## 1 Introduction  
Nowadays, artificial intelligence are widely used for the dynamic management of complex systems. Spanning the last two decades, among the most successful artificial intelligence techniques, artificial neural networks (ANNs) certainly deserve attention, for their simplicity of implementation and wide possibility of application [1; 2]. ANNs' strong point is the ability to predict the evolution of a system when non-linear phenomena are occurring [3]: for that reason, they are claimed particularly interesting for control engineering and fault diagnosis matters. Hence, in the field of energy systems,scientific literature shows a plethora of recent advancements, addressing forecast and simulation of system based on unpredictable power sources such as sun and wind [4], along with algorithms for load management in sub-grids [5; 6; 7], where dynamic and self-learning codes are useful to customize the system management (i.e., demand-side response programs). Beside management issues, with regard to energy conversion systems, ANNs exhibit a great relevance to the matters of performance mapping and fault diagnosis (e.g., related to gas-turbines [8; 9; 10] heat exchangers [11], internal combustion engines [12; 13; 14], degeneration units [15]). With regard to the present energy roadmap, electrochemical energy systems--like batteries and fuel cells--are increasingly attracting attention, because of their high efficiency and eco-friendliness [16; 17]. Whilst electrochemistry and catalysis are complex non-linear processes, ANNs can easily handle performance variation and faults prediction of such systems. For this reason, they are a particularly suitable technique to foster the integration of electrochemical devices in energy systems and facilitate their management, coping with the variability of operating conditions. To this end, other researchers already investigated the topic of artificial intelligence applications in electrochemical energy systems. For example, the authors of [18] applied ANNs to fault diagnosis of batteries for electric vehicles, while in Reference [19] the topic was addressed in the outlook of energy management in hybrid configuration. Yet, ANNs significantly lower computational times and their application specific to fuel cells have been studied in several papers [20; 21]. In many of them, this method is applied to go through the integration of the fuel cell in a complex system, as in Reference [22] regarding the development and control of a Polymer Electrolyte Fuel Cell-based (PEFC) electric power train for bicycles. Also Reference [23] shows a study to improve the integration of fuel cells in grid-connected power plants. Finally, in others, artificial intelligence is used to gain insight within the fuel cell itself, being a sophisticated component and subcomponent-level modelling approach. For instance, the authors of Reference [24] applied ANNs to improve the power density of low temperature PEFCs. Similarly, high temperature fuel cells like Solid Oxide Fuel Cells (SOFCs) are particularly interesting, since the implementation of ANN in the system modelling and control allows by-passing a detailed characterization required because of complex chemistry on the fuel cell surface. SOFCs show high flexibility with regard to the fuel, yet their performances might be sensitive to the variation of fuel composition and operative conditions. For example, a change in temperature impacts on the electrolyte conductivity, as well as it affects chemical kinetics of internal reforming and gas shift reactions. Then, as the anode feeding total flow rate decreases, the gas residence time at the active sites of the cells increases, driving equilibrium-driven internal processes. Detailed modelling approaches take into account non-linear phenomena (viz. gas diffusivity, kinetics), which require the knowledge of many parameters that are hard to collect experimentally. Moreover, any model stands upon the superimposition of some hypothesis, introducing a loss of knowledge due to a certain level of simplification.