Bond graph modeling,design and experimental validation of a photovoltaic/fuel cell/ electrolyzer/battery hybrid power system

This work presents a complete bond graph modeling of a hybrid photovoltaic-fuel cell-electrolyzer-battery system. These are multi-physics models that will take into account the influence of temperature on the electrochemical parameters. A bond graph modeling of the electrical dynamics of each source will be introduced. The bond graph models were developed to highlight the multi-physics aspect describing the interaction between hydraulic, thermal, electrochemical, thermodynamic, and electrical fields. This will involve using the most generic modeling approach possible for managing the energy flows of the system while taking into account the viability of the system. Another point treated in this work is to propose. In this work, a new strategy for the power flow management of the studied system has been proposed. This strategy aims to improve the overall efficiency of the studied system by optimizing the decisions made when starting and stopping the fuel cell and the electrolyzer. It was verified that the simulation results of the proposed system, when compared to simulation results presented in the literature, that the hydrogen demand is increased by an average of 8%. The developed management algorithm allows reducing the fuel cell degradation by 87% and the electrolyzer degradation by 65%. As for the operating time of the electrolyzer, an increment of 65% was achieved, thus improving the quality of the produced hydrogen. The Fuel Cell's running time has been decreased by 59%. With the ambition to validate the models proposed and the associated commands, the development of this study gave rise to the creation of an experimental platform. Using this high-performance experimental platform, experimental tests were carried out and the results obtained are compared with those obtained by simulation under the same metrological conditions.     

Optimal Multi-Criteria Waypoint Selection for Autonomous Vehicle Navigation in Structured Environment

This paper deals with autonomous navigation of unmanned ground vehicles (UGV). The UGV has to reach its assigned final configuration in a structured environments (e.g. a warehouse or an urban environment), and to avoid colliding neither with the route boundaries nor any obstructing obstacles. In this paper, vehicle planning/set-points definition is addressed. A new efficient and flexible methodology for vehicle navigation throughout optimal and discrete selected waypoints is proposed. Combining multi-criteria optimization and expanding tree allows safe, smooth and fast vehicle overall navigation. This navigation through way-points permits to avoid any path/trajectory planning which could be time consuming and complex, mainly in cluttered and dynamic environment. To evaluate the flexibility and the efficiency of the proposed methodology based on expanding tree (taking into account the vehicle model and uncertainties), an important part of this paper is dedicated to give an accurate comparison with another proposed optimization based on the commonly used grid map. A set of simulations, comparison with other methods and experiments, using an urban electric vehicle, are presented and demonstrate the reliability of our proposals.     

Effect of Power Amplifier Nonlinearity on the Physical Layer Security of MIMO Systems

Wireless Personal Communications - This paper presents an analytical investigation on the effect of nonlinear high-power amplifiers on the physical layer security of multiple-input-multiple-output...     

Inner surface modification of halloysite nanotubes and its influence on morphology and thermal properties of polystyrene/polyamide‐11 blends

The asymmetry of halloysite surface chemistry was used to perform a selective modification of its inner surface via grafting of a synthesized styrene/(methacryloyloxy)methyl phosphonic acid copolymer. Fourier transform infrared spectroscopy, thermogravimetric analysis (TGA) and pyrolysis gas chromatography/mass spectrometry were used to evidence and quantify the grafting. Then, raw and hybrid nanoparticles were incorporated in polystyrene (PS)/polyamide‐11 (PA11) blends (80/20 and 60/40 wt%). Scanning electron micrographs showed differences in localization of the halloysite nanotubes (HNTs), since raw halloysite is concentrated in the PA11 phase while modified halloysite is also located at the PS/PA11 interface, leading to a better interfacial adhesion between PS and PA11. An inhibiting effect of modified halloysite on PA11 coalescence was evidenced by measuring the particle size distribution of the extracted nodules. Moreover, the presence of modified halloysite at the interface shows an improvement in terms of thermal stability as observed by TGA, but with no significant effects on PA11 crystallization behaviour as shown by differential scanning calorimetry results. Rheological measurements were carried out to study the influence of the surface modification of halloysite on the blend morphology. A gel‐like behaviour was observed for the (60/40 wt%) HNTs reinforced composition that was enhanced in the case of 10% functionalized halloysite. © 2016 Society of Chemical Industry     

A practical approach for modelling and control of biomass pyrolysis pilot plant with heat recovery from combustion of pyrolysis products

A pilot plant of biomass pyrolysis using pyrolysis products as fuel has been tested and shown to improve energy balance of the process and to be environmentally friendly by avoiding rejection of pyrolysis pollutants fumes into the atmosphere. The high number of parameters involved in a pyrolysis process makes it difficult to specify an optimum procedure for charcoal yield and pyrolysis cycle durability. So the knowledge of the essential parameters which govern the kinetics mechanisms of the biomass thermal decomposition and the combustion of pyrolysis gases is very useful to understand the operating cycle of the plant. In the present study a thermochemical model is developed in order to simulate and control the operating cycle of the system. The effect of the inlet molar air flow rate on the temporal evolution of biomass mass loss rate and temperatures in the different active zones of the pilot plant as well as the determination of the critical inlet molar air flow rate for which accidental runaway of combustion reactions occurs are presented. To avoid this accidental phenomenon a Proportional-Integral-Derived (PID) anticipated regulation is used in order to control temperatures evolution in the different zones of the device and avoid the runaway of combustion reactions.     

On the Efficiency of NH3–SCR Catalysts for Heavy Duty Vehicles Running on Compressed Natural Gas in Synthetic Gas Bench Scale

This work aims to study the effectiveness of NH₃–SCR after-treatment systems, initially developed for a Diesel application, on Heavy duty natural gas engines working in lean conditions for exhaust gas pollutants abatement. Commercial oxidation and NH₃–SCR catalysts were investigated for respectively CH₄, CO oxidation and NO_X reduction. In this study, we showed that the NH₃–SCR coupled with an oxidation catalyst lead to significant conversion of CH₄, CO and NO_X, and can be used as after-treatment system for pollutants providing from CNG lean burn engines.     

ADR-SPLDA: Activity discovery and recognition by combining sequential patterns and latent Dirichlet allocation

This paper presents ADR-SPLDA, an unsupervised model for human activity discovery and recognition in pervasive environments. The activities are encoded in sequences recorded by non-intrusive sensors placed at various locations in the environment. Our model studies the relationship between the activities and the sequential patterns extracted from the sequences. Activity discovery is formulated as an optimization problem in which sequences are modeled as probability distributions over activities, and activities are, in turn, modeled as probability distributions over sequential patterns. The optimization problem is solved by maximization of the likelihood of data. We present experimental results on real datasets gathered in smart homes where people perform various activities of daily living. The results obtained demonstrate the suitability of our model for activity discovery and characterization. Also, we empirically demonstrate the effectiveness of our model for activity recognition by comparing it with two of the widely used models reported in the literature, the Hidden Markov model and the Conditional Random Field model.