Analysis of Fast Charging Station Network for Electrified Ride-Hailing Services

2018-01-0667

04/03/2018

Features
Event
WCX World Congress Experience
Authors Abstract
Content
Today’s electric vehicle (EV) owners charge their vehicles mostly at home and seldom use public direct current fast charger (DCFCs), reducing the need for a large deployment of DCFCs for private EV owners. However, due to the emerging interest among transportation network companies to operate EVs in their fleet, there is great potential for DCFCs to be highly utilized and become economically feasible in the future. This paper describes a heuristic algorithm to emulate operation of EVs within a hypothetical transportation network company fleet using a large global positioning system data set from Columbus, Ohio. DCFC requirements supporting operation of EVs are estimated using the Electric Vehicle Infrastructure Projection tool. Operation and installation costs were estimated using real-world data to assess the economic feasibility of the recommended fast charging stations. Results suggest that the hypothetical transportation network company fleet increases daily vehicle miles traveled per EV with less overall down time, resulting in increased demand for DCFC. Sites with overhead service lines are recommended for hosting DCFC stations to minimize the need for trenching underground service lines. A negative relationship was found between cost per unit of energy and fast charging utilization, underscoring the importance of prioritizing utilization over installation costs when siting DCFC stations. Although this preliminary analysis of the impacts of new mobility paradigms on alternative fueling infrastructure requirements has produced several key results, the complexity of the problem warrants further investigation.
Meta TagsDetails
DOI
https://doi.org/10.4271/2018-01-0667
Pages
13
Citation
Wood, E., Rames, C., Kontou, E., Motoaki, Y. et al., "Analysis of Fast Charging Station Network for Electrified Ride-Hailing Services," SAE Technical Paper 2018-01-0667, 2018, https://doi.org/10.4271/2018-01-0667.
Additional Details
Publisher
Published
Apr 3, 2018
Product Code
2018-01-0667
Content Type
Technical Paper
Language
English