Experimental validation of fuel cell, battery and supercapacitor energy conversion system for electric vehicle applications

Due to the increasing air pollution and growing demand for green energy, the most of research is focused on renewable and sustainable energy. In this work, the PEM fuel cell is proposed as a solution to reduce the impact of the internal combustion engines on air pollution. In this paper a PEM fuel cell, battery and supercapacitor energy conversion system is proposed to ensure the energy demand for an electric vehicle is achieved. The storage system consisting of a battery and supercapacitor offers good performance in terms of autonomy and power availability. In this paper, an energy management of the PEM fuel cell electric vehicle has been first simulated in Matlab/Simulink environment and the results are discussed. Second, a Realtime experimental set up is used to test the performance of the proposed PEM fuel cell electric vehicle system. Experimental results have shown that the proposed system is able to satisfy the energy demand of the electric vehicle.N/

Reverse electrowetting as a new approach to high-power energy harvesting

Over the last decade electrical batteries have emerged as a critical bottleneck for portable electronics development. High-power mechanical energy harvesting can potentially provide a valuable alternative to the use of batteries, but, until now, a suitable mechanical-to-electrical energy conversion technology did not exist. Here we describe a novel mechanical-to-electrical energy conversion method based on the reverse electrowetting phenomenon. Electrical energy generation is achieved through the interaction of arrays of moving microscopic liquid droplets with novel nanometer-thick multilayer dielectric films. Advantages of this process include the production of high power densities, up to 103 W m−2; the ability to directly utilize a very broad range of mechanical forces and displacements; and the ability to directly output a broad range of currents and voltages, from several volts to tens of volts. These advantages make this method uniquely suited for high-power energy harvesting from a wide variety of environmental mechanical energy sources

Quantitative power loss analysis and optimisation in nth-order low voltage multilevel converters

Focusing on cascaded H-bridge converters for grid-tie battery energy storage, a practical, analytical method is derived to evaluate the switching-associated power loss in multilevel converters, evaluated from a number of sources of loss. This new method is then used to find performance trends in the use of converters of increasing order over a range of switching frequencies. This includes an experimental analysis into predicting the performance of MOSFET body diodes. Our analysis with this model shows that a multilevel converter can have lower losses than the equivalent single bridge, three-level converter, particularly at higher switching frequencies, due to the availability of suitable switching devices. It also has interesting implications for enabling the use of cutting-edge non-silicon power switching devices to further improve potential efficiencies

Effect of mineral-enriched diet and medicinal herbs on Fe, Mn, Zn, and Cu uptake in chicken

<p>Abstract</p> <p>Background</p> <p>The goal of our study was to evaluate the effects of different medicinal herbs rich in polyphenol (Lemon balm, Sage, St. John's wort and Small-flowered Willowherb) used as dietary supplements on bioaccumulation of some essential metals (Fe, Mn, Zn and Cu) in different chicken meats (liver, legs and breast).</p> <p>Results</p> <p>In different type of chicken meats (liver, legs and breast) from chickens fed with diets enriched in minerals and medicinal herbs, beneficial metals (Fe, Mn, Zn and Cu) were analysed by flame atomic absorption spectrometry. Fe is the predominant metal in liver and Zn is the predominant metal in legs and breast chicken meats. The addition of metal salts in the feed influences the accumulations of all metals in the liver, legs and breast chicken meat with specific difference to the type of metal and meat. The greatest influences were observed in legs meat for Fe and Mn. Under the influence of polyphenol-rich medicinal herbs, accumulation of metals in the liver, legs and breast chicken meat presents specific differences for each medicinal herb, to the control group that received a diet supplemented with metal salts only. Great influence on all metal accumulation factors was observed in diet enriched with sage, which had significantly positive effect for all type of chicken meats.</p> <p>Conclusions</p> <p>Under the influence of medicinal herbs rich in different type of polyphenol, accumulation of metals in the liver, legs and breast chicken meat presents significant differences from the group that received a diet supplemented only with metal salts. Each medicinal herb from diet had a specific influence on the accumulation of metals and generally moderate or poor correlations were observed between total phenols and accumulation of metals. This may be due to antagonism between metal ions and presence of other chelating agents (amino acids and protein) from feeding diets which can act as competitor for complexation of metals and influence accumulation of metals in chicken meat.</p> <p><b>Graphical abstract</b></p

Activity in perceptual classification networks as a basis for human subjective time perception

Despite being a fundamental dimension of experience, how the human brain generates the perception of time remains unknown. Here, we provide a novel explanation for how human time perception might be accomplished, based on non-temporal perceptual classification processes. To demonstrate this proposal, we build an artificial neural system centred on a feed-forward image classification network, functionally similar to human visual processing. In this system, input videos of natural scenes drive changes in network activation, and accumulation of salient changes in activation are used to estimate duration. Estimates produced by this system match human reports made about the same videos, replicating key qualitative biases, including differentiating between scenes of walking around a busy city or sitting in a cafe or office. Our approach provides a working model of duration perception from stimulus to estimation and presents a new direction for examining the foundations of this central aspect of human experience

A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition