The complexity of Fuel Cell (FC) systems demands a profound and sustained understanding of the various phenomena occurring inside of it. Thus far, FCs, especially Proton Exchange Membrane Fuel Cells (PEMFCs), have been recognized as being among the most promising technologies for reducing Green House Gas (GHG) emissions because they can convert the chemical energy bonded to hydrogen and oxygen into electricity and heat.
1. Introduction
Climate change has been raising different questions and concerns with scientists and researchers around the world. It is caused by the increase in Green House Gas (GHG) emissions, such as carbon dioxide, which are released during the process of burning fossil fuel to produce electric energy. Nonetheless, most European countries are starting to have a significant quota of Renewable Energies
[1]. Even though the percentage of renewable energy is increasing, another problem related to energy storage arises. This problem is related to the intermittent nature of renewable sources, which are dependent on external, uncontrollable factors, such as the sun and the wind, generating a gap between the available energy and its final use. Therefore, storing the excess produced energy is paramount to eliminating the intermittent nature of renewable energies
[2]. Among other solutions, hydrogen storage is being intensively researched to work as an energy vector, as illustrated in
Figure 1, for future direct conversion to electricity when demanded
[1,3][1][3].
Figure 1.
General classification of electrical energy storage technologies.
Converting the energy stored in hydrogen back to electricity requires an electrochemical device, such as an FC, which chemically converts the energy present into two reactants, in this case, hydrogen and oxygen, into electricity and heat
[4]. Despite the simple theoretical process, the overall composition of the system is still dependent on expensive and rare materials, such as gold and platinum
[4]. So, to ensure that the lifetime expectancy increases and the FC system is reliable, a deep understanding of how it operates under different conditions is required; this includes identifying ways to prevent and address any potential issues, like faults
[5]. Thereafter, the analysis of fault diagnosis plays a crucial role in ensuring the stable operation and efficient output of FC systems. In recent years, numerous scholars have delved into this area to enhance accuracy and reliability. There are primarily two types of approaches: model-based methods (both qualitative and quantitative) and data-driven methods. In the model-based approach, an accurate mathematical model is developed based on the internal mechanisms of FC system operation
[6]. Subsequently, residual vectors are utilized as features for statistical analysis to extract fault information embedded in the residual sequence. This process enables the detection of abnormal situations within the system, but existing fault diagnosis technology falls short in predicting the future degradation trend of FC systems. If the degradation of FCs could be estimated before a complete failure, maintenance personnel would have ample time to devise maintenance plans and replace components proactively. This proactive approach could significantly reduce repair and maintenance costs and prevent the unscheduled downtime of FC systems
[7,8][7][8]. Faults, or failure modes, have been well established in the literature
[9], and some reviews on fault diagnosis have been summarised in
[10,11][10][11]. For example, in both
[12[12][13],
13], several tests for single and multi-cells under the influence of constant and dynamic loads have been reviewed and analyzed. It has been concluded that ensuring a specific relationship between failure modes and a particular signal, referenced as an indicator, requires an extensive amount of data under various operation conditions. However, there is an evident relationship between the cathode pressure drop and cathode flooding. Moreover, ref.
[14] presented different model and non-model diagnosis methods for FC, while ref.
[15] focused more on non-model-based diagnostics approaches. The conclusion drawn was that, when compared to model-based techniques, non-model-based approaches require a larger dataset of both normal and faulty conditions. Nevertheless, they are considered a prominent method for FC fault diagnosis due to their simplicity, flexibility, and ability to handle system uncertainties.
2. Fuel Cells Technologies
Fuel cells can be classified based on several factors. First, they can be categorized depending on the type of electrolyte separating the electrodes they use, which can be either alkaline or acidic. Second, a more common classification is through the operating temperature; therefore, they are referred to as a High-Temperature Fuel Cell (HT-FC) or a Low-Temperature Fuel Cell (LT-FC).
Table 1 outlines the typical characteristics of the different types of FCs, organized by their operating temperatures. The efficiency of FCs is influenced by the choice of electrolytes, catalysts, and operating temperature. Different FCs and their applications are illustrated in
Table 1.
Table 1.
An overview of the different materials for different types of FCs.