The development of different diagnostic tools for FC systems become a must in order to ensure safety, security, and availability during faults. Consequently, these faults should be detected as early as possible. Although the studies on FCs comprises different disciplines, including materials science, transport phenomena, electro-chemistry and catalysis science, it is always a major challenge to deeply understand the thermodynamics, the fluid mechanics, the fuel cell dynamics and the inherent FC electrochemical processes. Furthermore, FC developed nowadays in industries do not manifest a perfectly constant behavior, still manifesting a certain variation in the model for any similar FCs produced at different times. For all these reasons, the energy generation systems based on fuel cell is complex. The FC system consists of four circuits of matter and energy, as depicted in Fig. 2, such as air, hydrogen, humidification and electrical circuit. Hydrogen valve is used for controlling the flow of gas H2. The air filter permits to eliminate solid particles like dust, molds and bacteria while the motor compressor favorites increasing of the air pressure by reducing its volume. The humidifier is a device that increases the moisture in the compressed air and allows filtering through the humidification circuit. The fluid manifold consists of one input and several outputs. It allows distributing the gas uniformly to guarantee the supply of fuel gas of each cell of the stack. The FC, the heart of the system, constitutes of several cells depending on the power. The cooling group includes electric fans. One is placed beside the compressor and the humidifier for cooling, and the other fan assures the stack low temperature in normal operating conditions [10].
Fuel cell faults Considering the pre-established optimal operating point, the FC systems need a set of auxiliary elements such as valves, compressor, sensors, controllers…etc. For this reason, they are vulnerable to faults that can cause the FC stop or permanent damage. These faults should be taken into account to guarantee the FC system safe operation. Furthermore, the use of the systematic techniques is required for fault diagnosis (detection and isolation of faults). In fact, the diagnostic tools allow distinguishing the structure-property-performance relationships between the FC and its components [11]. There are several FC faults, the most of them are cited in the literature as faults tree such illustrated in Fig. 3 [11,12]. Failures of hydrogen circuit may be either at the level of the hydrogen source due to blockage or leakage of hydrogen from the tank, or at the level of the hydrogen regulator valve,
Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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Fig. 2 e Fuel cell stack system (FCSS).
Fig. 3 e Fault trees of FCS system [12].
caused by the blockage or hydrogen leakage from the valve. The faults of air compressor failures are from diverse nature, namely electrical faults caused for example by short-circuit, over-voltage; mechanical faults principally due to the Crank shaft's stall; hydraulic faults caused by the decrease of compressibility effect. Moreover, the fault of the Controller's that due to its breakdown. Inside the FC, the water distribution is the principal parameter that affects the channel of membrane and the electrode and consequently FC performance is affected. Hence, the water available inside of the FC must be purged appropriately [13]. The accumulation of water in the channels can block the diffusion of gases. Thus, the FC operation may be disturbed, and may result in the FC flooding. The lack of water causes dehydration of the membrane. Therefore, the bad water management in the FC system causes the faults in Catalyst support oxidation, Catalyst agglomeration and migration [12]. Faults tree is an important architecture that allows identifying the various breakdowns that the FC may encounter. These defects cause the disturbance of the operation and the degradate this component,
which necessarily leads to the decrease in overall system performance [14,15]. According to [12], the fault tree could be completed using a fourth level that defines the principal causes of the detailed failures. Generally, the PEMFC flooding is linked into three parameters: current density, gas flow rate and temperature. At high current density operation, the water-produced rate is greater than the water quantity leaving the cathode. The increased water quantity inside the FC significantly depends on the operating condition's interactions particularly the low gas flow/temperature levels [16]. Moreover, the flooding can be caused at low current densities under low temperature and high gas relative humidity that result in gas phase saturation by water vapor [16]. On the opposite of the flooding phenomenon, unsuitable operating conditions may lead to drying faults. The high temperature accelerates the water evaporation, which produces the drying membrane. Therefore, the use of inlet dry gas, or with a low relative humidity, can result in undesired dry membrane. Dysfunction of humidity system decreases the
Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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relative humidity and the relevant drying. The produced current is an important parameter to be considered that affects the water produced quantity. Less water quantity at the cathode can be explained by lower current [17]. Concerning the FC poisoning due to H2 produced by a reforming process or air gas quality. Obviously, the gas contamination causes FC degradation [18]. The efficiency of the air compressor in FC system is estimated by its capability to supply the desired value of stoichiometry with an acceptable time constant regardless of the FC current variation. The faults at air compressor level have been explained in [12]. In electrical systems, the short circuit causes the change of the internal resistance of the compressor destroying the moto-compressor group. The stall of crack shaft and friction decrease mechanical and hydraulic efficiency of the moto-compressor. In the controller subsystem, the main fault cause is the failure of the regulator [12]. The hydrogen subsystem is of the utmost importance and its misbehavior may lead to dangerous consequences, such as application of a high pressure. Mishandling can cause the destruction of the valves, leading to hazardous operating conditions. As the hydrogen is the lightest molecule, its leakage is a critical issue as hydrogen is highly inflammable [19]. Several researchers are interested in the study of FCs tree faults. In [20] the authors have established a tree model of failure of the FC considering four types of failure symptoms: hardware, software failures besides the environmental and human factors. In [21], the degradation of PEMFC based on fault tree has been studied. This fault tree is a power representation to establish a deductive analysis, which assesses the internal state of the fuel cell and its lifetime. The authors in [22] have developed the fault tree in order to discriminate the different failures and to understand the origin of each fault and the relation between causes and symptoms. To understand in depth the reliability of the PEMFC and to improve modeling accuracy of this technology, the authors in [23] have proposed to use the fault tree of fuel cell. In the work [14], the authors have proposed using the fault tree because it helps to model clearly and intuitively the different causal relations of the degradation mechanisms and fault tree allows quantifying components specific degradations, and their effects on the global degradation of the cell. Based on the data extracted from the literature, the histogram shown in Fig. 4 presents the percentage of most common PEMFC faults. The water management, namely the flooding and drying that represent 33% and 19%, respectively. Followed by the durability, the leakage and other faults, that can be of CO poising, membrane degradation. Several studies have resorted to the transparent cell design based on optical diagnostics as demonstrated in Fig. 5 [2] in order to define the origin and the development of flood phenomena with high spatial and temporal accuracy in FC systems. The cell is constituted of two transparent acrylic cover plates and two flow field brass plates that are anode and cathode [24] have proposed on transparent PEMFC to study the distribution of water and its flooding inside the cathode gas channels. The system is used for explaining the phenomena of the membrane dehydration [25] have considered the same device for H2 fueled PEMFC in order to visualize CO2 gas bubble behavior in direct methanol FCs [26] have used a transparent cell to visualize the liquid water and ice formation during
Fig. 4 e The percentage of different FCs faults.
Fig. 5 e Transparent FC [2]. startup of the FC at subzero temperatures [27,28] have developed a number of transparent FCs with different flow fields each in order to study the water flood, besides the two-phase flow of the reactant, products and pressure drop in the cathode flow channels. In addition to the water, the FC temperature can affect on both the drying and the flooding faults. The third fault of part FC concerns gas contamination, where different contamination of feed gas by N2, CO or CO2 are possible. Because of the existence of parasitic reaction, different contamination of the feed gas can be detected. This case especially appears in stack systems, which use hydrogen produced from a fuel reformer or for systems operating in a polluted environment [11].
Fault diagnosis methods for FC systems The definition of fault diagnosis constitute several concepts such as: the detection of the occurrence of faults in the system, the localization of faults and the identification or analysis to provide the essential information of fault determination, such as: type, magnitude and causes,…etc. [15,29].
Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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The fault diagnosis approach is considered efficient if the FC system is monitored in continuous and automated manner. This allows reducing the system downtime and improving the system performance considering the maintenance cost. There are essentially two approaches of FC fault diagnostics as illustrated in Fig. 6: model based approaches and non-model based approaches.
Model based method This model is considered as an analytical approach that requires a comprehensive understanding of the cell and its inner phenomena. A series of key relations of the different natures (electrochemical, thermodynamic, thermal, electrical and fluidic) are necessary to develop this model. This approach is defined as an on-line comparison between the real behavior of the monitored systems obtained by means of sensors and the dynamic model of the same simulated system [6]. In case of detecting a significant discrepancy or residual between the model and the obtained sensor measurement, the existence of a fault is assumed. If a set of measurements is available, it is possible to generate a set of residuals (indicators) that present different sensitivities that correspond to the possible faults. The relive by analysing in real-time how the faults affect the residuals, it is possible in most cases to isolate the fault and to determine its magnitude [30]. These approaches are divided in two principal categories, as illustrated in Fig. 6, either is qualitative-or quantitativebased model. The former approach, it includes the abstraction hierarchy (functional and structural analysis), causal models (signed direct graphs [31], fault tree analysis [32], qualitative physics). However, the quantitative model based approaches include Analytical Redundancy Relations (ARR), observers, Kalman filters and statistic methods [22]. The performances of the quantitative model-based methods depend mainly on the model accuracy.
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The model of FC system is very complex. It contains nonlinearities and has interactions with several energy areas of different nature such as electrical, thermodynamic and electrochemical. The model based diagnostic techniques have been comprehensively discussed in [33] and [34]. In [29], six faults of interest in the PEMFC system have been diagnosed by the model based method: 1) the increase of the friction in the compressor motor, 2) its overheating, 3) the block of the channels in the diffusion layer of the FC, 4) the leakage in the air supply manifold, 5) the compressor motor control failure and 6) the stack temperature control failure. In order to characterize these faults, several variables have been used such as the oxygen excess ratio, the compressor's current density and its speed as well as the stack voltage. The sensitivity of the residual to the fault has been studied in order to distinguish the faults. A theoretical relative fault sensitivity matrix with the residual sensitivity in the row and the faults in columns has been considered for these fault types identification.
Non-model based method Concerning the non-model based methods, the availability of large amount of historical process data has been supposed in [15]. The objective of this method is to obtain fault information based on heuristic knowledge or signal processing or a combination of both [9]. In model based method, fault features, produced either by the residual generation or by relying on signal decomposition play a predominant role in the fault diagnosis task based on two sequential of faults procedures: detection and identification of the faults. However, in non-model approach, the diagnosis concept is different because these procedures are merged into a single diagnostic step by means of simulating human reasoning activities. This type of diagnosis refers to a decision-making process that can be automated by an expert system based on Artificial Intelligence (AI) technology [15].
Fig. 6 e Different fault diagnosis approaches for FCSS [9,12]. Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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Table 1 e The relevant recent studies on the PEMFC fault diagnosis. Diagnosis method Wavelet Transform (WT)
Fault Water management
Air stoichiometry
Residual method
Anodic flooding
Artificial Neural Network (ANN) ensemble method
Linked to the stack cooling system, Increasing of fuel crossover. Fault in air delivery system. Fault in hydrogen delivery system
Signal Processing (SP) method
Water management
Double fuzzy þ EIS
Water management
Pressure drop
Water management
EIS
Hydrogen leak, oxygen concentration
Infrared thermography þ a reactive flow
Catalyst loading defects
Neural networks
Air leakage, sensor fault, actuator fault
Description This WT has been employed to the PEMFC system reached the dysfunctions caused by inappropriate humidity level inside the cell, to diagnose the drying or flooding faults. The WT is used for detecting the defaults of the stoichiometry of air in the PEMFC. The energy contained in each detail of the wavelet decomposition is employed to diagnose the excess quantity of air in the system A difference in harmonic content is used as a new method to detect the flooding fault of a deadended PEMFC. The ANN ensemble method based on BP-ANN and the Lagrange multiplier method. A combination of four ANNs have been employed to detect four faults types: the stack cooling system, increasing of fuel crossover, fault in air delivery system, fault in hydrogen delivery system. The parameters characterizing these faults have been evolved as function of the time: voltage, current, temperature, airflow and H2, O2 pressures. The simulation results of this method has shown fault diagnosis accuracy between 75.24% and 85.62%. The SP method based on the empirical mode decomposition has been employed. Decomposing the output voltage of the FC has been used for detecting and isolating the fault flooding or drying. The SP provides high accuracy to diagnose the fault of poor management of water. The diagnostic methodology that concerns the faults of water management, discriminating different degrees of flooding or drying inside the FC system. The EIS has been used as the basis tool besides the fuzzy clustering and fuzzy logic. Through this method, five health states of the stack have been discriminated with easiness. The diagnosis has been performed but the faulty cell has not been located. A review of diagnosis method based on pressure drop has been introduced. This method has been considered as online water fault diagnosis. This method has been used for the diagnosis of the water fault because it has a significant variable which reflects the water content of the GDL or the flow channels. The identification of important parameters to detect the existence of the defect leakage inside the PEMFC has been studied. Among those parameters, the potential reverse fault has been cited which can be detected using EIS methods. The combination of infrared thermography together with the reactive flow for detecting catalyst loading defects in the PEMFC with high spatial and temporal resolutions has been considered. Experimental results and model prediction of thermal response has provided adequate agreement. The detection and isolation of faults using the neural network have been presented. Concerning different faults in the PEMFC stack: One component fault, one actuator fault and three sensor faults.
Reference Ibrahim et al. [41]
Pahon et al. [42]
Fukuhara et al. [43]
Shao et al. [44]
Damour et al. [45]
Zhixue et al. [46]
Pei et al. [47]
Ghassan et al. [48]
Das et al. [49]
Kamal et al. [10]
Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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Table 1 e (continued ) Diagnosis method Magnetic field measurement
Fault Membrane degradation
Description The approach is based on the control of the current density distribution inside the PEMFC. The proposed method has been used for diagnosing the failure of the membrane degradation of PEMFC due to long-term running with low stoichiometric air ratio.
This method is expected to be able, both to interpret realtime signals and to deliver the required control action, in order to recommend diagnosis procedures [35]. These methods have a adequate nonlinear approximation capability, and more computational efficiency [36]. In Fig. 6, the main non-model based approaches are illustrated: (i) the signal processing approaches: the magnetic resonance imaging, acoustic emission, magnetic field, neutron radiography. (ii) the AI based approaches: fuzzy logic, neural network, expert system; (iii) the experimental methods: voltage measurement, impedance spectroscopy, polarization curve interpretation, spatial current density distribution, pressure drop and gas chromatography. All these approaches are grouped into the non-model method type since there is no detailed mathematical modeling of all components and their interactions. It can be considered as a black box of inputeoutput behavior mentioned in this approach [35]. Several studies based on this method have been proposed in order to determine if the phenomena related to water management are acoustically active. Legros et al. [37] have used the acoustic emission technique. According to [38], the magnetic resonance imaging technique has been used for obtaining the liquid water distribution in the FC flow. In [39] the AI neural networks approach has been used for alleviating the task of on-board FC diagnostic.
Reference Hamaz et al. [50]
The main approach used in the AI is the fuzzy logic which needs a fuzzy model. In [40], a diagnosis-oriented model in Sugeno type of a FC power generator dedicated to automotive applications has been proposed. The Genetic Algorithm (GA) has been used for tuning of the fuzzy diagnosis model. Through this method, the accumulation of water and nitrogen in the anode compartment in case of a dead-end mode use of the FC has been diagnosed as well as the drying of the PEMFC localized by the configuration of threshold [40]. The chemical phenomena inside the FC can be studied with the electrochemical impedance spectroscopy by applying a very small amplitude of current signal over a wide range of frequency and to register the response. The ratio of the variation voltage and current gives the magnitude of the impedance and the phase shift. According to the literature, the majority of AC impedance studies of PEMFC involve in situ measurements due to the most PEMFC pertinent data [40]. To minimize the performance degradation of PEMFC, several studies have been performed the degradation mechanisms of the components and the different methods of diagnosing the appropriate FC system breakdown that must be detected, isolated and corrected to prevent serious damages overall system. Table 1 cites the relevant recent studies on the PEMFC fault diagnosis that have been treated:
Fig. 7 e Test bench. Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181
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From Table 1, the most critical fault in FC stacks is the poor water management that may cause flooding or drying of the system. Therefore, the experimental study will focus on one of this critical fault type, namely the flooding in dead end anode PEMFC.
An experimental case study: anode flooding of a dead-ended PEMFC An emphasis on the anode flood phenomenon is developed. Using the previous experimental observations, an idea of the new method for anode flooding diagnostic is introduced. First, the test bench is presented, the FC is an open cathode, dead end anode PEMFC from the H-500 Horizon FC series composed of 36 cells with a nominal active surface area of 57 cm2. The air is forced through two fans, which are used for cooling down the PEMFC. The test bench includes a DC/DC converter, a 12 V 6 batteries, a programmable load and sensors for flow rate, currents and voltages. All the measured quantities are acquired through a measuring/control device (CompactRIO of National Instrument®) which controls the current provided by the FC through the DC/DC converter. Fig. 7 describes how the components of the FCS are interconnected. As explained in [43], anode flooding has led to the carbon corrosion of the cathode catalytic support and decreasing irreversibly the electrical performances of the PEMFC. At short-term, the liquid water has prevented the fuel to access to the catalytic sites. Fig. 8 shows the short-term impact at static conditions over a period of time of 10 min. To mitigate these long and short terms degradation, it is necessary to detect the anode flooding. Fukuhara et al. [43] have shown that, during an anode flooding, the hydrogen inlet mass flow peaks appear during the purge process of the dead-ended anode tends to decrease drastically when compared to the healthy state. The main disadvantage of the work of Fukuhara et al. [43] was the establishment of a 0D model where two parameters are determined by ad-hoc experiments. The idea of the experimental part is to detect an anode flooding by designing threshold on the Fast-Fourier Transform (FFT) of the inlet mass flow. From Fig. 9, the difference in the harmonic content can be used as a new diagnostic method for anode flooding.
Fig. 9 e Frequency spectrum of the hydrogen inlet mass flow for healthy and flooding conditions.
Conclusion and perspectives This paper presents the basics of the fault diagnosis of PEMFC to improve its durability and reliability which are crucial issues to be widely commercialized. These failures are mainly due to the faults on different FC system components, each failure is presented in this paper as a fault tree. Two diagnostic methods are explained: the non-model based methods and the model based methods. The model based method require an accurate behavior model to allow the analysis and faults diagnosis. Therefore, concerning FC, it is necessary to understand different domains, such as: electricity, mechanic, thermodynamic, electrochemical. Moreover, the determination of the internal parameters of FC systems is difficult because it requires a large number of sensors or some parameter are nonobservable. All of these reasons explain the complexity and difficulty of using the model based method for fault diagnosis. For the non-model based approach, an experimental database is necessary for the fault diagnosis. The information of fault is directly extracted from database for its classification. The characteristic of the fault can be obtained by signal processing. The AI methods provided the efficiency solution for isolation of the fault and further analyses. In conclusion, the non-model based approach is more interesting for the fault diagnosis due to the less knowledge requirement of the system and its relative simplicity and efficiency for on-line fault diagnosis applications. From the last studies, it is remarkable that the critical fault in the PEMFC system is mainly the bad water management. For this, an experimental study is proposed which study the flooding fault of dead-ended anode PEMFC. It has been noticed that the difference in the harmonic content can diagnose the anode flooding. The future work is planned to design of a fault detection and isolation (FDI) algorithm based on this brief investigation.
references
Fig. 8 e Short-term impact of the anode flooding.
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Glossary FCs: Fuel Cells PEM: Proton Exchange Membrane PEMFC: Proton Exchange Membrane Fuel Cell FCSS: Fuel Cell Stack System SOFC: Solid Oxide Fuel Cell MCAFC: Molte Carbonate Acid Fuel Cell EV: Electrical Vehicle AI: Artificial Intelligence EIS: Electrochemical Impedance Spectroscopy ANN: Artificial Neural Network BP: Black Propagation GDL: Gas Diffusion Layer WT: Wavelet Transform GA: Genetic Algorithm SP: Signal Processing
Please cite this article in press as: Benmouna A, et al., Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.07.181