111 research outputs found
Experimental validation of fuel cell, battery and supercapacitor energy conversion system for electric vehicle applications
Get PDFDue 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/
Quantitative power loss analysis and optimisation in nth-order low voltage multilevel converters
Get PDFFocusing 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
How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?
Get PDFHumans 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
Activity in perceptual classification networks as a basis for human subjective time perception
Get PDFDespite 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
E-transportation: the role of embedded systems in electric energy transfer from grid to vehicle
Get PDFIMPROVING THE PRODUCTION PROCESS OF BIODIESEL FROM WASTE OIL BY TIO2 CATALYTIC NANOPARTICLES COMPARED WITH CONVENTIONAL CATALYST, SODIUM
No full textDue to a huge increase in world energy consumption, the limited traditional fossil energy resources, and increased environmental concerns, a requirement for alternative energy sources has been paid great attention in recent years. Biodiesel is known as a nontoxic, renewable and environmental-friendly biodegradable fuel that is free from sulfur and aromatic compounds. The biodiesel production by transesterification of vegetable oils has the potential to solve the above problems and concerns. Nanocatalysts are considered as important material in chemical processes, energy production and energy savings, and prevent environmental pollution. In this study, the characteristics and performance of TiO2 nanoparticles (TNPs) and one commonly used catalyst for
alkaline-catalyzed transesterification, i.e., sodium hydroxide, were evaluated using waste olive oil. The present method affords nontoxic and non-corrosive medium, high yield of biodiesel, clean reaction and simple experimental and isolation procedures. The catalyst can be recycled by simple filtration and reused without any significant reduction in its activity. The process variables
that influence the transesterification of triglycerides, such as volume ratio of methanol to waste olive oil, type and loading of catalyst, reaction time and reaction temperature, were investigated. High catalysis activity and a much more specific surface TNPs were found to be more superior to sodium hydroxide under the same conditions. The results showed that TNPs as catalyst can improve the biodiesel production up to 87.8\% in the same condition in which the efficiency is 76.4\% for sodium hydroxide as a homogeneous catalyst. The effect of biodiesel/diesel blend fuels on engine exhaust emissions in a Robin engine was evaluated. The testing results show that the B20 blend fuel (including 20\% and 80\% v/v biodiesel and diesel fuel, respectively) reduced (HC) and carbon monoxide (CO) emissions to 28.9 and 20.6\% compared to the petroleum diesel fuel, respectively. In addition, in this study, the effective use of biodiesel to reduce air pollutant emissions was approved, although a slight increase in
nitrogen oxides emissions than pure diesel fuel was observed that quite what was expected due to increasing combustion temperature
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