R. Zenon

Date of Award

3-19-2023

Document Type

Thesis - SCU Access Only

Publisher

Santa Clara : Santa Clara University, 2023.

Department

Electrical and Computer Engineering

First Advisor

Ahmed Amer

Abstract

With the increased awareness of our need to completely transition to green modes of transportation comes the more complicated issue of building new infrastructure to sustain green transportation. Part of this involves making decisions on where to invest heavily while still ensuring everyone involved has equal access. Being able to know where funding should be directed is only able to be understood once there is knowledge of where the infrastructure already exists, is most prevalent and well maintained. This creates the need to provide a method for visualizing existing EV infrastructure to effectively create investment and construction plans to build necessary infrastructural improvements and additions.

VEVI is a tool that takes electric vehicle charging station location data to help give users a better understanding of their current and potential EV infrastructure. Unlike many other EV infrastructure mapping tools, VEVI was designed to be able to be used independently with the potential to be integrated into other tools and web pages. The user inputs their own GIS location data to feed into one of the three Python based methods of their choosing to have tailored heatmap covering the geographic data given. Future recommendations include integrating VEVI into other visualization tools and allowing mixed GIS location data inputs.

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