Need for strategic planning of electric vehicle charging locations in Windsor, Ontario

Abstract

The paper evaluates the impact of the new additional electric vehicle (EV) charging stations on accessibility to charging in the city of Windsor Ontario. It uses locations and coordinates information of electric vehicle charging stations extracted from the Environmental Canada database combined with details of the new additions by the city, manually abstracted from publications and google. These were spatially aggregated into the city’s Ward along with demographic and stations-per-Ward determined.Point-in-polygon, floating catchment area - FCA, and network level (travel-time) methods were used to understand variations in accessibility across Wards of the city, considering the existing and a combination of existing and new stations. The study found that the point-in-polygon method tends to overestimate the accessibility of Wards with a low population where kinetic (affluents) resides and could lead to improper identification of Wards highly accessible to charging. Findings from FCA and network accessibility methods highlights caution with the use of the point-in-polygon method which favors low-populated areas. Accessibility analysis using FCA, and network level (travel-time) technique revealed that the addition of new stations in the city does not significantly change accessibility levels. The study found that only a few Wards in the city of Windsor will be benefited from the addition of new stations as the median travel times from Wards to stations did not reduce significantly and larger variations exist around the median times suggesting that the new stations are randomly introduced rather than planned. Multicriteria analysis however aided in ranking of Wards based on available charging infrastructures, EV owned, travel time to stations and population density per Ward. The paper recommends data-driven machine learning approach in measuring accessibility and locating charging infrastructures in the city.