NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Technical Report

NREL/TP-5400-71198 

February 2019 

Meeting 2025 Zero Emission Vehicle 

Goals: An Assessment of Electric 

Vehicle Charging Infrastructure in 

Maryland

Matthew Moniot, Clément Rames, and Eric Wood

National Renewable Energy Laboratory
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Technical Report

NREL/TP-5400-71198 

February 2019 

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Meeting 2025 Zero Emission Vehicle 

Goals: An Assessment of Electric 

Vehicle Charging Infrastructure in 

Maryland

Matthew Moniot, Clément Rames, and Eric Wood

National Renewable Energy Laboratory

Suggested Citation

Moniot, Matthew, Clément Rames, and Eric Wood. 2019. Meeting 2025 Zero Emission 

Vehicle Goals: An Assessment of Electric Vehicle Charging Infrastructure in Maryland. 

Golden, CO: National Renewable Energy Laboratory. NREL/TP-5400-71198.

https://www.nrel.gov/docs/fy19osti/71198.pdf.
NOTICE

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

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

provided by Potomac Power Company under agreement FIA-18-1870. The views expressed herein do not 

necessarily represent the views of the DOE or the U.S. Government.

This report is available at no cost from the National Renewable 

Energy Laboratory (NREL) at www.nrel.gov/publications. 

U.S. Department of Energy (DOE) reports produced after 1991 

and a growing number of pre-1991 documents are available 

free via www.OSTI.gov. 

Cover Photos by Dennis Schroeder: (clockwise, left to right) NREL 51934, NREL 45897, NREL 42160, NREL 45891, NREL 48097, 

NREL 46526.

NREL prints on paper that contains recycled content. 
iii

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Acknowledgments

This study was funded through an agreement between the National Renewable Energy 

Laboratory (NREL) and Potomac Electric Power Company. The authors would specifically like 

to thank Robert Stewart and Douglas Micheel for their support. Additional thanks to Marissa 

Gillett (formerly of the Maryland Public Services Commission) for her efforts in coordinating 

the partnership between NREL and Potomac Electric.

The Electric Vehicle Infrastructure Projection (EVI-Pro) model used in this study was developed 

in collaboration with the California Energy Commission. Special thanks to Kadir Bedir, Noel 

Crisostomo, Jennifer Allen, and Charles Smith for their support of EVI-Pro. Additional 

development has also been supported by the U.S. Department of Energy’s Vehicle Technologies 

Office. The authors would specifically like to thank Michael Berube, Jacob Ward, Rachael 

Nealer, and Kelly Fleming for their support. 

Global positioning system (GPS) travel trajectories underlying this analysis have been provided 

by INRIX and made available to NREL through an agreement with the Maryland State Highway 

Administration and the University of Maryland’s Center for Advanced Transportation 

Technology. Special thanks to Rick Schuman (INRIX) and Thomas Jacobs (University of 

Maryland) for facilitating this agreement.

Reviews conducted by the Electric Power Research Institute (EPRI), the University of Texas at 

Austin, and the University of Washington were critical in ensuring a high level of quality in this 

analysis. Special thanks to Jamie Dunckley, Kara Kockelman, and Don MacKenzie respectively.
iv

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

List of Acronyms

BEV battery electric vehicle

BEVxx battery electric vehicle with a range of xx miles

DCFC direct current fast charge

EVI-Pro Electric Vehicle Infrastructure Projection Tool 

GPS global positioning system 

HEV hybrid electric vehicle

IHS IHS Markit

kW kilowatt

L1 Level 1 charging station

L2 Level 2 charging station

LDV light-duty vehicle 

MUD multiple-unit dwelling

NREL National Renewable Energy Laboratory

PEV plug-in electric vehicle (BEV or PHEV)

PHEV plug-in hybrid electric vehicle

PHEVxx plug-in hybrid electric vehicle with a range of xx miles

SUD single-unit dwelling

VMT vehicle miles travelled
v 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Table of Contents

List of Acronyms........................................................................................................................................ iv

List of Figures ............................................................................................................................................ vi

List of Tables .............................................................................................................................................. vi

Executive Summary.................................................................................................................................. vii

1 Introduction........................................................................................................................................... 1

2 Maryland Existing Light-Duty Vehicle Stock and Public Charging Networks................................ 2

2.1 Maryland Existing Light-Duty Vehicle Stock ............................................................................... 3

2.2 Maryland Existing Public Charging Networks ............................................................................. 5

2.3 Baseline PEV Scenario: 300,000 in 2025 ..................................................................................... 7

3 INRIX GPS Travel Trajectories ............................................................................................................ 9

3.1 Data Summary and Visualization .................................................................................................. 9

3.2 Down Sampling and Data Processing ........................................................................................... 9

3.3 Data Validation ........................................................................................................................... 11

4 EVI-Pro Methodology ......................................................................................................................... 13

4.1 EVI-Pro Model Description ........................................................................................................ 13

4.2 Simulated PEV Attributes ........................................................................................................... 15

4.3 Simulated Infrastructure Attributes ............................................................................................. 16

5 Simulation Results ............................................................................................................................. 17

5.1 Baseline Results .......................................................................................................................... 17

5.2 Work Charger Availability Adjustment ...................................................................................... 18

5.3 Sensitivity Analysis ..................................................................................................................... 18

5.4 Simulated Charging Loads, Baseline .......................................................................................... 23

5.5 Simulated Charging Loads, Time of Use Response .................................................................... 25

6 Summary ............................................................................................................................................. 27

References................................................................................................................................................. 28

Appendix A. Existing Plug Counts by Utility Service Territory and County....................................... 30

Appendix B. Maryland Housing Stock by Size and Ownership Status (with NREL Assumptions 

Regarding Potential Availability of Home Charging)...................................................................... 31

Appendix C. PEV Projections by County................................................................................................ 32

Appendix D. Electric Vehicle Supply Equipment Projections by County ........................................... 33

Appendix E. Select Load Profiles from Sensitivity Analysis................................................................ 34
vi 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

List of Figures

Figure 1. Maryland utility service territories ................................................................................................ 2

Figure 2. Maryland counties considered in this study................................................................................... 3

Figure 3. LDVs registered in Maryland as of the end of 2016 by fuel type ................................................. 3

Figure 4. Maryland LDV registrations by ZIP code ..................................................................................... 4

Figure 5. Maryland PEV registrations by make/model ................................................................................. 4

Figure 6. Existing public PEV charging infrastructure in Maryland ............................................................ 5

Figure 7. Existing public PEV charging plug counts in Maryland by electric utility ................................... 6

Figure 8. Maryland combined PEV and HEV registrations by ZIP code. .................................................... 7

Figure 9. Trip-destination frequency map derived from the INRIX travel data set .................................... 10

Figure 10. Distribution of daily VMT from processed INRIX data and additional GPS datasets. ............. 12

Figure 11. Distribution of trips by time of day from processed INRIX data and additional GPS datasets. 12

Figure 12. Graphical representation of inputs/outputs and data flow in EVI-Pro ....................................... 13

Figure 13. Results of parametric sensitivity analysis for non-residential (work and public) L2 plugs (top) 

and fast charging (bottom). .................................................................................................... 21

Figure 14. Aggregate statewide load profiles for a weekday, baseline ....................................................... 23

Figure 15. Statewide load profiles by charging type for a weekday, separated by residence type ............. 24

Figure 16. Statewide load profiles by charging type for a weekday, separated by vehicle type. The 

vehicles shown belong to SUD residents. .............................................................................. 25

Figure 17. Aggregate statewide load profiles for a weekday with baseline assumptions and an applied 

TOU rate. ............................................................................................................................... 26

Figure E1. Aggregate statewide weekday load profiles with 30% MUD share. Top: No residential 

charging at MUDs. Bottom: L2 residential charging available at MUDs. ............................. 34

Figure E2. Aggregate statewide weekday load profiles. Top: 4:1 PHEV:BEV ratio. Bottom: 1:4 

PHEV:BEV ratio. ................................................................................................................... 35

Figure E3. Aggregate statewide weekday load profiles. Top: Short-range PEV scenario. Bottom: Longrange PEV scenario. ............................................................................................................... 36

List of Tables

Table 1. Maryland Utility Service Territories Considered in this Study by Reporting Aggregation Level.. 2

Table 2. Maryland PEV registrations by utility service territory .................................................................. 5

Table 3. Projected 2025 PEV Counts by Type and Utility Service Territory ............................................... 8

Table 4. Comparison of daily VMT between urban and rural areas ........................................................... 11

Table 5. Modeled PEV Attributes ............................................................................................................... 15

Table 6. Charging Options Available to Consumers in EVI-Pro ................................................................ 16

Table 7. Estimated Plug Counts by Utility Service Territory ..................................................................... 17
vii 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Executive Summary

In 2017 the Maryland Public Services Commission established the PC44 working group on 

electric vehicles to assess the potential for the state’s electric utilities to adopt an electric vehicle 

portfolio, which would include mechanisms for electric utilities to potentially finance, install, 

and operate charging infrastructure within Maryland. To this end, a motion was proposed before 

the Maryland Public Services Commission in January of 2018 titled “In the Matter of the Petition 

of the Electric Vehicle Work Group for Implementation of a Statewide Electric Vehicle 

Portfolio” (case number 9478). As part of this proposal, the National Renewable Energy 

Laboratory (NREL) has been enlisted to conduct a statewide assessment of the electric vehicle 

charging infrastructure requirements for Maryland to meet its goal of supporting 300,000 zero 

emission vehicles by 2025. 

NREL’s Electric Vehicle Infrastructure Projection Tool (EVI-Pro) was used to generate 

scenarios of statewide charging infrastructure to support consumer PEV adoption based on travel 

patterns provided by INRIX (a commercial mapping/traffic company) that are used to 

characterize regional travel in Maryland and anticipate future demand for PEV charging. This 

report provides guidance on PEV charging infrastructure to stakeholders in Maryland to reduce 

range anxiety as a barrier to PEV sales and ensure the effective use of private/public investments 

in PEV charging infrastructure.

Results indicate that significant expansion of Maryland's electric vehicle charging infrastructure 

will be required to support the state’s PEV goal for 2025. Analysis shows that a fleet of 300,000 

PEVs will require 17,400 workplace Level 2 plugs, 9,300 public Level 2 plugs, and 1,000 fast 

charge plugs. These estimates assume that future PEVs will be driven in a manner consistent 

with present day gasoline vehicles and that the majority of charging will happen at residential 

locations. A sensitivity study explores edge cases pertaining to several assumptions, highlighting 

factors that heavily influence the projected infrastructure requirements. Variations in the makeup 

of the PEV fleet, evolving consumer charging preferences, and availability of residential 

charging are all shown to influence 2025 infrastructure requirements. 
1 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

1 Introduction

As a member of the Multi-State Zero Emission Vehicle Task Force, Maryland has adopted a 

public goal of supporting 300,000 plug-in electric vehicles (PEVs) within the state by 2025 

(O'Malley et al. 2013). Successfully meeting this goal will provide the state with widely 

recognized benefits, including lower vehicle operational and maintenance costs for consumers, 

reduced greenhouse gas emissions, improved air quality, reduced dependence on petroleum, and 

potential positive synergies with the electric grid (Melaina et al. 2016).

Meeting Maryland’s Zero Emission Vehicle goal will require coordination among many 

stakeholders, including consumers, automotive manufacturers, electric utilities, electric vehicle 

charging companies, state/local governments, and research organizations. As part of this 

coordination, a convenient and effective charging network must be deployed across the region. 

This network should allow long-distance travel for battery electric vehicles (BEVs), empower 

residents who do not have access to residential charging, and provide convenient charging 

options for all PEV drivers. This report seeks to provide guidance on PEV charging 

infrastructure to stakeholders in Maryland, both to reduce range anxiety as a barrier to PEV sales 

and to ensure the effective use of private/public investments in PEV charging infrastructure. 

This report is organized into four main parts. First, an analysis of the existing Maryland lightduty vehicle (LDV) market is conducted and PEV adoption goals are reviewed. Second, detailed 

global positioning system (GPS) travel data from INRIX (a commercial traffic/mapping 

provider) are described, including a discussion of quality control measures. Third, these travel 

data are used in the National Renewable Energy Laboratory’s (NREL’s) Electric Vehicle 

Infrastructure Projection (EVI-Pro) model to estimate the total amount of charging infrastructure 

by type and location required to support PEV adoption goals in Maryland. Finally, results are 

presented for a baseline scenario in Maryland supporting 300,000 PEVs, and a sensitivity 

analysis is conducted to highlight sources of uncertainty. 
2 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

2 Maryland Existing Light-Duty Vehicle Stock and 

Public Charging Networks

While most of the results in this study are reported at the state level, charging infrastructure 

estimates are also provided at the utility service territory and county levels where appropriate. 

The geographies used for segmenting results are shown in Figure 1 and Figure 2. Note that the 

geographies vary in area, population, and vehicle registrations. For the sake of simplicity, results 

for the six smallest utility service territories (as measured by LDV registrations) are consolidated 

as shown in Table 1. 

Figure 1. Maryland utility service territories

(ABB Ability Energy Velocity Suite 2018).

Table 1. Maryland Utility Service Territories Considered in this Study by Reporting Aggregation 

Level

Individual Reporting by Utility Aggregated Reporting

Baltimore Gas & Electric Company (BGE)

Potomac Electric Power Company (Pepco)

Potomac Edison Company (PE)

Southern Maryland Electric Coop Incorporated 

(SMECO)

Delmarva Power & Light Company

City of Hagerstown, Maryland

Choptank Electric Coop Incorporated

Easton Utilities Commission

Thurmont Municipal Light Company

Town of Berlin, Maryland

Town of Williamsport, Maryland
3 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 2. Maryland counties considered in this study

2.1 Maryland Existing Light-Duty Vehicle Stock

IHS data report 4.73 million LDVs registered in the state of Maryland at the end of 2016 (IHS 

Markit 2017). Figure 3 summarizes the makeup of these vehicles by fuel type. The proportions 

are consistent with national trends, with gasoline vehicles representing an overwhelming 

majority of the stock (89.3%), and the remainder comprised E85 flexible fuel (6.4%), diesel 

(2.4%), and electrified powertrains (2.0%). 

Figure 3. LDVs registered in Maryland as of the end of 2016 by fuel type

“Flexible” indicates E-85 capable vehicles, and “Convertible” indicates vehicles that were ready-made by the 

manufacturer for alternative fuel systems. Based on NREL analysis of IHS data (IHS 2017).
4 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

IHS data are available throughout Maryland, enabling a spatial understanding of how the 

vehicles from Figure 3 are spread across the state. Figure 4 shows LDV registrations (4.72 

million vehicles) by ZIP code. LDV registrations are understandably highest in areas with the 

largest populations (Baltimore and Washington, DC metropolitan areas, Anne Arundel County).

Figure 4. Maryland LDV registrations by ZIP code

IHS data report a total of 7,900 PEVs registered in Maryland at the end of 2016. The 

distributions by make/model and utility service territory are shown in Figure 5 and Table 2, 

respectively). More recent data provided by the Maryland Vehicle Administration shows a total 

of 10,175 PEVs registered in Maryland as of November 2017 with 57% as plug-in hybrid electric 

vehicles (PHEVs) and 43% as BEVs indicating a recent surge in BEV registrations (relative to 

PHEV registrations).

Figure 5. Maryland PEV registrations by make/model
5 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Table 2. Maryland PEV registrations by utility service territory

Utility Service Territory

BEV 

Total

PHEV 

Total

PEV 

Total % BEV

Baltimore Gas & Electric Co 1,320 2,720 4,050 33%

Potomac Electric Power Co 1,190 1,380 2,570 46%

Potomac Edison Co 210 480 690 31%

Southern Maryland Electric Coop Inc 80 240 320 26%

Delmarva Power & Light Co 50 140 190 26%

Remaining Utilities 20 70 90 23%

Total 2,900 5,000 7,900 

2.2 Maryland Existing Public Charging Networks

Figure 6 shows the current charging infrastructure characterized by charger type, as recorded by 

the Alternative Fuels Data Center (AFDC) Station Locator (U.S. Department of Energy 2018). 

The type of charging available at each station is designated by the dot color, signifying either 

Level 1 (L1), Level 2 (L2), or direct current fast charge (DCFC). As of February 2018, Maryland 

had a total of 70 L1 plugs, 966 L2 plugs, and 172 DCFC plugs in the AFDC database. Existing 

stations are primarily located in the Baltimore and Washington D.C. metropolitan areas. The 

stations shown in Figure 6 are all publicly accessible; restricted stations at workplaces and 

residences are not shown given their limited access.

Figure 6. Existing public PEV charging infrastructure in Maryland
6 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Beyond listing the electric vehicle charging locations, the AFDC also reports the types and 

number of plugs at each station. Stations with a large number of plugs are able to service more 

vehicles at a given time. Figure 7 shows the number of publicly available electric vehicle 

charging plugs by type at the electric utility territory level as of February 2018. Baltimore Gas 

and Electric and Potomac Electric Power have the majority of the plugs in the state, which is 

expected given the large populations they serve and the current clustering of PEVs in these 

territories.

Finally, note that a given charging location may have multiple charging types. A station with 

plugs for Level 1 and Level 2 charging is counted as both types. Thus, the total number of Level 

1, Level 2, and DCFC stations reported for the state of Maryland exceeds the number of charging 

locations. Please refer to Appendix A for raw plug and station counts at the utility service 

territory and county levels. 

Figure 7. Existing public PEV charging plug counts in Maryland by electric utility
7 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

2.3 Baseline PEV Scenario: 300,000 in 2025

Maryland’s Zero Emission Vehicle goal of 300,000 PEVs by 2025 serves as the basis for the 

stock of vehicles that must be supported by the state’s electric vehicle charging infrastructure for 

the baseline scenario in this report. 

This analysis assumes that the Maryland 2025 PEV stock is distributed as: 30% PHEV20, 20% 

PHEV50, 15% BEV100, and 35% BEV250.1 The effects of this stock assumption are explored 

using sensitivity analysis in Section 5.3. Prior research has suggested minimal need for 

describing vehicles beyond their powertrain type and electric range (SUVs, sedans, performance 

vehicles, etc.) for purposes of estimating PEV charging infrastructure requirements. For instance, 

Wood et al. (2017) found minimal sensitivity between the percentage of SUVs in the forecasted 

stock and the required electric vehicle charging infrastructure. However, differentiating between 

PEV powertrain type (BEV vs. PHEV) and electric range are shown to be important factors for 

the analysis.

While projections of the number and type of PEVs on the road in 2025 are valuable, projections 

of where the 300,000 PEVs might be distributed throughout the state are also important for 

forecasting infrastructure requirements. Attributing the forecasted number of PEVs in 2025 to 

current geographic clustering of PEVs is considered unreliable given the increase in PEV 

availability and the ongoing expansion of electric vehicle charging networks. Instead, existing 

hybrid electric vehicle (HEV) registrations are used to predict the spatial distribution of PEVs in 

2025. Spatial distribution of existing HEVs by ZIP code is shown in Figure 8. Prior research has 

shown that areas with large number of HEVs are correlated with adoption of PEVs (Wood et al. 

2018). 

Figure 8. Maryland combined PEV and HEV registrations by ZIP code.

 

1 BEVxxx indicates a BEV with a range of xxx miles. PHEVxx indicates a PHEV with a range of xx miles.
8 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

While most present day PEV charging takes place at residential locations, high density parking 

and housing environments present challenges for urban PEV owners, particularly those living in 

multi-unit dwellings (MUDs). This analysis accounts for the share of charging taking place at 

residential locations to estimate demand for charging infrastructure at work and public locations. 

As such, assumptions must be made regarding the number of PEVs adopted by residents of 

MUDs. This analysis uses data from the 2016 American Community Survey to quantify 

household ownership and residence type information (summarized in Appendix B) (American 

Community Survey 2016). Assumptions regarding potential availability of residential charging at 

each household type are applied considering renters and individuals in large complexes having 

limited ability to park and charge their vehicle in a consistent location. These individuals would 

presumably be reliant on workplace charging and public networks to satisfy most of charging 

needs. This combination of housing data and assumptions regarding availability of home 

charging results in 70% of 2025 PEVs having access to home charging, and 30% classified in the 

remainder of the report as MUDs without access to home charging.

Table 3 describes the assumed spatial disaggregation of 2025 PEVs by type at the utility territory 

level (see Appendix C for a similar breakdown at the county level). 

Table 3. Projected 2025 PEV Counts by Type and Utility Service Territory

Utility Service Territory MUD (%) PHEV20 PHEV50 BEV100 BEV250 Total

Baltimore Gas & Electric 30 42,678 28,452 21,339 49,791 142,260

Potomac Electric Power 36 29,090 19,393 14,545 33,938 96,966

Potomac Edison 20 7,494 4,996 3,747 8,743 24,980

Southern Maryland Electric Coop 13 5,435 3,624 2,718 6,341 18,118

Delmarva Power & Light 17 3,503 2,335 1,751 4,087 11,676

Remaining Utilities 12 1,800 1,200 900 2,100 6,000

Total 30 90,000 60,000 45,000 105,000 300,000
9 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

3 INRIX GPS Travel Trajectories

Travel patterns are one of the main drivers of PEV charging infrastructure requirements. To 

properly model PEV charging infrastructure requirements in Maryland, NREL acquired 

individual GPS travel trajectories from INRIX, a commercial traffic/mapping provider that 

works with automotive manufacturers, commercial fleet operators, mobile companies, and state 

and local transportation departments to provide real-time traffic and mobility analytics. INRIX 

products are based on anonymized GPS data collected from hundreds of millions of devices. 

3.1 Data Summary and Visualization

The INRIX data set used in this study includes all GPS travel trajectories (mode imputed as 

driving trips by INRIX) that intersected the Maryland region during a four-month sample from 

2015. Each travel trajectory features trip-level data such as start and end times and GPS 

coordinates (including origin, destination, and intermediate waypoints). The data set contains a 

total of 5.6 million unique device identifiers, 20 million trips, 426 million miles of driving, and 

over 1.3 billion GPS waypoints. Figure 9 shows a destination heatmap of the INRIX data set for 

trips ending in Maryland. Trips starting in Maryland and ending out of state are considered in the 

infrastructure analysis but are not included in the figure. 

3.2 Down Sampling and Data Processing

Prior to using the INRIX travel data subset in PEV driving/charging simulations, several data 

processing steps were completed, including: 

• Eliminating all travel data not originating from light-duty vehicles 

• Removing the first and last vehicle-day for each device identifier (to remove incomplete 

travel days)

• Editing trip origins to ensure consistency with previous destination in the trip chain 

• Computing trip driving distance based on adjusted origin, waypoints, and original 

destination

• Assessing trip destinations (i.e., home and workplace location for each device identifier

based on destination frequency and time of day over multiple travel days) 

• Implementing spatial joins on county, utility service territory, and land use data layers. 

These processing steps are consistent with measures taken by NREL on a similar INRIX data set

from Columbus, Ohio (Wood et al. 2018). Down sampling and data processing for all LDV trips 

in the INRIX travel data set remove approximately 90% of the original data, reducing the data

set used in this study to approximately 440,000 full travel days, 1.4 million trips, and 20 million 

miles of driving (a sufficiently large data set for purposes of estimating charging infrastructure 

requirements for the 300,000 PEVs considered in this study).
10 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 9. Trip-destination frequency map derived from the INRIX travel data set
11 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

3.3 Data Validation 

The processed INRIX data for Maryland are compared to other large-scale GPS travel datasets to 

check for consistency and compare trends. Figure 10 shows the distribution of daily vehicle 

miles traveled (VMT) from the processed INRIX travel data and comparable distributions from 

the GPS portion of the 2012 California Household Travel Survey (CHTS) and an additional 

processed GPS dataset from INRIX describing light duty vehicle travel in Columbus Ohio during 

2016.The processed INRIX travel data displays a longer daily distance driven compared to the 

travel datasets in Columbus and California, with up to a 10% deviation in the cumulative 

distribution of daily VMT (impact of daily driving distance on charging infrastructure 

requirements is explored as a sensitivity in Section 5.3). One explanation for the difference in 

daily VMT is the differing levels of urbanization within the regions shown; the 2016 INRIX 

Columbus dataset is almost exclusively urban driving while the 2012 CHTS and 2015 INRIX 

contain increasingly larger portions of rural travel. Several iterations of the National Highway 

Travel Survey support the role urbanization plays on reducing driving. Table 4 contains results 

comparing the daily VMT calculated in urban and rural regions over a period of approximately 

20 years (Santos et al. 2011). 

Table 4. Comparison of daily VMT between urban and rural areas

Year Mean Urban VMT Mean Rural VMT Percent Difference

1990 NPTS 22.41 29.61 +32%

1995 NPTS 25.00 34.63 +39%

2001 NHTS 27.30 37.56 +38%

2009 NHTS 23.14 34.18 +48%
12 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 10. Distribution of daily VMT from processed INRIX data and additional GPS datasets.

Similarly, Figure 11 shows a comparison of trip counts by time of day from the processed 2015 

INRIX travel data for Maryland and comparable data from 2012 CHTS and 2016 INRIX 

Columbus. All three data sets show a similar pattern of morning/afternoon peaks with trip counts 

tapering off in the early evening and few trips taking place during the night.

Figure 11. Distribution of trips by time of day from processed INRIX data and additional GPS 

datasets.
13 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

4 EVI-Pro Methodology

NREL’s EVI-Pro model was used with the INRIX data to simulate PEV driving and charging 

behavior and estimate infrastructure requirements. Section 4.1 describes the EVI-Pro model, 

Section 4.2 reviews PEV attributes used for baseline simulations in this study, and Section 4.3 

reviews charging infrastructure attributes used for baseline simulations in this study.

4.1 EVI-Pro Model Description

In collaboration with the California Energy Commission, NREL developed EVI-Pro to estimate 

requirements for charging infrastructure to support consumer adoption of PEVs. EVI-Pro uses 

PEV market projections and real-world travel data from mass market consumers to estimate 

future requirements for residential, workplace, and public charging under a variety of scenarios. 

Outputs of the model include: anticipating spatial/temporal consumer demand for charging 

accounting for the impact of single-unit dwelling (SUD) and MUD residency, weekday/weekend 

travel behavior, and regional differences in travel behavior and vehicle adoption. A graphical 

representation of the input/output relationships in EVI-Pro is shown in Figure 12, including the 

primary processing steps in the model: 1) conducting individual PEV driving/charging 

simulations over real-world 24-hour driving days, 2) spatial-temporal post processing of 

individual charging events to derive ratios of charging plugs to PEVs, and 3) scaling said ratios 

per a PEV stock goal or projection. 

Figure 12. Graphical representation of inputs/outputs and data flow in EVI-Pro

eVMT: electric vehicle miles traveled

EVSE: electric vehicle supply equipment 
14 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

EVI-Pro uses a “bottom-up” approach to estimate PEV charging requirements with the 

fundamental element of 24-hour daily driving schedules from real-world vehicles. While these 

driving schedules are typically sourced from gasoline vehicles, EVI-Pro simulates each driving 

day as though it were attempted in a PEV. By applying real-world travel data from gasoline 

vehicles to simulated PEVs, EVI-Pro attempts to estimate charging solutions that enable future 

PEVs to serve as a direct replacement for the gasoline vehicles that represent the present-day 

majority of the LDV fleet.

Charging solutions to complete individual days of driving are estimated by identifying charging 

opportunities that are consumer-oriented for both convenience and cost. Convenience is achieved

by simulating charging events as only taking place during dwell times present in the original 

travel data. The EVI-Pro method implies that the mainstream PEV drivers will have a low 

tolerance for altering travel behavior on a regular basis to accommodate charging their vehicle. 

When the price of charging is equivalent for two or more locations, EVI-Pro assumes that 

consumers prefer to charge at locations with long dwell times. This approach implies a greater 

energy transfer per charging event and helps to minimize the number of charging events per day. 

Simulated consumers in EVI-Pro are modeled as being economically efficient, preferring to 

charge their vehicles at locations that help minimize charging costs. Simulated consumers are 

provided with charging cost information and the energy needed to complete their next trip, so 

each simulated PEV driver can decide whether a charging event is needed at their current 

location. Once feasible charging solutions are identified, the model further iterates through 

driving/charging events until the battery state of charge at the start and end of the simulated day 

are consistent.

In addition to the objective of minimizing cost, simulated consumers are also subject to 

constraints on battery state of charge. For each simulated driving day in EVI-Pro, BEVs are 

required to maintain battery state of charge above a pre-defined level, defined as a reasonable 

proxy for minimizing range anxiety (a 20-mile range tolerance is assumed in this study). Since 

PHEVs can operate with a depleted battery in charge-sustaining mode, EVI-Pro does not place a 

constraint on the minimum allowable state of charge for PHEVs, but instead attempts to 

maximize electric VMT and minimize gasoline consumption. 

While the individual driving and charging simulations determine the number of vehicles that 

utilize each charger type, the amount of infrastructure required to satisfy charging demand is 

dependent on the spatial-temporal coincidence of charging. For example, consider a fixed 

number of charging events at public L2 chargers. If these charging events take place at the same 

location and are uniformly distributed throughout the day, a minimal amount of infrastructure 

can meet the demand (corresponding to the high utilization of a small number of chargers). 

Conversely, if the same number of charging events take place in isolated locations all at the same 

time, a much larger amount of infrastructure is required (corresponding to the low utilization of a 

large number of chargers). EVI-Pro calculates spatial-temporal coincidence of simulated 

charging events by geographically aggregating charging sessions and allocating sufficient 

charging capacity (plugs) to prevent queuing at each individual charging location (stations).
15 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

4.2 Simulated PEV Attributes

The vehicle attributes specified in EVI-Pro include the electric range (in miles), vehicle drive 

efficiency (watthours-per-mile), minimum range tolerance (in miles), onboard charger efficiency, 

and maximum alternating current charging power. Vehicle energy consumption, which is the 

main driver of charging requirements, is computed based on highly resolved speed profiles for 

each different model type and accounts for temperature effects on fuel economy (Wood et al. 

2017). Table 4 summarizes the attributes of the PEVs considered in this report. 

Table 5. Modeled PEV Attributes

 PHEV20 PHEV50 BEV100 BEV250

Nominal Electric Driving 

Range, mi 20 50 100 250

Nominal Energy 

Consumption, Wh/mi 325 325 325 325

Minimum Range 

Tolerance, mi 0 0 20 20

Onboard Charger 

Efficiency 90% 90% 90% 90%

Maximum AC Charging 

Power, kW 3.6 3.6 20 20

AC: alternating current

BEVxx: battery electric vehicle with a range of xx miles

kW: kilowatt

PHEVxx: plug-in hybrid electric vehicle with a range of xx miles

Wh: watt-hour
16 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

4.3 Simulated Infrastructure Attributes

Charging infrastructure is segmented by location type as home (SUD or MUD), workplace, and 

public (any destination not classified as either a home or work destination). For each location 

type, up to three charging power levels are available (L1, L2, DCFC). For all simulated charging 

opportunities, a minimum dwell time for the driver to consider plugging in (at all location types, 

including home) is also be specified (minimum dwell time of 2 hours for L1/L2 opportunities, 30 

minutes for DCFC opportunities), though simulated consumers may not plug in at every 

opportunity depending on their daily charging needs. 

Consumer charging preferences are implemented in EVI-Pro to simulate selection of charging 

opportunities for individual travel days. This analysis considers a baseline scenario where 

residential charging is preferred by consumers (if available) with workplace charging, public L2 

charging, and DCFC used to fill gaps in daily charging needs (in that order). Sensitivities on 

consumer charging preferences are explored in Section 5.3. 

Several scenarios are simulated for each consumer to cover a broad range of possible charging 

options and capture their impact on PEV charging, infrastructure requirements, and resulting 

electric load. A matrix of all charging options considered is shown in Table 5. The effective 

DCFC charge rate is adjusted for temperature effects as well as charge duration, assuming that 

charging power tapers as battery state of charge approaches high levels (Wood et al. 2017). 

Table 6. Charging Options Available to Consumers in EVI-Pro

Location Level Power Comment

Home L1 1.4 kW 

L2 3.6 kW BEVs simulated with higher L2 power to enable full 

overnight charge

Work L2 6.2 kW PHEV on-board charger limits maximum power to 

3.6 kW in EVI-Pro

Public L2 6.2 kW PHEV on-board charger limits maximum power to 

3.6 kW in EVI-Pro

 DCFC 50 kW BEVs only; charge rate tapers off at high state of 

charge

L1 = level 1 charging station

L2 = level 2 charging station
17 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

5 Simulation Results

Section 5.1 reports infrastructure requirements for a baseline scenario considering 300,000 PEVs 

on the road in Maryland by 2025. Section 5.2 reviews the potential for utilizing L1 and L2 plugs 

at workplaces. Sensitivity analyses around some of the key assumptions are explored in Section 

5.3. Several simulated load profiles are reviewed in Section 5.4 with a discussion on how PEV 

selection and consumer charging behavior influence simulated electrical loads from PEV 

charging. Finally, the simulated load profiles are explored further with an investigation into time 

of use pricing in Section 5.5.

5.1 Baseline Results

Statewide EVI-Pro simulations were run assuming 300,000 PEVs on the road in Maryland by 

2025. Simulations utilized 24-hour driving days derived from 426 million miles of real-world 

driving data provided by INRIX. Use of this driving data (collected during 2015) for PEV 

simulations implicitly assumes that future Maryland drivers will attempt to utilize PEVs in a 

manner consistent with present-day vehicles. Furthermore, EVI-Pro assumes a partial level of 

support for non-residential PHEV charging by residents with consistent access to residential 

charging (under the assumption that half of these drivers will not seek to charge away from home 

as a matter of convenience). A 50/50 split was assumed between PHEVs and BEVs. A 

significant percent of overall required charging infrastructure was driven by the assumption that 

30% of PEVs statewide will be owned by individuals without consistent access to residential 

charging. These individuals (broadly defined as MUD residents) are thus reliant on workplace 

charging and public networks for most of their daily charging needs.

Baseline results for Maryland are presented in Table 6 by utility service territory and charger 

type. Please refer to Appendix D for baseline results at the county level. A statewide total of 

approximately 27,000 non-residential L2 plugs and 1,000 DCFC plugs are estimated as 

necessary for supporting future consumer demand for PEV charging. Note that these estimates 

include a significant amount of residential charging. Despite simulating 30% of PEVs as not 

having access to residential charging, 66% of statewide charging is simulated as taking place at 

home. The percentage of residential charging increases to approximately 90% for the 70% of 

PEVs assumed to have access to charging at their home location). 

Table 7. Estimated Plug Counts by Utility Service Territory

Utility Service Territory Work L2 Public L2 DCFC

Baltimore Gas & Electric 8,564 4,568 486 

Potomac Electric Power 5,946 3,059 221 

Potomac Edison 1,376 719 139 

Southern Maryland Electric Coop 649 416 58 

Delmarva Power & Light 556 377 87 

Remaining Utilities 302 160 43 

Total 17,393 9,299 1,034 
18 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Despite the fact that L2 charging outnumbers DCFC by a factor of 26 in these estimates, readers 

are cautioned against regarding L2 infrastructure as more valuable than DCFC, as this is not 

necessarily true. Fundamentally, each type of charging infrastructure plays a unique role in 

supporting the PEV market. EVI-Pro simulations typically provide the majority of L2 charging 

to support increased electric VMT of PHEVs, which are simulated as not being capable of fast 

charging. As PHEVs are equipped with a gasoline backup for quickly replenishing driving range, 

non-residential L2 charging can be considered a relatively soft infrastructure requirement. DCFC 

infrastructure on the other hand is a relatively hard requirement for BEV drivers who have no 

other alternative for quickly replenishing driving range. In fact, the mere presence of DCFC 

stations may be required to provide BEV drivers with a safety net against infamous range anxiety 

concerns.

Estimated L2 requirements at workplaces are nearly twice as great compared to public locations. 

EVI-Pro finds that commuting distances to workplaces and dwell times for vehicles parked at 

workplaces on average provide superior charging opportunities for supplementing residential 

charging for residents of SUDs and for serving as the primarily charging solution for residents of 

MUDs. In additional to favorable travel pattern characteristics, workplace charging results are 

bolstered by the EVI-Pro baseline assumption that consumers prefer L2 charging at work as 

opposed to public locations when both alternatives are viable options. The impact of this 

assumption is tested using sensitivity analysis in Section 5.2. 

Overall, the baseline estimate implies that significant growth in Maryland’s current charging 

network is necessary to satisfy charging demand from future PEVs. 

5.2 Work Charger Availability Adjustment

The baseline case assumes exclusive availability of L2 chargers at work locations. Omission of 

L1 plugs at workplaces is based on stakeholder requirements (in Maryland and elsewhere) that 

chargers have smart and networked capabilities for dynamic load management and billing 

purposes. Additionally, the use of L1 chargers also makes metering and billing more challenging 

at work locations. As the existing market for chargers with these features has been dominated by 

L2 units, L1 is manually restricted in the baseline scenario. However, a deviation from the 

baseline set of assumptions was explored in which workplace charging options included L1 

charging in addition to L2 charging. For many travel days, the choice between a L1 and L2 plug 

did not change the amount of energy consumed at work; workplace charging frequently consists 

of long dwell times supplemented by charging at residencies. In fact, nearly 75% of workplace 

charging events simulated in the baseline case were found to be satisfied by L1 charging. 

Despite the large majority of workplace charging events which can be satisfied through L1 plugs, 

these plugs produce only 35% of the energy dispensed at workplaces. L1 charging events at work 

are limited to vehicles with smaller energy needs, while L2 charging events require high power 

throughout the majority of workplace dwell times (MUD residents depending on workplace 

charging, for instance). 

5.3 Sensitivity Analysis 

The 2025 statewide charging infrastructure values presented in Section 5.1 are a reasonable 

estimate built upon a large data set of real-world driving patterns, a modeling framework 
19 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

predicated on rational consumer behavior, and sound engineering judgment regarding technology 

development. However, a technical problem this vast leaves much room for uncertainty. To 

better understand how baseline estimates are affected by various assumptions, a parametric 

sensitivity analysis is conducted varying individual parameters one at a time (in both directions 

where possible). Sensitivity analysis results are presented in Figure 13 for non-residential L2 

(work and public) and DCFC as relative to the baseline results previously presented. 

By far the largest sensitivity for non-residential L2 was simply the number of PEVs on the road. 

Varying this parameter induced an approximate ±67% swing in both non-residential L2 and 

DCFC estimates. This is not necessarily a surprising result as this input parameter was explored 

quite aggressively using a ±200,000 swing in the number of PEVs on the road in Maryland by 

2025. 

In addition to the number of PEVs on the road, the types of PEVs that get adopted also have a 

significant impact on charging infrastructure requirements. The “PHEV:BEV” and “Range 

Preference” sensitivities manipulate the mix of PEVs while holding the statewide total constant. 

PHEV:BEV ratio is swept from 4:1 (80% PHEV) to 1:4 (80% BEV) and results in a ±16% swing 

for non-residential L2 and a ±60% for DCFC. Range preference is swept from a “short” setting 

(comprising 50% PHEV20 and 50% BEV100) to a “long” setting (comprising 50% PHEV50 and 

50% BEV250). This sensitivity results in a ±10% swing in non-residential L2 and a -14% to 

+32% swing for DCFC. These two sensitivities directly relate PEV attributes to infrastructure 

requirements. For instance, PHEVs are not simulated as DCFC capable; thus, increasing PHEV 

shares decreased DCFC demand and increased L2 demand. Similarly, PEVs with longer electric 

driving ranges generally decrease infrastructure requirements as they can accommodate most of 

their charging needs with an overnight charge at home.

Impacts of fast charging technology development are prominent in the sensitivity analysis. 

Research enabling high power fast charging (up to 400 kW) is currently an area of great interest 

as an enabling technology for BEV ownership. The “DCFC Power Level” sensitivity explores 

the relationship between high-power fast charging and infrastructure requirements. This 

sensitivity is explored from a 50-kW baseline to a 400-kW scenario where the charge acceptance 

rate of BEV battery packs is assumed to scale accordingly with the nominal DCFC power rating. 

Increasing fast charge power from 50 kW to 400 kW is simulated as decreasing DCFC plug

requirements by 71%. While this result is revealing, some caveats should be made. First, it must 

be acknowledged that this sensitivity is explored within the context of fixed demand. This means 

that faster charging times translate directly to faster turnover times for individual charging events 

at DCFC stations, which results in lower plug count requirements at each simulated DCFC 

station. Additionally, it is reasonable to assume that successful deployment of 400-kW fast 

charging stations would increase sales of high-power fast charge capable BEVs. While this 

feedback loop is readily acknowledged, it is not explicitly considered in this parametric 

sensitivity analysis.

Consumer preference for fast charging is explicitly considered in the “DCFC Preference” 

scenario (though not directly coupled to market success of long-range BEVs or high-power fast 

charging in the sensitivity analysis). This scenario assumes that consumers place sufficient value 

on the convenience of fast charging such that they seek it out ahead of L2 for public charging 

opportunities; however, residential and workplace charging remain preferred to fast charging. 
20 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

This sensitivity results in a 13% decrease in demand for non-residential L2 and a 28% increase in 

demand for fast charging. 

Similarly, the “Workplace Preference” scenario manipulates consumer charging preferences such 

that public charging (both L2 and DCFC) is preferred above workplace charging. To some 

degree the non-residential L2 effects of this scenario are offsetting (demand for workplace L2 

decreases and public L2 increases); however, on net non-residential L2 demand decreases by 

approximately 14%. Conversely, DCFC demand experiences a 11% increase, essentially 

covering the lost demand for workplace charging that cannot be met by public L2.

While not impacting consumer demand for DCFC, the “PHEV Support” sensitivity has a 

dramatic impact on consumer demand for non-residential L2. Consumer demand for nonresidential L2 infrastructure decreases by 57% in the “No Support” scenario and increases by 

43% in the “Full Support” scenario. These scenarios represent extreme conditions regarding 

demand for non-residential charging by PHEV owners with access to home charging (a segment 

of consumers with relatively soft demand for charging away from home).

The role of MUDs is of unique importance to estimating charging infrastructure requirements in 

Maryland given the state’s relatively high urban population. Recall that the baseline scenario in 

this analysis assumes 30% of PEVs are adopted by individuals without consistent access to 

residential charging (broadly referred to as MUD residents in this report). The “MUD PEV 

Share” sensitivity explores the impact of decreasing this assumption from 30% to 0%, which 

results in decreases for non-residential L2 and DCFC demand of 22% and 37%, respectively. 

Given that MUDs are simulated in the baseline as not having access to home charging, this result 

is relatively intuitive. It is also worth noting that this sensitivity is largely consistent with a

scenario in which MUD charging access at home is enabled (non-residential infrastructure 

requirements for PEVs with access to home charging are roughly independent of residence type). 

Recall from Figure 10 that the cumulative distribution of daily VMT for the INRIX data from 

Maryland exceeds the national average by up to 10%. The “Daily VMT Distribution” sensitivity 

conducts a new set of EVI-Pro simulations using a subset of the INRIX GPS data which more 

closely resembles the national distribution of daily VMT. As expected, this scenario decreases 

infrastructure requirements overall by approximately 18% for both non-residential L2 and 

DCFC.
21 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 13. Results of parametric sensitivity analysis for non-residential (work and public) L2 plugs (top) and fast charging (bottom).

Results are presented as relative to baseline shown in Section 5.1.
22 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Finally, ambient temperature is swept from 25°C (77°F) in the baseline scenario to 0°C (32°F) in 

the “Ambient Temperature” sensitivity. Ambient temperature is incorporated in the EVI-Pro 

model through adjustments made to driving efficiency (as a result of cabin heating/cooling loads 

and battery efficiency impacts) and to DCFC power levels (modulations to charge power based 

on battery temperature) as discussed in previous studies (Wood et al. 2017). The sensitivity 

analysis neglects hot ambient conditions as cold conditions are modeled as having a more 

significant impact on PEV driving and charging efficiency. Maryland simulations show cold 

ambient temperature increasing demand for non-residential L2 by 6% and increasing DCFC 

demand by 26%. 
23 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

5.4 Simulated Charging Loads, Baseline

While the primary output of EVI-Pro simulations are estimates for consumer charging 

infrastructure requirements, the highly resolved nature of EVI-Pro simulations lends itself well to 

estimating aggregate charging load profiles. This section presents simulated load profiles by time 

of day and location type from the baseline scenario presented in Section 5.1. Please refer to 

Appendix E for additional load profiles selected from the sensitivity analyses in Section 5.3. 

Figure 14 shows the aggregated weekday charging load for the 300,000 PEVs assumed in the 

baseline scenario. This weekday profile represents approximately 3,200 megawatt-hours of 

electrical demand at residential and non-residential charging stations across Maryland (66% 

residential) with a peak load of approximately 275 megawatts at 6 p.m. Note that simulations 

assume uncontrolled charging and do not consider any mechanisms for shifting load off-peak or 

for integration with renewable resources. In reality, Maryland utilities are likely to provide 

consumers with incentives for favorably aligning charging with non-PEV loads and constraints 

on the electrical grid (e.g., time of use rates).

Figure 14. Aggregate statewide load profiles for a weekday, baseline
24 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 15 decomposes the statewide charging load between residents of SUDs and MUDs (70% 

and 30% of 300,000 PEVs, respectively). The SUD profile takes a familiar shape with 

approximately 90% of charging at home locations, primarily concentrated between 4 p.m. and 8 

p.m. The MUD profile is exclusively non-residential (by definition) and concentrated between 8 

a.m. and 12 p.m. 

Figure 15. Statewide load profiles by charging type for a weekday, separated by residence type
25 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure 16 further decomposes the statewide load, this time segmenting the weekday loads from 

SUDs by the four PEV types considered in the study; note that the scales vary across plots, 

partially due to the different number of vehicles in each PEV type. The share of non-residential 

charging tends to increase as single-charge electric range decreases. This manifests in more 

charging events per day for short-range PEVs and a more uniformly distributed load across the 

day. Note that the BEV250 almost exclusively relies on L2 charging at home, resulting in a sharp 

peak in aggregate load in the early evening hours.

Figure 16. Statewide load profiles by charging type for a weekday, separated by vehicle type. The 

vehicles shown belong to SUD residents.

5.5 Simulated Charging Loads, Time of Use Response

The charging behavior in the previous section assumed that drivers immediately began charging 

their vehicles upon parking during dwells for which electricity is needed. Uncontrolled charging 

events create large load on the grid as drivers return home from work, often during times when 

the grid is already experiencing peak load from traditional demands. Electric utilities are 

beginning to respond to this problem through smart charging (Southern California Edison, 2016), 

or utility-controlled charging rates, and time of use (TOU) rates, or variable charging costs by 

time of day (Austin Energy, 2017). Although utilities may influence charging behavior, meeting 

travel requirements presents some demand constraints limiting the flexibility of the state-wide 

charging loads. For instance, drivers with high mileage demands and limited dwell opportunities 

may not be able to avoid charging during high priced periods. This section explores an alternate 

charging behavior where electric vehicle owners are offered a lower price during the late evening 

to incentivize delaying the beginning of their charge.
26 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Unlike the scenarios presented in section 5.4, charging scenarios were modeled with a variable 

residential utility rate. The price was low outside of the hours of 10AM and 8PM, representing a 

simplified approximation of the summer pricing offered by BG&E for PEV loads at residences 

(Baltimore Gas and Electric Company, 2018). Every driver in this scenario is assumed to attempt 

to respond to the TOU rate and minimize their cost of residential charging. Figure 17 below 

shows the state-wide load profile and the relative residential utility rate by time of day. 

Consumers are assumed to delay their charging only if they can successfully meet their travel 

demand requirements. 

Figure 17. Aggregate statewide load profiles for a weekday with baseline assumptions and an 

applied TOU rate.

Results from the TOU pricing scenario revealed that nearly every vehicle was capable of delaying 

their residential charging to avoid increasing additional demand during peak hours of the day. A 

peak of approximately 480MW occurs at 10PM as compared to 280MW at 6PM in the 

uncontrolled scenario. The large increase in peak demand can be attributed to network-wide 

reaction to the TOU rate change; the baseline scenario peak is a function of arrival times at home, 

which are not uniform. 

Note that these results are only intended to suggest the flexibility of the charging demand while 

still satisfying travel requirements; grid requirements such a power dispatching, transformer 

capabilities, etc. are not considered. 
27 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

6 Summary

This report examined the current PEV market and existing charging infrastructure in the state of 

Maryland. As of November 2017, 10,175 PEVs were registered in Maryland with 57% as 

PHEVs and 43% as BEVs. As of February 2018, Maryland had a total of 70 L1 plugs, 966 L2 

plugs, and 172 DCFC plugs publicly accessible to PEV drivers. 

Real-world GPS data were analyzed to understand the travel behavior of the state's residents. 

INRIX GPS data from 2015 containing a total of 5.6 million unique device identifiers, 20 million 

trips, 426 million miles of driving, and over 1.3 billion GPS waypoints were investigated for this 

analysis. Down sampling of these data for quality control purposes resulted in a sufficiently large 

data set for purposes of estimating charging infrastructure requirements for the 300,000 PEVs 

considered in this study, with checks made on the distribution of daily VMT and time of day 

drivers are on the road. 

EVI-Pro simulations estimated that 17,400 workplace Level 2 plugs, 9,300 public Level 2 plugs, 

and 1,000 DCFC plugs are necessary to support 300,000 PEVs in Maryland by 2025. These 

estimates assume future PEV owners will drive PEVs in a manner consistent with present day 

gasoline vehicles using a combination of home, workplace, and public charging infrastructure to 

maximize electric VMT. Despite assuming 30% of PEVs will be adopted by individuals without 

consistent access to home charging, simulations reveal 66% of statewide charging taking place at 

residential locations and approximately 90% for individuals with consistent access to home 

charging.

Although care has been taken to develop baseline estimates using the best available data, 

uncertainty remains. Parametric sensitivity analysis was used to explore impacts of multiple 

alternative scenarios. Results suggest that baseline estimates vary by double digit percentage 

points depending on evolving consumer preferences, charging behavior, and technology 

development. 

Overall, this report finds that significant growth of Maryland’s existing charging infrastructure

network is necessary to support a goal of 300,000 PEVs by 2025. 
28 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

References

ABB. 2018. “ABB Ability Velocity Suite.” Accessed March 1, 2018. 

http://new.abb.com/enterprise-software/energy-portfolio-management/marketintelligence-services/velocity-suite 

Austin Energy. 2017. “Residential Time-of-Use Rate EV360 Pilot Program”. Accessed July 13, 

2018. https://austinenergy.com/wcm/connect/88a9db10-fd11-4b51-8378-

61db579988be/EV360OnePageInstallerOverview200617LM.PDF?MOD=AJPERES&C

VID=lSuHZ91 

Baltimore Gas and Electric Company. 2018. “Residential Electric Vehicle Time-Of-Use-Electric 

Schedule EV.” Accessed June 26, 2018. 

https://www.bge.com/MyAccount/MyBillUsage/Documents/Electric/ScheduleEV.pdf 

IHS Markit. 2017. “Vehicle Market Analysis: Registrations and Vehicles-in-Operation.” 

Accessed March 1, 2018. https://www.ihs.com/products/automotive-market-dataanalysis.html 

INRIX. “INRIX Analytics: Trips.” Accessed March 1, 2018. http://inrix.com/products/trips/ 

Melaina, Marc, Brian Bush, Joshua Eichman, Eric Wood, Dana Stright, Venkat Krishnan, David 

Keyser, Trieu Mai, and Joyce McLaren. 2016. National Economic Value Assessment of 

Plug-In Electric Vehicles: Volume I. DOE/GO-102016-4902. Golden: National 

Renewable Energy Laboratory, Golden, CO. 

http://www.nrel.gov/docs/fy17osti/66980.pdf 

O'Malley, Martin, et al. 2013. "State Zero-Emission Vehicle Programs, Memorandum of 

Understanding." http://www.nescaum.org/documents/zev-mou-8-governors-signed20131024.pdf/ 

Santos, A., McGuckin, N., Nakamoto, H.Y., Gray, D., Liss, D. Summary of Travel Trends: 2009 

National Household Travel Survey. FHWA-PL-ll-022. United States Department of 

Transportation, Washington, DC. https://nhts.ornl.gov/2009/pub/stt.pdf 

Southern California Edison. 2016. “Southern California Edison Plug-In Electric Vehicle (PEV) 

Smart Charging Pilot”. Accessed July 13, 2018. 

http://www3.sce.com/sscc/law/dis/dbattach5e.nsf/0/0A867A0A088A764B88257FBE007

A30A5/$FILE/A1410014-R1309011-

SCE_PEV_Smart%20Charging%20Pilot_Final%20Report.pdf 

U.S. Census Bureau. 2016. “American Community Survey (ACS).” Accessed March 1, 2018. 

https://www.census.gov/programs-surveys/acs/ 

U.S. Department of Energy, Alternative Fuels Data Center. “Alternative Fueling Station 

Locator.” Accessed March 1, 2018. http://www.afdc.energy.gov/locator/stations/ 
29 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Wood, Eric, Clement Rames, Matteo Muratori, Sesha Raghavan, and Marc Melaina. 2017. 

National Plug-In Electric Vehicle Infrastructure Analysis. DOE/GO-102017-5040. 

Golden: National Renewable Energy Laboratory. 

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

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. 

NREL/TP-5400-70367. Golden: National Renewable Energy Laboratory. 

https://www.nrel.gov/docs/fy18osti/70367.pdf
30 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Appendix A. Existing Plug Counts by Utility Service 

Territory and County

Utility Service Territory

L1 

Plugs

L2 

Plugs

DCFC 

Plugs

L1 

Stations

L2 

Stations

DCFC 

Stations

Baltimore Gas & Electric 56 580 84 27 237 38

Delmarva Power & Light 0 55 36 0 26 8

Potomac Edison 0 50 15 0 28 5

Potomac Electric Power 11 247 29 7 107 14

Southern Maryland Electric Coop 3 31 7 3 20 2

Remaining Utilities 0 3 1 0 3 1

County

L1 

Plugs

L2 

Plugs

DCFC 

Plugs

L1 

Stations

L2 

Stations

DCFC 

Stations

Allegany County 0 0 0 0 0 0 

Anne Arundel County 13 75 9 5 33 5 

Baltimore City 9 265 33 6 90 17

Baltimore County 32 130 12 15 60 5

Calvert County 0 12 0 0 7 0 

Caroline County 0 0 0 0 0 0 

Carroll County 0 11 2 0 7 1 

Cecil County 0 6 22 0 2 3

Charles County 3 16 7 3 11 2 

Dorchester County 0 1 2 0 1 1

Frederick County 0 22 3 0 11 2 

Garrett County 0 10 0 0 5 0 

Harford County 0 23 2 0 14 1 

Howard County 0 58 12 0 25 5

Kent County 0 4 0 0 2 0 

Montgomery County 1 168 26 1 86 13

Prince George's County 12 112 17 7 39 5 

Queen Anne's County 0 10 2 0 4 1 

Somerset County 0 5 0 0 3 0 

St. Mary's County 0 0 0 0 0 0

Talbot County 0 4 0 0 3 0 

Washington County 0 6 13 0 5 4

Wicomico County 0 7 8 0 4 2 

Worcester County 0 21 2 0 9 1 
31 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Appendix B. Maryland Housing Stock by Size and 

Ownership Status (with NREL Assumptions Regarding 

Potential Availability of Home Charging) 

Maryland 

Shares 

(sums to 

100%)

Assume 

Home 

Charging?

Estimates: Owner-occupied housing units: 1, detached 47.7% yes

Estimates: Owner-occupied housing units: 1, attached 14.3% yes

Estimates: Owner-occupied housing units: 2 0.2% yes

Estimates: Owner-occupied housing units: 3 or 4 0.2% yes

Estimates: Owner-occupied housing units: 5 to 9 0.6% no

Estimates: Owner-occupied housing units: 10 to 19 1.2% no

Estimates: Owner-occupied housing units: 20 to 49 0.5% no

Estimates: Owner-occupied housing units: 50 or more 0.9% no

Estimates: Owner-occupied housing units: Mobile home 1.0% yes

Estimates: Owner-occupied housing units: Boat, RV, van, etc. 0.0% yes

Estimates: Renter-occupied housing units: 1, detached 5.4% yes

Estimates: Renter-occupied housing units: 1, attached 6.7% no

Estimates: Renter-occupied housing units: 2 1.2% no

Estimates: Renter-occupied housing units: 3 or 4 1.9% no

Estimates: Renter-occupied housing units: 5 to 9 4.2% no 

Estimates: Renter-occupied housing units: 10 to 19 7.0% no

Estimates: Renter-occupied housing units: 20 to 49 1.6% no

Estimates: Renter-occupied housing units: 50 or more 5.1% no

Estimates: Renter-occupied housing units: Mobile home 0.3% no

Estimates: Renter-occupied housing units: Boat, RV, van, etc. 0.0% no
32 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Appendix C. PEV Projections by County

County PHEV20 PHEV50 BEV100 BEV250 Total

Allegany County 242 162 121 283 808 

Anne Arundel County 9,291 6,194 4,646 10,840 30,971 

Baltimore County 11,213 7,475 5,607 13,080 37,375 

Calvert County 1,584 1,056 792 1,848 5,280 

Caroline County 255 170 127 297 849 

Carroll County 2,050 1,367 1,025 2,392 6,834 

Cecil County 822 548 411 959 2,740 

Charles County 2,052 1,368 1,026 2,394 6,840 

Dorchester County 248 165 124 289 826 

Frederick County 4,314 2,876 2,157 5,033 14,380 

Garrett County 122 82 61 143 408 

Harford County 2,664 1,776 1,332 3,108 8,880 

Howard County 7,351 4,900 3,675 8,576 24,502 

Kent County 372 248 186 434 1,240 

Montgomery County 25,842 17,228 12,921 30,149 86,140 

Prince George's County 9,624 6,416 4,812 11,228 32,080 

Queen Anne's County 669 446 335 781 2,231 

St. Mary's County 1,397 931 698 1,629 4,655 

Somerset County 96 64 48 112 320 

Talbot County 678 452 339 791 2,260 

Washington County 1,179 786 590 1,376 3,931 

Wicomico County 780 520 390 910 2,600 

Worcester County 535 356 267 624 1,782 

Baltimore city 6,620 4,414 3,310 7,724 22,068 

Total 90,000 60,000 45,000 105,000 300,000 
33 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Appendix D. Electric Vehicle Supply Equipment 

Projections by County

County Work L2 Public L2 DCFC

Allegany County 40 29 9 

Anne Arundel County 1,660 867 107 

Baltimore County 2,070 1,202 101 

Calvert County 189 140 31 

Caroline County 24 29 14 

Carroll County 278 167 42 

Cecil County 118 70 13 

Charles County 202 145 17 

Dorchester County 36 34 10 

Frederick County 797 421 72 

Garrett County 21 15 6 

Harford County 472 272 40 

Howard County 1,532 760 104 

Kent County 79 65 13 

Montgomery County 5,299 2,669 205 

Prince George's County 1,677 985 82 

Queen Anne's County 101 48 19 

St. Mary's County 197 104 14 

Somerset County 33 14 8 

Talbot County 104 49 18 

Washington County 193 103 22 

Wicomico County 113 89 12 

Worcester County 76 64 9 

Baltimore city 2,084 961 64 

Total 17,393 9,299 1,034 
34 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Appendix E. Select Load Profiles from Sensitivity 

Analysis

Figure E1. Aggregate statewide weekday load profiles with 30% MUD share. Top: No residential 

charging at MUDs. Bottom: L2 residential charging available at MUDs.
35 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure E2. Aggregate statewide weekday load profiles. Top: 4:1 PHEV:BEV ratio. Bottom: 1:4 

PHEV:BEV ratio.
36 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Figure E3. Aggregate statewide weekday load profiles. Top: Short-range PEV scenario. Bottom: 

Long-range PEV scenario.