The promotion of Electric Vehicles (EV) has become a key measure of the governments to reduce greenhouse gas emissions. However, range anxiety is a big barrier for drivers to choose EVs over traditional vehicles. Installing more charging stations in appropriate locations can relieve EV drivers’ range anxiety. To help decide the location and number of public charging stations, we propose two optimization models for two different charging modes - fast and slow charging, which aim at minimizing the total cost while satisfying certain spatial coverage goals. Instead of using discrete points we employ network and polygons to represent charging demands. Importantly, we resolve the partial coverage problem (PCP) by segmenting the geometric objects into smaller ones using Geographic Information System (GIS) functions. We compare the geometric segmentation method (GS) and the complementary partial coverage method (CP) developed by Murray (2005) to solve the PCP. After applying the models to Greater Toronto and Hamilton Area (GTHA) and to Downtown Toronto, we show that that the proposed models are practical and effective in determining the locations and number of required charging stations. Moreover, comparison of the two methods shows that GS can fully eliminate PCP and provide much more accurate result than CP. / Thesis / Master of Science (MSc)

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-04-24T19:36:29Z No. of bitstreams: 1 lucascorreanettomachado.pdf: 1851458 bytes, checksum: 2a8b67f0adf8343c28d4e1121a757f6d (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-04-25T15:23:10Z (GMT) No. of bitstreams: 1 lucascorreanettomachado.pdf: 1851458 bytes, checksum: 2a8b67f0adf8343c28d4e1121a757f6d (MD5) / Made available in DSpace on 2017-04-25T15:23:10Z (GMT). No. of bitstreams: 1 lucascorreanettomachado.pdf: 1851458 bytes, checksum: 2a8b67f0adf8343c28d4e1121a757f6d (MD5) Previous issue date: 2013-11-22 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho apresenta uma técnica para treinamento de Redes Neurais Artificiais (RNA), capaz de obter os parâmetros da rede através dos dados disponíveis para treinamento, sem necessidade de estabelecer a arquitetura da rede a priori, denominado Método de Segmentações Geométricas Sucessivas (MSGS). O MSGS agrupa os dados de cada classe em Hipercaixa (HC) onde cada caixa é alinhada de acordo com os eixos de maior distribuição de seu conjunto de pontos. Sendo as caixas linearmente separáveis, um hiperplano de separação é identificado originando um neurônio. Caso não seja possível a separação por um único hiperplano, uma técnica de quebra é aplicada para dividir os dados em classes menores para obter novas HCs. Para cada subdivisão novos neurônios são adicionados à rede. Os resultados dos testes realizados apontam para um método rápido e com alta taxa de sucesso. / This work presents a technique for Artificial Neural Network (ANN) training, able to get the network parameters from the available data for training, without establishing the network architecture a priori, called Successive Geometric Segmentation Method (SGSM). The SGSM groups the data of each class into hyperboxes (HB) aligned in accordance with the largest axis of its points distribution. If the HB are linearly separable, a separating hyperplane may be identified resulting a neuron. If it is not, a segmentation technique is applied to divide the data into smaller classes for new HB. For each subdivision new neurons are added to the network. The tests show a rapid method with high success rate.