A comprehensive study of key Electric Vehicle (EV) components, technologies, challenges, impacts, and future direction of development

Abstract: Electric vehicles (EV), including Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV), Plug-in Hybrid Electric Vehicle (PHEV), Fuel Cell Electric Vehicle (FCEV), are becoming more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace internal combustion engine (ICE) vehicles in the near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system could face huge instabilities with enough EV penetration, but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of the smart grid concept. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emissions produced by the transportation sector. However, there are some major obstacles for EVs to overcome before totally replacing ICE vehicles. This paper is focused on reviewing all the useful data available on EV configurations, battery energy sources, electrical machines, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector

Compensação digital de distorções da fibra em sistemas de comunicação óticos de longa distância

The continuous increase of traffic demand in long-haul communications motivated the network operators to look for receiver side techniques to mitigate the nonlinear effects, resulting from signal-signal and signal-noise interaction, thus pushing the current Capacity boundaries. Machine learning techniques are a very hot-topic with given proofs in the most diverse applications. This dissertation aims to study nonlinear impairments in long-haul coherent optical links and the current state of the art in DSP techniques for impairment mitigation as well as the integration of machine learning strategies in optical networks. Starting with a simplified fiber model only impaired by ASE noise, we studied how to integrate an ANN-based symbol estimator into the signal pipeline, enabling to validate the implementation by matching the theoretical performance. We then moved to nonlinear proof of concept with the incorporation of NLPN in the fiber link. Finally, we evaluated the performance of the estimator under realistic simulations of Single and Multi- Channel links in both SSFM and NZDSF fibers. The obtained results indicate that even though it may be hard to find the best architecture, Nonlinear Symbol Estimator networks have the potential to surpass more conventional DSP strategies.O aumento contínuo de tráfego nas comunicações de longo-alcance motivou os operadores de rede a procurar técnicas do lado do receptor para atenuar os efeitos não lineares resultantes da interacção sinal-sinal e sinal-ruído, alargando assim os limites da capacidade do sistema. As técnicas de aprendizagem-máquina são um tópico em ascenção com provas dadas nas mais diversas aplicações e setores. Esta dissertação visa estudar as principais deficiências nas ligações de longo curso e o actual estado da arte em técnicas de DSP para mitigação das mesmas, bem como a integração de estratégias de aprendizagem-máquina em redes ópticas. Começando com um modelo simplificado de fibra apenas perturbado pelo ruído ASE, estudámos como integrar um estimador de símbolos baseado em ANN na cadeia do prodessamento de sinal, conseguindo igualar o desempenho teórico. Procedemos com uma prova de conceito perante não linearidades com a incorporação do ruído de fase não linear na propagação. Finalmente, avaliamos o desempenho do estimador com simulações realistas de links Single e Multi canal tanto em fibras SSFM como NZDSF. Os resultados obtidos indicam que apesar da dificuldade de encontrar a melhor arquitectura, a estimação não linear baseada em redes neuronais têm o potencial para ultrapassar estratégias DSP mais convencionais.Mestrado em Engenharia Eletrónica e Telecomunicaçõe