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

Planar electromagnetic bandgap structures and applications

The thesis concerns the planar EBG structures in the forms of conventional circular and rectangular photonic bandgap structures (PBGSs) and defected ground structures (DGSs). Novel PBGSs in the form of non-uniform Binomial and Chebyshev distributions of unit PBG cells have been proposed.DOCTOR OF PHILOSOPHY (EEE

Emotion recognition from EEG-based relative power spectral topography using convolutional neural network

Emotion recognition, a challenging computational issue, finds interesting applications in diverse fields. Usually, feature-based machine-learning methods have been used for emotion recognition. However, these conventional shallow machine learning methods often find unsatisfactory results as there is a tradeoff between feature dimensions and classification accuracy. Besides, extraction and selection of features from the spatial and frequency domains could be an additional issue. This work proposes a method that transforms EEG (electroencephalography) signals to topographic images that contain the frequency and spatial information and utilizes a convolutional neural network (CNN) to classify the emotion, as CNN has improved feature extraction capability. According to the proposed method, the topographic images are prepared from the relative power spectral density rather than power spectral density that shows remarkable improvement in classification accuracy. The proposed method is applied to the well-known SEED database and has given outperforming results than the current state-of-the-art