Charging Electric Vehicles 

in Smart Cities:

An EVI-Pro Analysis of Columbus, Ohio

Eric Wood, Clément Rames, Matteo Muratori, Sesha Raghavan, and Stanley Young

June 13, 2018

Electric Power Research Institute 

Infrastructure Working Council
$40 MILLION

78 APPLIED • COLUMBUS WON
NREL | 3

NREL’s Infrastructure Analysis

NREL analyzed charging behavior and infrastructure requirements to 

support plug-in electric vehicle (PEV) adoption in Columbus, OH, including 

estimating PEV supply equipment counts, location, use, and resulting hourly 

load profiles 
NREL | 4

Smart Columbus: Goals

As part of the Smart Columbus Initiative, the city has set specific goals for 

annual PEV sales:

• Approximately 91,500 light-duty vehicles (LDVs) are sold per year in Columbus

• The Columbus goal translates to 3,200 new PEVs registered in Columbus over 

three years, bringing the 2019 Columbus PEV fleet to 5,300 vehicles

Year PEV Sales Goal

2017 0.6%

2018 1.1%

2019 1.8%
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Analysis Approach

• Use NREL’s Electric Vehicle Infrastructure Projection (EVI- Pro) model to:

 Generate scenarios of regional charging infrastructure based on 

regional travel to support PEV adoption in line with Smart Columbus

 Anticipate future demand for PEV charging to better inform the 

impact of PEV adoption on the electric load

• GPS travel data

• Assess current PEV market

• Results: electric vehicle supply equipment (EVSE) requirements

• Sensitivity analysis

• Promising locations for public EVSE
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EVI-Pro

The Electric Vehicle Infrastructure Projection (EVI-Pro) tool estimates PEV 

charging requirements and charging load profiles

PEV 

Driving/Charging 

Simulator

PHEVs & BEVs 

Attributes

Home/Work/Public

L1/L2/DCFC

Real-World 

GPS Data

PEV Charging 

Infrastructure

Future PEV 

Stock

(exogenous)

Foundational Assumptions

• Future PEVs will be driven in a manner 

consistent with today’s gasoline vehicles

• Consumers prefer to perform the majority 

of charging at their home location

• Charging at non-residential stations will 

be used to maximize eVMT
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EVI-Pro

Destination Departure Arrival

Drive 

Miles

Dwell 

Hours

Simulated

Charging

Work 8:20 AM 9:00 AM 32.8 5.00 L2

Non-Res 2:00 PM 3:30 PM 68.9 0.25 ---

Non-Res 3:45 PM 4:00 PM 6.3 0.25 ---

Non-Res 4:15 PM 4:20 PM 0.9 0.67 DCFC

Non-Res 5:00 PM 5:30 PM 9.2 0.25 ---

Non-Res 5:45 PM 6:00 PM 5.0 0.50 ---

Home 6:30 PM 7:30 PM 46.8 12.83 L1

DCFC

L2-Work

L1-Home

Bottom-up simulations based on travel behavior 

are used to produce a variety of charging scenarios

Optimal charging behavior is assumed to 

investigate spatial and temporal charging demand 

and to estimate:

• Non-residential infrastructure requirements 

• Aggregate load profiles

Simulated charging 

behavior for a BEV100 

under an example travel 

day

L1-Home
NREL | 8

National Infrastructure Analysis

Source: Wood et al. 2017. Model: NREL’s EVI-Pro

NREL analyzed national charging behavior and 

infrastructure requirements to support PEV adoption, 

including interstate corridors
NREL | 9

GPS Travel Data

To properly model PEV charging infrastructure requirements in Columbus, 

NREL acquired individual GPS travel trajectories from INRIX 

Each travel trajectory features trip-level data such as start and end times 

and GPS coordinates (including origin, destination, and intermediate 

waypoints) 

• 7.82 million unique device identifiers

• 32.9 million trips 

• 1.04 billion miles of driving

• 2.58 billion GPS waypoints
NREL | 10

GPS Travel Data

INRIX travel data for Columbus are compared to traditional travel surveys to 

check for consistency and compare trends 

As additional validation, the 

INRIX data are compared to 

estimated trip counts by traffic 

analysis zone (TAZ) from the MidOhio Regional Planning 

Commission’s (MORPC) 2015 

travel demand model showing 

good agreement
NREL | 11

Current LDV Market

The Columbus region is defined 

as the seven-county area 

surrounding the City of 

Columbus

LDV in Columbus:

o ~ 1.70 million vehicles 

o ~91,500 sales per year

o Currently dominated by ICE 

vehicles (98%) with spatial 

distribution roughly mirroring 

population
NREL | 12

Current PEV Market

• Only 2,100 PEVs in Columbus

• Columbus PEV preference 

consistent with Ohio

• Relatively PHEV dominant 

(2/3 of PEVs compared to 

~1/2 at the national level) 

• Clustering effects in PEV 

adoption assumed in line with 

historical HEV adoption
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Baseline Scenario

Baseline assumptions:

• 5,300 PEVs on the road by the end of 2019 

• 54:46 PHEV/BEV split (national average), evenly split between short- and long-range

• Spatial PEV adoption in line with existing HEVs 

• Full support for PHEV charging

• Mild ambient temperature (typical of May in Columbus)

• Consumers in both single-unit dwellings (SUDs) and multi-unit dwellings (MUDs) 

have access to home charging and prefer to do the majority of charging at home 

– SUD: one plug per PEV, split evenly between L1 and L2

– MUD: one L2 plug per PEV
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Results: Plug Counts

Estimated plug counts for Columbus by the end of 2019: 

Delaware Fairfield Franklin Licking Madison Pickaway Union Total

SUD L1 319 152 1,622 182 36 39 74 2,424

SUD L2 313 147 1,448 164 32 44 75 2,222

MUD L2 27 15 327 18 4 5 8 404

Work L2 29 12 70 13 3 8 3 138

Public L2 31 13 146 13 2 4 7 217

DCFC 4 1 7 0 0 0 1 13
NREL | 15

Results: Charging Profiles

• Majority of charging between 

4 p.m. and 12 a.m. at home 

• Workplace charging peaks 

around 8 a.m. for the PHEV20

• Longer range vehicles do not 

require workplace charging

• Public L2 charging is used 

consistently throughout the day 

by PHEVs (maximizing eVMT)

• DCFC demand is modest for the 

BEV100 and almost nonexistent 

for longer-range BEVs, since we 

focus on local travel 
NREL | 16

Sensitivity Analysis

• Consumer preferences (PHEV vs. 

BEV, range) have the largest 

influence on infrastructure 

requirements 

• Spatial disaggregation of PEV 

adoption will largely affect the 

EVSE geographical distribution

• Ambient temperature is known 

to drastically affect the energy 

consumption of PEVs. Winter 

conditions are harsher in 

Columbus
NREL | 17

Promising Locations for Public L2

• Purple outline: Columbus area 

• Blue pentagons: existing L2 EVSE 

• Green stars: future sites under 

consideration by local planners 

• Dots: simulated PEV charging “hot 

spots” for L2 public charging (0.3-

mi diameter) color coded by tier 

(1st tier = red, 2nd tier = orange, 

3rd tier = yellow) 
NREL | 18

Promising Locations for Public DCFC

• Purple outline: Columbus area. 

• Blue dots: Sixteen existing DCFC 

station locations in Columbus as 

of August 2017 

• Red dots: 13 hypothetical future 

locations to improve DCFC 

coverage to support 5,300 PEVs 

in 2019
NREL | 19

Conclusions

• Guide PEV charging infrastructure deployment to reduce range anxiety 

and ensure the effective use of private/public investments

• Assuming ubiquitous residential charging (including multi-unit dwellings) 

approximately 400 MUD Level 2 plugs, 350 non-residential Level 2 plugs, 

and 13 DCFC plugs are required to support Columbus’ primary PEV goal 

of 5,300 PEVs on the road by the end of 2019

• While consumer demand for fast charging is expected to remain low

(due to modest anticipated adoption of short-range battery electric 

vehicles and ubiquitous residential charging), a minimum level of fast 

charging coverage is required to ease consumer range anxiety 
NREL | 20

EVI-Pro Lite

NREL released EVI-Pro Lite to provide a simple way to estimate how much 

electric vehicle charging might be needed need at a city and state level
NREL | 21

Ongoing Research and 

Collaboration Opportunities

Research at NREL:

• Assess opportunities to leverage PEV charging flexibility to support grid 

operation and facilitate renewable integration (demand response)

• Assess the impact of non-residential PEV charging on the power system, 

especially DC fast charging

• Better capture the infrastructure implications of transportation 

electrification, including: 

– The “PEV adoption–EVSE availability” nexus for light-duty vehicles 

– Electrification strategies for different medium- and heavy-duty vocations

– Impact of automated vehicles, future mobility options, and transportation network 

companies (TNCs)
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Acknowledgements

This study was supported by the U.S. Department of Energy’s Vehicle Technologies Office. The authors would 

specifically like to thank Michael Berube, Robert Graham, Jacob Ward, Rachael Nealer, David Gohlke, and Kelly 

Fleming for their guidance and support. Additional thanks to members of the Smart Columbus working group on EV 

charging infrastructure. Their feedback was invaluable in tailoring this analysis to the mid-Ohio region.

Please cite as: Wood, Eric, Clément Rames, Matteo Muratori, Sesha Raghavan, and Stanley Young. 2018. “Charging 

Electric Vehicles in Smart Cities: An EVI-Pro Analysis of Columbus, Ohio.” Golden, CO: National Renewable Energy 

Laboratory. NREL/PR-5400-71776. URL: https://www.nrel.gov/docs/fy19osti/71776.pdf. 

References:

• Wood, E., C. Rames, M. Muratori, S. Raghavan, and S. Young. 2018. Charging Electric Vehicles in Smart Cities: An EVI-Pro 

Analysis of Columbus, Ohio. https://www.nrel.gov/docs/fy18osti/70367.pdf

• Wood, E., C. Rames, M. Muratori, S. Srinivasa Raghavan, and M. Melaina. 2017. National Plug-In Electric Vehicle 

Infrastructure Analysis. https://www.nrel.gov/docs/fy17osti/69031.pdf

• Electric Vehicle Infrastructure Projection Tool (EVI-Pro) Lite

• Matteo Muratori.2018. “Electrification Opportunities in the Transportation Sector and Impact of Residential Charging.” 

https://www.nrel.gov/docs/fy18osti/71102.pdf
www.nrel.gov

This work was authored by the National Renewable Energy Laboratory, operated by the Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by 

U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Vehicle Technologies Office. The views 

expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. 

Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. 

Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published 

form of this work, or allow others to do so, for U.S. Government purposes.

Thank you

Matteo.Muratori@NREL.gov

NREL/PR-5400-71776
NREL | 24

Charging Requirements

While the majority of PEV charging is expected 

to come from residential plugs, a network of 

non-residential chargers is still required to: 

• Support adopters that cannot reliably charge 

at home

• Enable long-distance travel 

• Cope with range anxiety (safety net)

Infrastructure plays a big role in 

enabling and supporting PEV adoption

(dynamic charging or battery swapping 

also have big infrastructure components) Source: National Research Council. Overcoming barriers to deployment 

of plug-in electric vehicles. National Academies Press, 2015.
NREL | 25

EVI-Pro: Conceptual

Consumers’ demand for PEV charging is 

coverage-based: 

“Need access to charging anywhere their 

travels lead them”

Infrastructure providers make capacitydriven investments:

“Increase supply of stations proportional 

to utilization”

A “utilization gap” persists in a low vehicle density environment making it difficult to justify 

investment in new stations when existing stations are poorly utilized (aka: chicken and egg)

We quantify non-residential PEV charging requirements necessary to meet consumer 

coverage expectations (independent of PEV adoption level) and capacity necessary to meet 

consumer demand in high PEV adoption scenarios
NREL | 26

PEV Sales Distribution
NREL | 27

PEV Sales Distribution
NREL | 28

L1 Vs. L2 Charging

With 12% of the population of the United States, California has 24% of the public PEV 

charging stations and 30% of the outlets for charging PEVs . 

159 BEV owners and 156 PHEV owners responded to questions in the 2016 California 

Vehicle Survey about where and when they charged their vehicles on a typical weekday
NREL | 29

Rebound Peaks

Widespread participation (automated energy management systems) in 

demand response programs using time-varying electricity pricing (e.g., TOU) 

might create pronounced rebound peaks.

M. Muratori and G. Rizzoni. 2016. “Residential demand response: dynamic energy management and time-varying electricity pricing.” IEEE Trans. 

on Power Systems, Vol. 31 (2). 10.1109/TPWRS.2015.2414880
NREL | 30

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Modeling

ADOPT

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