charging electric vehicles in smart cities: a scenario analysis of columbus, ohio charging... ·...

Charging Electric Vehicles in Smart Cities:A Scenario Analysis of Columbus, Ohio

Eric Wood, Clement Rames, and Stanley YoungApril 2017

Preliminary Analysis

2

NREL ObjectiveProvide guidance on plug-in electric vehicle (PEV) charging infrastructure to regional/national stakeholders to:

o Reduce range anxiety as a barrier to increased PEV saleso Ensure effective use of private/public infrastructure investments

How many?

What kind?

Where?

Some key questions related to investment in PEV charging stations…

Getting to know Columbus

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Existing Light-Duty Fleet

2015 Registrations by Zip Code1.68M total

0 60,000

Fleet currently dominated by internal combustion vehicles

with spatial distribution roughly mirroring populationTo be updated with 2016 data

5

Existing Light-Duty Fleet (PEVs)

2015 Registrations by Zip Code1,319 total PEVs

0 176

Columbus currently favors PHEVs to BEVs (relative to national trends) led by the

Chevy Volt

To be updated with 2016 data

6

Primary PEV Growth Scenario

Columbus PEV sales goals are percentage based.

Translation to absolute sales assumes 100,000 sales per year.

Assume 15% of PEVs are adopted by residents of MUDs

Year Percent Sales Goal2016 0.4%2017 0.6% ± 0.1%2018 0.9% ± 0.2%2019 1.4% ± 0.4%

To be updated with new goal (1.8% by project end)

INRIX Travel Data

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INRIX GPS Data

By the numbers:12 months of trips (all of 2016)All trips intersecting Columbus regionDriving mode imputed by INRIX trip engine

7.82M device ids32.9M trips

2.58B waypoints

NREL has purchased a GPS dataset for the Columbus EVSE analysis to characterize regional travel patterns

EVI-Pro Methodology

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Electric Vehicle Infrastructure Projection Tool

• PEV driving/charging simulator + Real-world travel profiles

• Economically efficient consumer charging behavioro Home dominant (default scenario)

• Estimates EVSE requirements foro PHEV and BEV powertrainso Single- and multi-unit dwellingso Weekday and weekend travel

What is EVI-Pro?

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• Travel profiles are simulated using six vehicle models• A matrix of charging options are made available to

each combination of travel profile and vehicle type• Optimization algorithm selects charging behavior at

the individual level to maximize eVMT and minimize charging cost

o Simulated consumers have an assumed preference for charging type and location (based on electricity price) which is home dominant by default

Vehicle / Infrastructure Attributes

Vehicle TypesPHEV20

PHEV40

PHEV60

BEV100

BEV200

BEV300

EVSE Type / Location Home-SUD Home-MUD Non-ResidentialLevel 1 Available Excluded ExcludedLevel 2 Available Available AvailableDC Fast Excluded Excluded Available

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Destination Departure ArrivalDrive Miles

Dwell Hours

SimulatedCharging

Non-Res 8:20 AM 9:00 AM 32.8 5.00 L2Non-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 DCFCNon-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

Driving/Charging Simulations

Simulated charging behavior for a BEV100 under an example travel day

DCFC

L2-Non-Res

L1-Home

Preliminary Results

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Preliminary Results

Estimated plug count by

type (end of 2019)

EVI-Pro estimates the majority of PEV

charging occurring in the evening hours at

Level 1 rates in the homes of single-unit

dwellings (SUD) residents.

A relatively small amount of charging

stations are needed to support:

• Residents of multi-unit dwellings

• Maximizing eVMT in PHEVs

• Completing long distance travel in BEVs

EVI-Pro simulated charging

load profiles by PEV type

15

EVI-Pro Hot Spots, Existing Stations, CFO Candidates

Existing Public L2

EVI-Pro Hot Spots

Clean Fuels Ohio Candidate Site

Next Steps

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• Draft report has been submitted to Columbus working group on EV charging infrastructure

• Feedback is currently being consolidated and addressed by NREL, including:

– Updating PEV sales/registrations through 2016– Updating Columbus sales goals (up to 1.8% by year 3)– SUD Home splits (L1 vs L2)– Explicitly addressing workplace charging– Public splits (L2 vs DCFC)– DCFC siting and queuing

• Updated draft report will be made available to Columbus in coming weeks

• Analysis to be finalized by end of August 2017

Next Steps

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The US Department of Energy Vehicle Technologies Office funded this work.We particularly wish to thank Robert Graham and Rachael Nealer for guidance and support.

Getting to know Columbus

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Columbus currently shows a PEV/HEV ratio (1:16) typical

of most US cities

INRIX Travel Data

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INRIX GPS Data

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Value of INRIX data lies in ability to track individual vehicle movement and simulate charging behavior as though vehicle was a PEV

Pictured: Movements of single mobile device

Trip Origins/Destinations Trip Waypoints

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INRIX Data Cleansing

• INRIX delivers trip and waypoint data largely unprocessed, meaning abnormalities do exist

• NREL has developed a routine for compiling the data into a format that is amenable to PEV driving/charging simulations

• Summary of data cleansing steps:o Filter out first and last vehicle-day for each device ido Chain trips such that origins are coincident with previous destinationso Compute trip distance using provided waypointso Use median destination coordinates to estimate home locationo Cluster destinations around inferred home location to assign home destination

typeo Perform spatial joins to add county, zip code, TAZ, and land use information

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Travel Survey Comparison

INRIX trip data is compared to DVMT and time of day distributions from NHTS and regional travel surveys

Trend are similar with the exception of the INRIX data showing:• Slightly lower DVMT (median value of less than 20 miles)• Depressed trip counts during AM peak hour (possible time zone issue)

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MORPC Travel Model

MORPC has provided NREL with output of

their travel demand model from a 2015

simulation with trip counts by TAZ (see right).

TAZ Trip Count Percentiles

Linear Correlation of MORPC demand model

with INRIX trip data shows an R2 value of 0.51

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• Provider typeo Consumer or Fleet

• Driving profileo Consumer vehicleo Field service / local deliveryo For hire / private truckingo Taxi / shuttle / town car serviceo Unknown

• Vehicle weight classo Light, medium, heavy, or unknown

• Probe source typeo Embedded GPS, mobile device, unknown

Down sampling INRIX Data

INRIX aggregates GPS data from multiple sources (providers). For the Columbus

EVSE analysis, we down sample to approximately 14% of the larger dataset

(based on data quality and provider type).

NREL down sample46.7k device ids

4.48M trips35.8M miles

Provider meta data

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MORPC Land UseMORPC has also provided NREL with

their land use datasetContains 663k polygons classified into 37 types including agriculture, office, commercial, industrial, park, public use, and residential

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MORPC Land Use (example zoom on CMH)

Airport

Park

Community Commercial

Moderate Suburban

Low Urban

Open Space

Education

Warehouse

Office

ID long term parking at airport?

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Cross reference INRIX with MORPC land use

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Residential

Non-Residential

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Residential

Non-Residential

Potentially of interest for EV charging locations

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Non-residential locations can be qualitatively segmented based on their suitability as site hosts for PEV charging

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High frequency;typically low dwell time

Low frequency;typically high dwell time

Low frequency;typically low dwell time

Moderate frequency;typically moderate dwell time

35


36


Residential

Public Use

Industrial

Commercial

EVI-Pro Methodology

38

Electric Vehicle Infrastructure Projection Tool

• PEV driving/charging simulator + Real-world travel profiles

• Economically efficient consumer charging behavioro Home dominant (default scenario)

• Estimates EVSE requirements foro PHEV and BEV powertrainso Single- and multi-unit dwellingso Weekday and weekend travel

What is EVI-Pro?

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• Anticipate spatial and temporal consumer demand for charging

o Specifically individuals with inconsistent access to home chargingo Model considers MUD residents as a special class of consumer

• Capture variations with respect too Residents of single- and multi-unit dwellingso Weekday/weekend travel behavioro Regional differences in travel behavior and vehicle adoption

• Fundamental assumptiono Consumers prefer to maximize eVMT and minimize operating cost

Model Goals

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EVI-Pro Schematic

Vehicle Attributes

Travel Data

Driving/Charging Simulations(Optimize individual charging behavior)

Infrastructure Attributes

Spatial/Temporal Post Processing(Estimate potential for shared use of EVSE)

Weight and scale EVSE density

EVSE counts

Participation ratesCharging load profiles

Consumer eVMT benefitsIndividual charging sessions

EVSE densityEVSE utilization

EVSE countsEVSE countsSpatial Dimensionality

Travel DataTravel Data PEV Sales

Projections

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• Travel profiles are simulated using six vehicle models• A matrix of charging options are made available to

each combination of travel profile and vehicle type• Optimization algorithm selects charging behavior at

the household level to maximize eVMT and minimize charging cost

o Simulated consumers have an assumed preference for charging type and location (based on electricity price) which is home dominant by default

Vehicle / Infrastructure Attributes

Vehicle TypesPHEV20

PHEV40

PHEV60

BEV100

BEV200

BEV300

EVSE Type / Location Home-SUD Home-MUD Work PublicNone On/Off On/Off On/Off On/Off

Level 1 On/Off On/Off On/Off On/Off

Level 2 On/Off On/Off On/Off On/Off

DC Fast Excluded Excluded Excluded On/Off

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Single travel day from conventional vehicle in CHTS with 170 miles of driving in a single day


Dwell Hours

Work 8:20 AM 9:00 AM 32.8 5.00Public 2:00 PM 3:30 PM 68.9 0.25Public 3:45 PM 4:00 PM 6.3 0.25Public 4:15 PM 4:20 PM 0.9 0.67Public 5:00 PM 5:30 PM 9.2 0.25Public 5:45 PM 6:00 PM 5.0 0.50Home 6:30 PM 7:30 PM 46.8 12.83


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Dwell Hours



Home Work Public Public Public Public Home

NoneL1L2

None

L2

None

L2

None

L2DCFC

None

L2

None

L2DCFC

Public

NoneL1L2

None

L2DCFCDCFCDCFC

L1 L1 L1 L1 L1 L1

A large number of potential charging combinations exist for each individual travel profile

Example for BEV100

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Dwell Hours




NoneL1L2

None

L2

None

L2

None

L2DCFC

None

L2

None

L2DCFC

Public

NoneL1L2

None

L2DCFCDCFCDCFC

L1 L1 L1 L1 L1 L1

EVI-Pro allows users to manually restrict individual charging types

Level 1 charging at work and public locations is restricted in this example

Example for BEV100

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Dwell Hours




NoneL1L2

None

L2

None

L2

None

L2DCFC

None

L2

None

L2DCFC

Public

NoneL1L2

None

L2DCFCDCFCDCFC

L1 L1 L1 L1 L1 L1

EVI-Pro allows users to manually restrict charging to locations with some minimum dwell time

A 30 minute minimum dwell time requirement is enforced in this example

Example for BEV100

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Dwell Hours




NoneL1L2

None

L2

None

L2

None

L2DCFC

None

L2

None

L2DCFC

Public

NoneL1L2

None

L2DCFCDCFCDCFC

L1 L1 L1 L1 L1 L1

All remaining combinations of charging options are simulated with behavior linked at all location types

Results in 18 unique combinations of charging behavior

Example for BEV100

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EVI-Pro internally reviews all combinations of charging behavior (18 in this example)

Example for BEV100

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Any charging behavior combination that violates the minimum SOC threshold is discarded (20% SOC in this example)

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Iterat

e on i

nitial

SOC

to ba

lance

ener

gy

Take remaining charging scenarios and iterate on initial SOC to balance energy across the day (discard any scenarios that violate SOC threshold as a result of balancing)

Select scenario from remaining option that minimizing cost of charging to the consumer

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Destination Departure Arrival

Drive

Miles

Dwell

Hours

Simulated

Charging

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

Public 2:00 PM 3:30 PM 68.9 0.25 ---

Public 3:45 PM 4:00 PM 6.3 0.25 ---

Public 4:15 PM 4:20 PM 0.9 0.67 DCFC

Public 5:00 PM 5:30 PM 9.2 0.25 ---

Public 5:45 PM 6:00 PM 5.0 0.50 DCFC

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


In this example, optimal charging

scenario is:

Home-L1

Work-L2

Public-DCFC

Next step is to trim unnecessary

charging events (if possible)

DCFC

L2-Work

DCFC

L1-Home

51

Destination Departure Arrival

Drive

Miles

Dwell

Hours

Simulated

Charging

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

Public 2:00 PM 3:30 PM 68.9 0.25 ---

Public 3:45 PM 4:00 PM 6.3 0.25 ---

Public 4:15 PM 4:20 PM 0.9 0.67 DCFC

Public 5:00 PM 5:30 PM 9.2 0.25 ---

Public 5:45 PM 6:00 PM 5.0 0.50 DCFC

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


Second DCFC event of the day is

eliminated as vehicle can complete

return trip home without second

DCFC event

DCFC

L2-Work

L1-Home

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Lower Bound Upper Bound

Home SUD Charging Simulated participation rate less 5%

Simulated participation rate

Home MUD Charging Peak load (temporal) Simulated participation rate

Workplace Charging Peak load (temporal) Simulated participation rate

Public Charging Peak load (temporal) Spatial clustering(0.1mi L1L2, 10mi DCFC)

• Since EVI-Pro simulation of CHTS is intended to inform a number of PEV adoption scenarios, a rates approach is taken in which results of driving/charging simulations are normalized to calculate EVSE density requirements

• These calculations utilize outputs of the driving/charging simulations including: participation rates, charging load profiles, and individual spatial/temporal charging sessions

• For each EVSE category, upper and lower bound estimates are generated to convey relative degrees of uncertainty

Estimating EVSE Density

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Spatial Clustering in EVI-Pro

Public destination(no simulated charging)

Public destination(yes simulated charging)

Aggregate simulated charging events to hypothetical stations within 0.1 miles (L1/L2) and 10 miles (DCFC)

Preliminary Results

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For modeling purposes, existing fleet of HEVs and PEVs will be used to extrapolate spatial distribution of future PEV adoption • More comparable to spatial distribution of fleet• HEV registrations have shown to be a reasonable indicator of consumer

preference for PEVs

2015 Registrations by Zip Code20,668 total HEVs + PEVs

0 900

Existing Light-Duty Fleet (HEVs + PEVs)

56

Estimated EVSE Requirements

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Preliminary Results

Present day

EVI-Pro models availability of charging stations growing proportionally to the projected growth in PEV adoption.

Relative to existing infrastructure, EVI-Pro assesses the Columbus region as having an adequate level of DCFC, however the model projects that access to non-residential L2 charging needs to be expanded.

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Existing DCFC Stations

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Estimated Plug Counts by Zip CodeMUD L2 Non-Res-L2

Census data on population by housing type drives EVI-Pro to project the majority of MUD L2 in Franklin County.Travel between the counties of Union, Delaware, and Franklin drive the need for charging stations in that area.

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Top 100 Non-Residential L2 EVSE “Hot Spots”

1-mile bubbles are drawn around EVI-Pro’s top 100 projected charging hot spots

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Honda of America Manufacturing

Columbus Zoo & Aquarium / Wild Tides

Manufacturing Facilities

Georgesville Square / Holt Run Shopping

Shopping Centers

Shopping CentersThe Ohio State

University

DowntownIndustrial Park

Ohio Wesleyan / Delaware City and County Facilities

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Zoom to this location on next slide

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EVI-Pro Hot Spots and MORPC POI

South Pointe Business Park

Shopping Centers

Hotels

Hotels

Shopping Centers

MORPC points of interest (POI) are used to reference potential sites

within EVI-Pro hot spots

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