Multifunctional converter to interface renewable energy sources and electric vehicles with the power grid in smart grids context

This paper proposes a multifunctional converter to interface renewable energy sources (e.g., solar photovoltaic panels) and electric vehicles (EVs) with the power grid in smart grids context. This multifunctional converter allows deliver energy from the solar photovoltaic panels to an EV or to the power grid, and exchange energy in bidirectional mode between the EV and the power grid. Using this multifunctional converter are not required multiple conversion stages, as occurs with the traditional solutions, where are necessary two power converters to integrate the solar photovoltaic system in the power grid and also two power converters to integrate an off-board EV battery charger in the power grid (dc-dc and dc-ac power converters in both cases). Taking into account that the energy provided (or delivered) from the power grid in each moment is function of the EV operation mode and also of the energy produced from the solar photovoltaic system, it is possible to define operation strategies and control algorithms in order to increase the energy efficiency of the global system and to improve the power quality of the electrical system. The proposed multifunctional converter allows the operation in four distinct cases: (a) Transfer of energy from the solar photovoltaic system to the power grid; (b) Transfer of energy from the solar photovoltaic system and from the EV to the power grid; (c) Transfer of energy from the solar photovoltaic system to the EV or to the power grid; (d) Transfer of energy between the EV and the power grid. Along the paper are described the system architecture and the control algorithms, and are also presented some computational simulation results for the four aforementioned cases. It is also presented a comparative analysis between the traditional and the proposed solution in terms of operation efficiency and estimated cost of implementation.

Three-phase three-level current-source converter for EVs fast battery charging systems

This paper presents a three-phase three-level fast battery charger for electric vehicles (EVs) based in a current-source converter (CSC). Compared with the traditional voltage-source converters used for fast battery chargers, the CSC can be seen as a natural buck-type converter, i.e., the output voltage can assume a wide range of values, which varies between zero and the maximum instantaneous value of the power grid phase-to-phase voltage. Moreover, using the CSC it is not necessary to use a dc-dc back-end converter in the battery side, and it is also possible to control the grid current in order to obtain a sinusoidal waveform, and in phase with the power grid voltage (unitary power factor). Along the paper is described in detail the proposed CSC for EVs fast battery charging systems: the circuit topology, the power control theory, the current control strategy and the grid synchronization algorithm. Several simulation results of the EV fast battery charger operating with a maximum power of 50 kW are presented.

A novel architecture of a bidirectional bridgeless interleaved converter for EV battery chargers

This paper presents a novel architecture of a bidirectional bridgeless interleaved converter for battery chargers of electric vehicles (EVs). The proposed converter is composed by two power stages: an ac-dc converter that is used to interface the power grid and the dc-link, and a dc-dc converter that is used to interface the dc-link and the batteries. The ac-dc converter is an interleaved bridgeless bidirectional boost-type converter and the dc-dc converter is a bidirectional buck-boost-type converter. The proposed converter works with sinusoidal grid current and with high power factor for all operating power levels, and in both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operation modes. In the paper is described in detail the proposed converter for EV battery chargers: the circuit topology, the principle of operation, the power control theory, and the current control strategy. Several simulation results for both G2V and V2G operation modes are presented.

A novel concept of unidirectional bridgeless combined boost-buck converter for EV battery chargers

This paper presents a novel concept of unidirectional bridgeless combined boost-buck converter for electric vehicles (EVs) battery chargers. The proposed converter is composed by two power stages: an ac-dc front-end converter used to interface the power grid and the dc-link, and a dc-dc back-end converter used to interface the dc-link and the batteries. The ac-dc converter is a bridgeless boost-type converter and the dc-dc converter is an interleaved buck-type converter. The proposed converter operates with sinusoidal grid current and unitary power factor for all operating power levels. Along the paper is described in detail the proposed converter for EV battery chargers: the circuit topology, the different stages describing the principle of operation, the power control theory, and the current control strategy, for both converters. Along the paper are presented several simulation results for a maximum power of 3.5 kW.

Predictive control of a current-source inverter for solar photovoltaic grid interface

Solar photovoltaic systems are an increasing option for electricity production, since they produce electrical energy from a clean renewable energy resource, and over the years, as a result of the research, their efficiency has been increasing. For the interface between the dc photovoltaic solar array and the ac electrical grid is necessary the use of an inverter (dc-ac converter), which should be optimized to extract the maximum power from the photovoltaic solar array. In this paper is presented a solution based on a current-source inverter (CSI) using continuous control set model predictive control (CCS-MPC). All the power circuits and respective control systems are described in detail along the paper and were tested and validated performing computer simulations. The paper shows the simulation results and are drawn several conclusions.

Three-phase current-source shunt active power filter with solar photovoltaic grid interface

This paper presents the proposal of a three phase current source shunt active power filter (CS-SAPF) with photovoltaic grid interface. The proposed system combines the compensation of reactive power and harmonics with the injection of energy from a solar photovoltaic array into the electrical power grid. The proposed equipment presents the advantage of giving good use to the current source inverter, even when the solar photovoltaic array is not producing energy. The paper describes the control system of the CS SAPF, the energy injection control strategy, and the current harmonics and power factor compensation strategy. Simulation results to assess the performance of the proposed system are also presented.

Parallelization of kinetic Monte Carlo algorithm to simulate AL3Sc precipitation

The present paper reports the precipitation process of Al3Sc structures in an aluminum scandium alloy, which has been simulated with a synchronous parallel kinetic Monte Carlo (spkMC) algorithm. The spkMC implementation is based on the vacancy diffusion mechanism. To filter the raw data generated by the spkMC simulations, the density-based clustering with noise (DBSCAN) method has been employed. spkMC and DBSCAN algorithms were implemented in the C language and using MPI library. The simulations were conducted in the SeARCH cluster located at the University of Minho. The Al3Sc precipitation was successfully simulated at the atomistic scale with the spkMC. DBSCAN proved to be a valuable aid to identify the precipitates by performing a cluster analysis of the simulation results. The achieved simulations results are in good agreement with those reported in the literature under sequential kinetic Monte Carlo simulations (kMC). The parallel implementation of kMC has provided a 4x speedup over the sequential version.

A geographic opportunistic forwarding strategy for vehicular named data networking

Studies in Computational Intelligence, 616

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Implementação de um sistema MPPT em circuito integrado CMOS

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores

Controlador com FPGA para um motor-in-wheel CA de fluxo axial

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e de Computadores

Desenvolvimento de um BMS para aplicações de mobilidade elétrica

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e de Computadores

Seguidor do ponto de máxima potência multi-conversor para sistemas solares fotovoltaicos

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e de Computadores

Modelos de simulação de processos por junção de componentes

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Current Challenges in Modeling Cellular Metabolism

Mathematical and computational models play an essential role in understanding the cellular metabolism. They are used as platforms to integrate current knowledge on a biological system and to systematically test and predict the effect of manipulations to such systems. The recent advances in genome sequencing techniques have facilitated the reconstruction of genome-scale metabolic networks for a wide variety of organisms from microbes to human cells. These models have been successfully used in multiple biotechnological applications. Despite these advancements, modeling cellular metabolism still presents many challenges. The aim of this Research Topic is not only to expose and consolidate the state-of-the-art in metabolic modeling approaches, but also to push this frontier beyond the current edge through the introduction of innovative solutions. The articles presented in this e-book address some of the main challenges in the field, including the integration of different modeling formalisms, the integration of heterogeneous data sources into metabolic models, explicit representation of other biological processes during phenotype simulation, and standardization efforts in the representation of metabolic models and simulation results.