Proton exchange membrane fuel cell (PEMFC) degradation prediction is essential especially in transportation applications, since one of the major issues that hinder its worldwide commercialization is represented by its durability. However, due to the complex physical phenomena inside the fuel cell, which are usually strongly inter-coupled, the conventional semi-empirical model-based prognostics approach may fail to predict the aging phenomena under varies fuel cell operating conditions. In order to improve prognostics accuracy, this paper proposed a data-fusion approach to forecast the fuel cell performance based on long short-term memory (LSTM) recurrent neuron network (RNN) and auto-regressive integrated moving average (ARIMA) method. LSTM can efficiently make a prediction regarding a long-term physical degradation, whereas the fusion with ARIMA can effectively track the degradation tendency. In order to validate the performance of the proposed data-fusion approach, two different PEMFCs are tested for recording the aging experimental datasets. The forecasting results indicate that the proposed LSTM-ARIMA approach can accurately predict PEMFC degradation, which can be then used directly to optimize fuel cell performance implemented in transportation applications.

This paper presents a multi-physical modeling of a 2D reversible tubular solid oxide cell. The developed model can represent both a solid oxide electrolysis cell (SOEC) and solid oxide fuel cell (SOFC) operations. By taking into account of the electrochemical, fluidic and thermal physical phenomena, the presented model can accurately describe the multi-physical effects inside a cell for both fuel cell and electrolysis cell operation under entire working range of cell current and temperature. In addition, an iterative solver is proposed which is used to solve the 2D distribution of physical quantities along the tubular cell. The proposed model is suitable for embedded applications such as real-time simulation or online diagnostic control. The reversible solid oxide cell model is then validated experimentally in both SOEC and SOFC configurations under different species partial pressures, operating temperatures and current densities conditions.

High-entropy films (HEFs) are of considerable interest in surface engineering applications due to their superior properties, such as good corrosion resistance, good thermal stability and excellent high temperature oxidation. Recently, the scientific community has seen an increasing development of the multicomponent coatings, improving their properties compared to conventional films. Technically, different strategies have been exploited to fabricate HEFs. Magnetron-sputtered HEFs have made significant advancements in this field. HEFs have various applications given their interesting performances. This article overviews the development and the outcome of HEFs prepared using the magnetron sputtering technique. The classification of HEFs is reported. The effect of magnetron sputtering parameters on the microstructural, mechanical, electrochemical and thermal properties of HEFs is also discussed. Applications of HEFs are reported in the last section.