Principal Investigator

Costa Samaras

Status

Completed

Start Date

Sept. 1, 2017

End Date

Aug. 31, 2020

Project Type

Research Applied

Grant Program

FAST Act - Mobility National (2016 - 2022)

Grant Cycle

2017 Mobility21 UTC

Visibility

Public

Abstract

The City of Pittsburgh and cities around the country have communicated a desire to convert a significant portion of their non-emergency vehicle fleet to electric vehicles, and needs to make decisions on where to charge and house a potential fleet of vehicles. At the same time, interest in privately-owned electric vehicles continues to grow. The decision of where to locate the charging infrastructure affects costs, availability and resiliency during an outage. The arrival of partially autonomous technology such as parking valet and other features enables another set of capabilities that the City should consider when planning new infrastructure. For this proposal, we will develop models that estimate the costs and benefits of locating electric vehicles in specific locations depending on specific levels of vehicle automation. Our work will have broad impacts in how cities plan for advanced mobility in the age of automation.    

Description

For this proposal, we will develop models that estimate the costs and benefits of locating electric vehicle charging infrastructure in specific locations depending on specific levels of vehicle automation. For Pittsburgh, and many municipalities across the nation, a challenge has been to identify and secure the financial resources necessary for enabling capital equipment to reduce environmental emissions and energy costs. Approximately one-third of Pittsburgh’s municipal GHG emissions are from City operations, one-third are from emissions from the Housing Authority, with the remainder composed of emissions from the Pittsburgh Water and Sewer Authority, the Urban Redevelopment Authority, and the Parking Authority . Electric vehicles provide the potential for lower operating costs per mile but have higher capital costs. In addition, electric vehicles for municipal use need charging stations to ensure vehicle capabilities during business hours. The City has signaled a desire to convert a significant portion of their non-emergency vehicle fleet to electric vehicles, and needs to make decisions on where to charge and house a potential fleet of vehicles. The decision of where to locate the charging infrastructure affects costs, availability and resiliency during an outage. The arrival of partially autonomous technology such as parking valet and other features enables another set of capabilities that the City should consider when planning new infrastructure for both public and private electric vehicles. These include flexible parking infrastructure and automated wireless charging.

Public charging stations are expensive, and generally have low charging utilization rates when cars remain in the spaces long after charging is complete. Analyses on optimizing alternative fuel and electric vehicle infrastructure are common for many different sets of criteria. A research gap remains on assessing how higher levels of vehicle automation can change these results. Automation enables a potential increase in charger utilization and reduction in the spatial limitations of where vehicles charge. Additionally, it may give more control over timing and location of charging demand than traditionally-driven vehicles would allow. This research investigates these potential effects by analyzing the following research question: What are potential electric vehicle charging infrastructure siting efficiencies and associated energy and environmental impacts from level 4 and level 5 automation?

In this work, we will create a demand model of municipal EV travel and optimal charging locations. Our work will have broad impacts in how the City plans for advanced mobility in municipal operations.

•	Task 1: Review of literature on electric vehicle charging station location planning 
•	Task 2: Create demand model of municipal EV travel and charging locations
•	Task 3: Create final report, briefings and journal papers

Timeline

Review of literature on electric vehicle charging station location planning: Q1 and Q2
Create demand model of EV travel and charging location: Q2-Q4
Create final report, briefings and journal paper: Q4-Q6

Strategic Description / RD&T

Deployment Plan

The project analyses support the planning of electric vehicle charging infrastructure in cities under various automated vehicle deployment futures.

Expected Outcomes/Impacts

We expect to develop a report and several presentations to City stakeholders over the course of the project, as well as attend numerous stakeholder meetings and listening sessions with the City. We will also publish several papers in peer-reviewed journals and conferences. 

Expected Outputs

TRID

Individuals Involved

Email	Name	Affiliation	Role	Position
csamaras@cmu.edu	Samaras, Costa	CEE	PI	Faculty - Untenured, Tenure Track

Budget

Amount of UTC Funds Awarded

$100000.00

Total Project Budget (from all funding sources)

$100000.00

Documents

Type	Name	Uploaded
Data Management Plan	Samaras_Data_Management_Plan_UTC.pdf	March 12, 2019, 1:19 p.m.
Presentation	202_Samaras_Parking_Slides.pptx	March 12, 2019, 1:30 p.m.
Presentation	2019-06_Samaras_Mersky_NBER.pdf	Dec. 17, 2020, 8:32 p.m.
Presentation	2019-11-21_Travelers.pdf	Dec. 17, 2020, 8:32 p.m.
Publication	Mersky_and_Samaras_Parking_Working_Paper.pdf	Dec. 17, 2020, 8:05 p.m.
Presentation	MerskySamaras_Poster_ASCE_2018.pdf	Dec. 17, 2020, 8:32 p.m.
Presentation	The Transition to Electrified Transportation	Dec. 17, 2020, 8:32 p.m.
Presentation	The Impact of Automated Vehicles on Cities	Dec. 17, 2020, 8:32 p.m.
Presentation	Will Electric and Driverless Cars Decarbonize Transportation?	Dec. 17, 2020, 8:32 p.m.
Presentation	Energy and Emissions Impacts of Automated Vehicles Under Uncertainty	Dec. 17, 2020, 8:32 p.m.
Final Report	M21_Final_Report_202.pdf	Dec. 18, 2020, 5:33 a.m.

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