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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 1
Economic Viability and Environmental Impact of
In-Motion Wireless Power Transfer
Braden J. Limb, Zachary D. Asher, Thomas H. Bradley, Evan Sproul, David A. Trinko,
Benjamin Crabb, Regan Zane, and Jason C. Quinn
Abstract—In-motion charging of electric vehicles (EVs) using
wireless power transfer (WPT) represents an alternative to both
traditional internal combustion engine (ICE) transportation
and long-range EVs. This study focuses on understanding the
economics, environmental impact, and infrastructure rollout
of in-motion WPT applied to the U.S. transportation fleet.
The work represents a novel, large-scale integration of nu-
merous research methodologies previously presented by our
research group into a comprehensive study to thoroughly ad-
dress potential in-motion WPT implementation scenarios using
geographically-diverse datasets, validated vehicle models, real-
world drive cycles, variable vehicle adoption rates, and variable
infrastructure deployment rates. By using both in-motion WPT
and conventional charging infrastructure, the proposed vehicle
and roadway architectures satisfy 97.7% of the sampled 24-hour
drive cycles, a 22.4% increase over a baseline short range EV
without in-motion charging. Economic results show a national
return on investment but economic viability is dramatically
impacted by up-front capital costs and technology adoption.
An environmental impact assessment shows that total GHG
emissions from light duty vehicles and Class 8 trucks would be
reduced by 29.3 trillion kg CO2-eq. (30.6%) when compared to
Braden J. Limb is a graduate from the Department of Mechanical and
Aerospace Engineering, Utah State University, Logan, UT 84322 USA.
Zachary D. Asher is in the Department of Mechanical and Aerospace
Engineering at Western Michigan University, Kalamazoo, MI 49008 USA.
Benjamin Crabb is with Apogee Instruments, Logan, UT 84321 USA. Regan
Zane is in the Department of Electrical and Computer Engineering, Utah
State University, Logan, UT 84322 USA. Evan Sproul, David A. Trinko,
Thomas H. Bradley, and Jason Quinn are in the Department of Mechan-
ical Engineering at Colorado State University, Fort Collins, CO 80523
USA (e-mail: Jason.Quinn@ColoState.edu). Manuscript received May 2018.
Print ISSN: 2332-7782 Online ISSN: 2332-7782 Digital Object Identifier:
10.1109/TTE.2018.2876067
a business as usual scenario (i.e. a scenario were current overall
emissions trends continue) for the first 50 years of technology
deployment. These results demonstrate that in-motion charging
using WPT presents both economic and environmental benefits
when compared to conventional ICE transportation and a long-
range EV fleet.
Index Terms—Electric Vehicle, Battery, Electrification, Trans-
portation, Techno-economic Analysis, Life Cycle Assessment,
Environmental Impact Assessment
I. INTRODUCTION
Transportation is a primary consumer of energy in the US
[1]. The improved performance of electrical energy storage
and conversion systems has resulted in the commercialization
of a variety of electrified vehicles including hybrid electric
vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs),
and fully electric vehicles (EVs) [2–5]. But, consumer adop-
tion of EVs has been limited due to their restricted range,
long recharging times, and higher total purchase price com-
pared to traditional internal combustion engine (ICE) vehicles
[2, 5–8]. EV designs that seek to improve their range, and
thereby improve their consumer acceptability have focused
on integrating large onboard battery systems and high-speed
charging infrastructure despite the high cost and excess mass
implications of these technologies [6, 8, 9]. A new solution
to these issues is technology known as in-motion wireless
power transfer (WPT), dynamic charging, or charging while
driving (CWD). This technology can be a means to improve
the range, consumer acceptability, and costs of EVs without
requiring the integration of large capacity batteries as has
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2
been stated in overall system operation reviews [10–12],
technology reviews [9, 13–15], and new reviews published
in just the last two years [16–21]. By using this technology,
the battery capacity of the EV could be downsized, while
improving the range and utility of the EV.
Because of the potential benefits of in-motion WPT, nu-
merous research programs are dedicated to the realization of
in-motion WPT by focusing on aspects such as driving range
[22], energy transmission efficiency considerations [23, 24],
weather considerations [25], overall system design [26–31],
physical vehicle implementation [32–34], infrastructure im-
plications [35–38], and investigating traffic implications [39–
42].
But, few researchers are investigating life cycle envi-
ronmental and techno-economic considerations of in-motion
WPT. One example result is a demonstration of WPT buses
that can be less expensive to operate than stationary charg-
ing EVs [43], with large reductions in fuel costs (∼80%
reduction) compared to diesel buses [44]. Environmental
studies have shown WPT can realize environmental benefits
compared to traditional transportation systems [44–46]. In
more general, large-scale, and fleet-wide applications, in-
motion WPT has been demonstrated to realize economic
benefits characterizable as a national return on investment
(ROI) of between 5-12 years [46–49].
Based on the current state of the field, there exists the need
for higher-fidelity large-scale assessment of the economic
potential and sustainability of a in-motion WPT fleet in
the U.S. with sensitivity considerations, a scope that has
not been adequately addressed by previous research. Our
research group began filling this gap in 2015 with some
initial environmental improvement results [46, 48], initial
regional results [47], and most recently initial Class 8 truck
focused results [50], all of which have been presented as
short conference or workshop papers. The work represents
a novel, large-scale integration of each of these pieces into
a comprehensive study to thoroughly address potential in-
motion WPT implementation scenarios using geographically-
diverse datasets, validated vehicle models, real-world drive
cycles, variable vehicle adoption rates, and variable infras-
tructure deployment rates. To enable this type of large
scale assessment, this work includes comprehensive dynamic
vehicle energy consumption models integrated with second-
by-second real-world drive cycles, variable vehicle adoption
and infrastructure deployment rates, as well as Geographic
Information Systems (GIS)-based infrastructure locating to
evaluate and enable networks of in-motion WPT. Infras-
tructure rollout and vehicle modeling are integrated with
economic and environmental models to understand the costs
and benefits of in-motion WPT for transportation in the U.S.
II. ME TH OD S
Evaluation of a proposed in-motion WPT system requires
concurrent modeling of both the in-motion WPT infrastruc-
ture and the vehicle. Descriptions of the vehicle model, WPT
technology modeling, economic modeling, and environmen-
tal assessment are presented in the following sections.
A. Vehicle Modeling
Vehicle models were developed for both light-duty vehicles
and Class 8 trucks, which together represent 94% of vehicle
miles traveled in the U.S. [51]. Additionally, for both of these
vehicle types, representative ICE vehicle models and WPT
EV models were developed for multiple vehicle standards
such as mid-size, compact, etc.
The ICE vehicle models were adapted to represent light
duty vehicles and Class 8 trucks that are representative
of current U.S. vehicles. The ICE powertrain model is
representative of a conventional modern powertrain, model
details are shown in Appendix A. It includes a gasoline-
powered engine mated to a 6-speed transmission for the light
duty vehicle models and a general diesel powertrain for the
Class 8 truck models. Vehicle specifications were selected
such that the combined UDDS/HWFET fuel economy of
the modeled ICE vehicles was equal to the US average
vehicle at 21.4 mpg (11.0 L
/100km) for light duty vehicles
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 3
TABLE I: An example ICE and WPT vehicle model specifications for one vehicle standard.
Parameter Name Light Duty Class 8 Truck Light Duty Class 8 Truck
ICE ICE WPT WPT
Vehicle Mass (kg) 2,072 20,000 1,498 20,000
Tire Diameter (m) 0.81 1.04 0.63 1.04
Frontal Area (m2) 2.23 10 2.30 10
Maximum Engine Torque (Nm) 303 2,000 280 2,500
Drag Coefficient 0.4 0.6 0.28 0.6
Driveline Efficiency 0.9 0.9 0.9 0.9
Rolling Coefficient 0.008 0.008 0.008 0.008
and 5.8 mpg (40.6 L
/100km) for Class 8 trucks [52]. Vehicle
parameter specifications from one light-duty and Class 8 ICE
vehicle standard is shown in Table I. A direct comparison of
simulated and current energy consumption for the ICE light
duty vehicles and Class 8 trucks was performed to provide
model verification. The light duty ICE was within 1.3% (0.3
MPG) and 2.8% (0.5 MPG) for the Highway Fuel Economy
Test (HWFET) and Urban Dynamometer Driving Schedule
(UDDS), respectively. The ICE Class 8 truck was within
0.6% (0.04 MPG) and 4.5% (0.2 MPG) for HWFET and
UDDS 28 drive cycles, respectively.
Once verified, vehicle dimensions from the ICE models
were used in the WPT EV models with the required energy
management systems (WPT and supercapacitors) and driv-
etrain. The WPT EV model includes a battery for energy
storage, a motor/generator connected to a fixed gear ratio
transmission, and a WPT system for receiving power. To
enable high-rate WPT, both the light-duty and Class 8 EVs
incorporate a supercapacitor system in addition to its lithium-
ion battery [53–55]. WPT charges the supercapacitor directly
and thus the WPT rate is limited based on SOC of the ca-
pacitor system. Class 8 trucks can support multiple WPT re-
ceiving pads and thus WPT infrastructure is most constrained
by the design characteristics of the light duty vehicle fleet.
The longer length of Class 8 trucks can support 5 receiving
pads which is necessary to compensate for the 4.8x energy
consumption increase for these vehicles when compared to
light-duty vehicles. This 4.8x energy consumption increase
was determined by comparing the energy consumption from
the light-duty and Class 8 vehicle models. The efficiency of
WPT from the grid to the battery was modeled at 83% which
is consistent with previous research [56–58] when accounting
for misalignment and other factors but is conservative com-
pared to modern systems that are capable of operating in
the 92-94% efficiency range for well-aligned instances [59].
Note that this energy loss is accounted for in fueling costs
(e.g. cost paid by the consumer) in the economic model as
discussed in Section II-B2. Control of the capacitor discharge
rates limits battery charging to 2C which is consistent with
existing research establishing that this rate ensures normal
battery life [60, 61]. Regenerative braking allows for the
recapture of vehicle kinetic energy during braking. Stationary
charging is assumed to be ubiquitous and to occur at any
location where the EV stops for more than 1 hour. No change
in speed or driver behavior is required to accomplish this
notional in-motion WPT. The performance of these vehicles
was scaled such that both the WPT EVs and the ICE vehicles
have the same 0-60 mph (0-102 kph) time. Vehicle parameter
specifications from one light-duty and Class 8 WPT vehicle
standard is shown in Table I.
B. Infrastructure and Vehicle Design
This study seeks to concurrently evaluate a range of infras-
tructure and vehicle designs so as to understand the costs and
benefits of in-motion WPT in the US. The infrastructure of
in-motion WPT and stationary chargers is characterized by
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 4
the power of WPT charging that is installed on US roadways
(between 25 kW and 100 kW were considered), and the
power of stationary charging infrastructure (between 1C and
3C was considered). The WPT-enabled vehicle designs are
characterized by the EV range of the vehicles (between 0
mi and 300 mi (483km) were considered). This analysis was
completed to find a suitable vehicle architecture for in-motion
WPT. A summary table of these ranges is shown in Appendix
A, Table III.
The WPT coverage on vehicle roadways is implemented
on a per-lane, per-mile basis where coverage for that lane
and mile is continuous for the specified power level which
corresponds to evenly spaced WPT pads. A diagram of the
proposed system is shown in Appendix A, Fig. 8.
By evaluating the design space of WPT-enabled vehicles
and their associated infrastructure, we seek to compare
tradeoffs among in-motion WPT infrastructure and vehicle
characteristics. The notional in-motion WPT infrastructure
and vehicle designs proposed in this study are evaluated using
three key performance metrics presented in the following
section.
1) Evaluation of the fraction of US driving met by in-
motion WPT:
The first metric is the fraction of U.S. simulated driving
that can be met in the long-term using 50 kW in-motion WPT
vehicles and infrastructure [62]. Drive cycles are achieved if
there is sufficient energy in the battery at all points in the
drive cycle. This is an important consideration because the
vehicles are frequently operating on roadways that are not
equipped with WPT. Note that for drive cycles that are very
short, we still assume that if the vehicle is on a WPT roadway,
they are using the WPT system. It may be the case that
those vehicles may replace all battery charge using stationary
charging despite the inconvenience because it has a lower
cost, but fully understanding that tradeoff would require a
consumer preference study which is outside of the scope of
this work and is anticipated to have a minimal impact.
To evaluate this metric we modeled the energy consump-
tion and associated battery SOC of the in-motion WPT EVs
on a second by second basis as they traverse geographically-
specific longitudinal real-world drive cycles, geographically-
specific in-motion WPT infrastructure, and notional sta-
tionary charging infrastructure. A 24-hour drive cycle was
satisfied if the WPT-enable EVs SOC remained above 0%
throughout the drive cycle. If, at any point, the vehicles
SOC fell below 0%, the drive cycle was not satisfied by
that particular WPT-vehicle dyad. Note that current hybrid
electric vehicles and plug-in hybrid electric vehicles reserve
some battery SOC for continued overall charge sustaining
operation, typically around 10% [63]. But for future in-
motion WPT vehicles that would exist in an environment
of improved connectivity and battery SOC estimations there
is no operational reason to leave a reserve of battery SOC,
despite that there is a degraded performance when SOC<5%
[64]. If this limit were increased and the battery size was kept
the same, the drive cycle satisfaction would slightly decrease.
Longitudinal drive cycle data from 6 geographically di-
verse locations across the U.S. (California; Southern Cal-
ifornia; Atlanta, GA; Chicago, IL; Kansas City, MO; and
Texas) was gathered from the National Renewable Energy
Laboratorys (NRELs) Transportation Secure Data Center
(TSDC) which includes light-duty vehicle drive cycles as
well as Class 8 truck drive cycles [50, 62]. Class 8 trucks
cannot achieve the same drive cycles that light-duty vehicles
can due to their slower acceleration and in general there
are significant differences between light-duty vehicle drive
cycles and Class 8 truck drive cycles. Each GPS-tagged
drive cycle in the database was referenced to its roadway
locations and classifications (primary, secondary, local, etc.)
as determined through the United States Census Bureaus
Topologically Integrated Geographic Encoding and Referenc-
ing (TIGER) database. The result was a sample set of 17,636
geographically-specific 24-hour duration drive cycles derived
from 6,254 instrumented vehicles. An example of three of the
drive cycles from this dataset are shown in Fig. 1.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 5
0 2 4 6 8 10 12 14
0
20
40
60
Time (h)
Velocity(mph)
0 1 2 3 4 5 6 7
0
20
40
60
80
Time (h)
Velocity(mph)
0 2 4 6 8
0
20
40
60
80
Time (h)
Velocity(mph)
(a)
(b)
(c)
Fig. 1: Three random day-long drive cycles used in this study
showing different driving behavior from the Austin region
(a), the Atlanta region (b), the LA region (c).
2) Evaluation of infrastructure and fueling cost of in-
motion WPT:
The second metric is the capital and operating costs of
the in-motion WPT vehicles and infrastructure. To evaluate
this metric, we calculate the incremental costs of the WPT
EV infrastructure and operating costs over a 25 year time
horizon.
Light duty ICE vehicle and Class 8 truck purchase prices
were set at $34,372 and $150,000, respectively, correspond-
ing to the average purchase price of vehicles in September
2016 [65]. Literature has shown that WPT EVs allow for a
large reduction in battery size compared to traditional EVs
resulting in lower purchase costs, therefore the purchase price
of both light duty and Class 8 truck WPT EVs was set at
30% cheaper than its ICE counterpart which is consistent
with previous research [56, 66]. The cost of batteries are
$230 per kWh, the cost of the wireless pads are $40 per
kW of WPT power, and the cost of the super capacitors
are $2,400 per kWh based on previous research [67, 68].
Note that a $40 per kW of WPT power for the wireless
pads is conservative when accounting for just pad material
but assumes significant reduction in cost if this number is
to include the compensation network and power electronics.
Also note that all of these costs are incorporated in the
model and the supercapacitor cost is much higher than the
battery cost. Modeled vehicles were driven an average annual
vehicle distance of 11,287 mi
/year (18,165 km
/year) for light
duty vehicles and 65,897 mi
/year (106,052 mi
/year) for Class 8
trucks [51]. Energy transfer efficiency losses for in-motion
WPT are incorporated into vehicle fueling costs (paid for
by the consumer) in the proposed model. All vehicles are
replaced after their 15 year life [69] has elapsed. Batteries
are assumed to last the life of the vehicle based on the slow
charge rate which corresponds to long lifetime [70].
The baseline cost for retrofitting roadways with WPT
used is $2.5 million per lane per mile which can deliver a
continuous 50kW from the evenly spaced WPT pads and is
consistent with previous research [71]. A detailed breakdown
of this cost can be found in Appendix A, Table IV. But, this
$2.5 million per lane per mile is subjected to a sensitivity
analysis to incorporate additional technical designs making
the cost used in the model $2.5 million ±$1 million per
lane per mile. Note that the range of $2.5 ±$1 million
encompasses the technical systems being developed today.
Four examples include the On-Line Electric Vehicle (OLEV)
inductive wireless system which is currently priced at $1.7
million per lane per mile [43, 72], the Primove Bombardier
inductive power system which costs $7 million per lane per
mile but this high power only requires 35% coverage which
works out to $2.45 million per lane per mile [73, 74], the
Siemens e-Highway conductive overhead line system which
costs $2.1 million per lane per mile [75], and lastly the
Utah State University system which is currently estimated
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 6
at $3.8 million per lane per mile but only requires 50%
coverage which works out to $1.9 million per lane per
mile effectively. This research is intended to provide life
cycle environmental and economic analysis for all of these
technical configurations thus the range of $2.5 ±$1 million
per lane per mile is used. In general, this cost is based on
retrofitting existing roadways and includes WPT electronics
(40%), electric grid power delivery and infrastructure (10%),
and resurfacing (50%). Maintenance costs associated with
this system are estimated to be equivalent to the maintenance
costs of conventional roadways which is consistent with pre-
vious research [11, 43, 76–78]. Note that scenarios assuming
different payers for the infrastructure cost are not anticipated
to significantly change the payback/recovery analysis and
instead an analysis of relevant profitability can be shown.
Operation and maintenance costs are the sum of fuel-
ing/charging costs and maintenance. The cost of energy
under the U.S. Energy Information Administration’s short
term energy outlook for each region was used to define the
gasoline and electricity cost for each US state [79]. Over the
life of the analysis, gasoline prices are modeled to increase
at 1.7% per year for 25 years, and electricity prices are
modeled to increase at 0.2% per year for 25 years [80].
Maintenance costs of ICE and EVs were set at 4% and 2%
of the purchase price per year, respectively, for all vehicle
classes [81, 82]. Vehicle-level costs are then scaled to a
national level using the average vehicle miles traveled per
classification of roadway in each state.
3) Evaluation of environmental costs and benefits:
The last metric of interest is the environmental costs and
benefits of the in-motion WPT fleet. The well-to-wheels
environmental impacts of the conventional ICE and in-
motion WPT vehicles are evaluated using Argonne National
Laboratory’s Greenhouse Gases, Regulated Emissions, and
Energy Use in Transportation (GREET) model using the
energy consumption results from vehicle modeling. Energy
and emissions impacts of vehicle manufacturing and electri-
fied roadway construction are excluded from this analysis.
The emissions intensity of the electric grid is assumed
unchanged over the lifetime of this study (continuing the
current trends in decreasing the emissions intensity of the grid
would further benefit the in-motion WPT EV in comparison
to ICE vehicles). Greenhouse gas emissions of electricity
are used based on the North American Electric Reliability
Council (NERC) region in which the WPT charging load
occurs. Greenhouse gas emissions of gasoline assume con-
ventional reformulated gasoline without modeling of regional
or seasonal differences. Environmental costs and benefits are
presented using metrics of greenhouse gas (GHG) emissions,
and the criteria pollutants VOC, CO, NOx, PM2.5, PM10, and
SOx. The role of future restrictions on pollutants were not
considered, Hazardous Air Pollutants Maximum Achievable
Control Technology (HAPs MACT) regulations, California
AB32, and other similar policies.
C. Infrastructure Rollout Scenario
A notional infrastructure rollout plan shown in Fig. 2
with a deployment at 13,788 miles per year is proposed
to understand the long-term dynamics of in-motion WPT
infrastructure construction, vehicle fleet adoption, and envi-
ronmental benefits. In-motion WPT infrastructure is assumed
to be deployed at a rate of 13,788 electrified roadway miles
(22,190 km) per year (equal to the average number of
centerline miles of new roads built per year from 2000 to
2013 in the U.S. [83]), but this value was also subjected to
a sensitivity analysis. WPT rollout was prioritized so that
“primary” roadways (e.g. interstate and other freeways, and
expressways) were retrofitted with in-motion WPT hardware
first. Only when all primary roadways were completed is
retrofitting of “secondary” roadways (other principal arterial
and minor arterial) begun [84, 85]. Local roads and secondary
roadways of less than 30 mph (48 kph) speed limit are not
retrofitted under the proposed scenarios of this study. Note
that Fig. 2 shows the primary roadways electrified within the
first five years and the secondary roadways are electrified
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 7
Fig. 2: Map of potential electrified roadway deployment at 13,788 miles per year. Light red lines represent primary roadways
electrified first and dark red represent primary roadways electrified last. Blue lines represent secondary roadways electrified
first and green lines represent secondary roadways electrified last.
starting in year five.
In all vehicle classes, WPT vehicle sales are modeled to
increase at a rate of 10% per year, and the number of new
vehicles purchased is modeled at 7.2% of registered vehicles
[86, 87]. The size and vehicle class breakdown of the U.S.
vehicle fleet is unchanged over the scenario period in the
model.
III. RES ULTS
Results analyzing the fraction of US driving met by in-
motion WPT, infrastructure and fueling costs, as well as en-
vironmental costs and benefits are presented in the following
sections.
A. Fraction of US driving met by in-motion WPT in light
duty vehicles and Class 8 trucks
Using the sample of geographically-realized longitudinal
driving traces and the second-by-second models of vehicle
energy consumption and battery SOC, we can model the frac-
tion of U.S. driving that could be enabled under the baseline
infrastructure rollout scenario. The results are presented on
a TSDC-database-specific basis in Table II. The results for
light-duty vehicles and Class 8 trucks broken out individually
exhibited the same overall trend.
First, the results without in-motion WPT are considered.
For EVs with a very small 25 mi (40 km) range, the vehicle
is only able to meet the 24-hour energy demands of the drive
cycle for between 70.5%-86.3% of the driving samples. This
implies that between 29.5% and 13.7% of the daily driving
samples cannot be met by such a low-range EV. For EVs
with a 300 mi (483 km) range, the vehicle is able to meet
the 24-hour energy demands of the drive cycle for between
99.4% and 100% of the driving samples. Only small fractions
of the daily driving samples cannot be met by this long-range
EV.
Comparing these results to the results for vehicles that can
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 8
TABLE II: Results of the TSDC study showing the percent
of drive cycles satisfied with a 25 mile (40 km) range EV, a
300 mile (483 km) range EV, and a 25 mile (40 km) range
EV with in-motion WPT.
Study 25mi 300mi 25mi Total
Region (40km) (483km) (40km) Cycles
range range range Analyzed
EV EV EV+WPT
Atlanta 70.5% 99.4% 98.9% 8,586
California 75.1% 99.6% 93.0% 3,255
Chicago 79.9% 99.8% 99.3% 1,625
Kansas City 73.6% 100% 98.9% 360
Texas 86.3% 100% 98.2% 2,597
S. California 79.9% 100% 97.7% 1,213
Average 75.3% 99.6% 97.7% 17,636
perform in-motion WPT, we can see that in-motion WPT
significantly increases the fraction of daily trip chains that
can be met using the small range (25 mi, 40km) EV. Under
the baseline in-motion WPT infrastructure rollout scenario,
between 93% and 98.9% of the daily driving samples can
be met by this short-range WPT-enabled EV. Note that if the
proposed infrastructure deployment rates are not reached, a
slightly higher range WPT-enabled EV may be necessary.
B. Vehicle, Infrastructure, Operations, and Maintenance
Costs
Economic results are developed first on a vehicle level
and then on a national level. Fig. 3 presents the national
average vehicle purchase, operations, and maintenance costs
on a per mile basis, and shows that summed costs for the
WPT vehicles decrease by 44.8% for light duty vehicles and
by 63.2% for Class 8 trucks, compared to conventional ICE
vehicles. For each case, and each cost category (maintenance,
purchase, and operations) the in-motion WPT EV is found
to have cost savings relative to ICE vehicles. Note that oper-
ational costs are fueling/charging costs costs averaged across
all computed geographic operation regions as described in
Section II-B2.
$0.00 $0.25 $0.50 $0.75
Truck
WPT EV
Truck
ICE
Light Duty
WPT EV
Light Duty
ICE
Cost Per Mile ($)
Operation Purchase Maintenance
Fig. 3: Vehicle level costs for each vehicle class and architec-
ture type. For the results presented, the operational costs are
a weighted average of state level operational costs based on
vehicle miles traveled on primary and secondary roadways.
Including the cost of the in-motion WPT infrastructure
with vehicle-level cost savings allows for the modeling of
national economic costs and the payback period. Payback
is achieved by a reduction in cost from fuel delivery and
availability. The baseline scenario assumes 13,788 roadway
miles electrified per year, and a retrofitting cost of $2.5
million per lane per mile ($1.6 million per lane per km)
±$1 million per lane per mile ($0.6 million per lane per km)
as shown in Fig. 4. For the baseline infrastructure rollout
and costing scenario, the payback period is 32 years. Higher
rates of infrastructure rollout increase the cost, but decrease
the payback period. Higher infrastructure costs can delay
the payback period relative to the baseline by up to 10
years. Note that the range of $2.5±$1 million per lane per
mile encompasses numerous technical systems currently in
development as described in Section II-B2.
C. Environmental Results
Results comparing the vehicle-level environmental impacts
of both conventional ICE vehicles and WPT EVs is shown
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 9
Fig. 4: National Cost/Revenue curves for varying roadway
miles electrified each year with sensitivity between costs of
$1.5 million per lane per mile ($0.9 million per lane per km)
and $3.5 million per lane per mile ($2.2 million per lane per
km).
in Fig. 5. All criteria pollutants, except for PM2.5and SOx,
decrease by moving to an electrified transportation system.
Both PM2.5and SOxincrease due to the PM and SOx
emissions of electricity generation in the Midwestern region
of the U.S. An average reduction of GHGs of 66.0% and
72.4% is experienced by moving from ICEs to WPT EVs for
light duty vehicles and Class 8 trucks, respectively. However,
large differences in environmental impact are seen depending
on geographic location. These GHG savings range from
35.3% for light duty vehicle and 48.8% for Class 8 trucks in
Hawaii, to 81.4% for light duty vehicles and 84.7% for Class
8 trucks in Connecticut. These results are consistent with
other studies evaluating the environmental costs and benefits
of electrified transportation [88].
Fig. 6 depicts the total amount of U.S. GHG emissions
with respect to time from light duty vehicles and Class 8
trucks under the baseline technology adoption scenario. As
0 100 200 300 400 500
GHG
Emissions (g of CO2
/mi)
0 1 2 3
CO
Pollution (g
/mi)
0 0.1 0.2 0.3 0.4
PM2.5
PM10
SOx
NOx
VOC
Pollution (g
/mi)
Light Duty ICE
Light Duty WPT EV
Fig. 5: Environmental impact comparison of light duty ICE
vehicle and WPT EV for U.S. Results presented are a
weighted average of state level results based on vehicle miles
traveled on primary and secondary roadways.
WPT EVs replace ICE vehicles the overall emissions from
the transportation sector decreases as do the emissions from
the ICE vehicles to be consistent with current trends. By
year 63 all ICE vehicles are replaced by WPT EVs. The
total emissions savings over the 50-year life of the system
(excluding the environmental costs of the infrastructure con-
struction) is 29.3 trillion kg CO2-eq. or a 30.6% CO2-eq.
reduction compared to a business as usual scenario (i.e. a
scenario were current overall emissions trends continue) as
shown in Fig. 6.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 10
0 10 20 30 40 50
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Year
CO2Equivalent GHG Emissions (quadrillion grams)
Class 8 ICE Light Duty ICE
Class 8 WPT EV Light Duty WPT EV
Fig. 6: Varying emissions from light duty vehicles and Class
8 trucks as the in-motion WPT technology is adopted.
IV. DISCUSSION
These results demonstrate that the baseline in-motion WPT
infrastructure scenario can realize vehicle-level and national-
level economic and environmental benefits, while satisfying
the sample set of US drive cycles at similar levels of
satisfaction as an advanced EV with 300 mi (483km) range.
The results are applicable to both the light-duty and Class 8
truck fleet.
The results also demonstrate a near-term means to be able
to electrify long-distance trucking, a transportation sector
particularly resistant to efficiency improvements, electrifi-
cation, and fueling changes. As an example, many studies
of transportation electrification have proposed that long-
distance Class 8 trucking is impervious to the economic and
environmental benefits of electrification [89, 90]. Longer-
term studies, seeking to understand the means to electrify
freight transportation in the US, have converged on electrified
rail transport as a means to reduce the environmental costs
of freight transport [91]. Studies have estimated the cost of
rail electrification infrastructure at between $4.8 million per
track mile ($3.8 million per km) [92] and $55 million per
track mile ($34 million per km) [93]. Economic comparison
of in-motion WPT to electrified rail demonstrates that the
in-motion WPT freight transportation system can realize
the environmental benefits of freight electrification but at a
significantly lower cost than electrified rail.
To understand these tradeoffs between the costs of in-
motion WPT infrastructure and the benefits realized, we have
investigated three additional infrastructure rollout scenarios
which are shown in Fig. 7. Satisfaction for both drive cycle
and vehicles using in-motion WPT was evaluated for four
varying infrastructure deployment scenarios which are shown
as red x’s on the plot. From left to right, these scenarios in-
clude: no in-motion WPT corresponding to 72% satisfaction,
all primary roadways which corresponds to 87% satisfaction,
all paved roadways (primary, secondary, and local) with
speed limits greater than 60 mph (97 kph) which corresponds
to 92% satisfaction, and a customized infrastructure (primary
and secondary roadways) with speed limits greater than 30
mph (48 kph) which corresponds to 98% satisfaction. A
exponential fit was applied to these three data points to
estimate the cost for drive cycle and vehicle satisfaction
with the technology. For all scenarios, a 25 mi (40km) EV
range, in-motion charging at 50 kW, and stationary charging
at locations stopped greater than 1 hour was assumed for all
vehicles. As an example, if 20,682 miles were electrified per
year at a cost of $2.5 million per lane per mile, the payback
period for 100% satisfaction would be approximately 44
years, for 98% satisfaction it would be approximately 28
years, for 92% satisfaction it would be approximately 12
years, and for 87% satisfaction it would be approximately
4.3 years.
The resulting curve for drive cycle satisfaction exponen-
tially increases from the case when no in-motion charging
takes place (72%) up to $2.26 trillion for 100% drive cycle
satisfaction, shown in Fig. 7. In order to achieve the last 2.3%
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 11
of drive cycle satisfaction, an additional capital investment
of $0.8 trillion is required corresponding to an infrastructure
cost 1.5x more than the original investment that satisfies 98%.
However, results show that this technology is expected to
make a profit of $124 billion annually once all high-speed
primary and secondary roadways are electrified representing
a significant opportunity to electrify other roadways and
improve technology adoption. Additionally, the sensitivity
analysis showed that even if part of the infrastructure is built,
there are still significant environmental benefits realized.
60%70%80%90%100%
$0
$1
$2
Percent Satisfaction
Infrastructure Cost (Trillion $)
Drive Cycle Data Point
Drive Cycle Curve Fit
Fig. 7: An infrastructure cost vs drive cycle satisfaction curve
for real world drive cycles and vehicles.
V. CONCLUSIONS
This study has sought to understand the economic and
environmental costs and benefits of an in-motion WPT based
automotive transportation system. Vehicle energy consump-
tion was modeled over real-world drive cycles under a set
of scenarios of vehicle adoption, infrastructure deployment
rates, in-motion WPT, and vehicle technologies. The baseline
WPT infrastructure proposal retrofits all high-speed (greater
than 30 mph, 48 kph) primary and secondary roadways in
US with in-motion WPT infrastructure. Coupled with more
conventional stationary charging, this infrastructure enables
a light-duty and Class 8 fleet of EVs with 25 mi (40 km)
range to meet 97.7% of the sample set of n=17,636 24-hour
longitudinal drive cycles. Time realized economic models
show a national ROI of 32 years with $98 billion per year
in savings under conservative electricity and fueling cost
projections. The environmental benefits of in-motion WPT-
based electrification of the vehicle fleet are also sizable.
The total emissions from light duty vehicles and Class 8
trucks will be reduced by 29.3 trillion kg CO2-eq. (30.6%
reduction) when compared to a business as usual scenario
for the first 50 years of technology deployment. Overall,
results show that in-motion charging using WPT can realize
both economic and environmental benefits when compared
to either conventional ICE transportation or a long-range EV
fleet.
In-motion WPT is particularly important to long range
trucking applications which have historically been difficult
to demonstrate environmental and cost benefits from elec-
trification. Additionally, in-motion WPT provides a signif-
icantly cheaper alternative to electrified rail transportation
solutions which is the current state-of-the-art for long term
sustainable freight transportation. Overall in-motion WPT
is a unique solution to transportation sustainability because
it allows vehicles to operate at a much lighter weight, it
does not require extensive large battery manufacturing, and
it is easily deployable through current vehicles and road
systems. Future work will focus on additional economic
modeling details such as the impact of varying interest rates
on system profitability as well as incorporating other near-
future technologies such as autonomous vehicle operation
and predictive energy management for improved environmen-
tal and economic benefits.
APPENDIX A
ADDITIONAL ANALYS IS DE TAILS
The ICE vehicle models (discussed in Section II-A) were
developed using an overall force balance on the vehicle which
provides the propulsive force required to propel the vehicle,
Fprop, defined as
Fprop =m˙v+Crr mg +1
2Cdρairv2Afront +mg sin θ(1)
where Crr is the coefficient of rolling resistance, mis the
mass of the vehicle, gis the acceleration due to gravity
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 12
(9.81 m
/sec2),Cdis the coefficient of drag, ρair is the density
of air (1.1985 kg
/m3),vis the vehicle velocity, Afront is
the frontal area, ˙vis the vehicle acceleration (calculated
using a numerical derivative), and θis the elevation angle.
Starting from this overall equation, detailed models of various
powertrains were developed in Matlab/Simulink.
Once verified, vehicle dimensions from the ICE models
were used in the WPT EV models which also incorporate
the necessary parts for in-motion WPT as shown in Fig. 8.
Fig. 8: Diagram of the in-motion WPT system implementa-
tion.
The range of WPT power charging that is installed on
US roadways, the power of stationary charging infrastructure,
and the EV range of the vehicles was varied (as discussed in
Section II-B) and a summary table of these ranges is shown
in Table III.
As discussed in Section II-B2, the cost of WPT installation
was set at $2.5 million per lane per mile based on the detailed
calculations shown in Table IV. If the delivered power level
increases, an increased form factor for the electronics would
slightly increase the roadway cost but may significantly
increase the electronics and grid connection which is why
a sensitivity analysis of the $2.5 million per lane per mile
cost was conducted. The easiest way to increase the power to
the vehicle is multiple receiver pads on the vehicle. Based on
vehicle modeling, it is expected that the 50 kW is sufficient,
however for larger vehicles, multiple pads could be required.
TABLE III: Analysis of ranges of WPT power levels, sta-
tionary charging levels, and ranged vehicles.
Battery Range WPT Supercapacitors Satisfied
25 0 0 79.8%
25 25 7 91.0%
25 25 10 94.5%
25 25 13 96.0%
25 50 13 97.8%
25 50 20 99.0%
25 100 50 98.6%
30 25 13 97.3%
30 0 0 83.7%
30 50 17 99.3%
30 50 13 99.0%
20 25 13 91.4%
20 0 0 73.6%
35 0 0 87.1%
35 50 10 98.8%
35 25 13 97.7%
300 0 0 99.6%
TABLE IV: A breakdown of the $2.5 million per mile
per lane cost equating to $960K electronics, $240K grid
connection, and $1.2 million roadway.
Onsite
Category Time Task Cost
(Days)
Traffic Control 1 Median (Barrier) $61,855
Signs $1,553
Cones $225
Milling 1 Mill $5,835
Haul milled material $63,318
Surface Grader $713
Placing Materials 1 WPT Pads $50,807
Dowel Bars $60,905
Placing Concrete 1 PCC Material Cost $389,570
Portable Batch Plant $978
Pour/ Transport Concrete $11,651
Paver $1,741
Curing/Finishing 7 Surface Treatment $488,717
Broom Finish $63,712
Total 11 Total $1,201,581
ACKNOWLEDGMENT
The authors acknowledge financial support from the Utah
Science, Technology, and Research Initiative (USTAR), Utah
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 13
State University, Colorado State University, and the Gov-
ernors Office of Energy Development in Utah. They also
acknowledge research support from the Electric Vehicle
and Roadway (EVR) group at Utah State University and
additional support from Benjamin Vegel, Kelli Morrill, and
Danna Quinn.
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Braden J. Limb received a B.S. and M.S. in
Mechanical Engineering with a minor in Mathe-
matics from Utah State University. Braden worked
as a Research Assistant in the Department of
Mechanical & Aerospace Engineering at Utah
State University. His research includes Techno-
Economic Assessment, Life Cycle Analysis, and
systems optimization of roadway electrification using wireless power transfer
through dynamic vehicle modeling.
Zachary D. Asher received a B.Sc. degree in me-
chanical engineering from Colorado State Univer-
sity, Fort Collins, CO, USA, in 2009, a M.Sc. de-
gree in mechanical and aerospace engineering from
the University of Colorado at Colorado Springs,
Colorado Springs, CO, USA, in 2012, and a Ph.D.
degree in mechanical engineering from Colorado
State University in 2018. He is currently an Assistant Professor in the
Department of Mechanical and Aerospace Engineering at Western Michigan
University, Kalamazoo, MI 49008 USA. His current research interests
include mathematical modeling for control and optimization of mechanical
systems.
Evan Sproul received B.Sc. and M.Sc. degrees
in mechanical engineering from the New Mex-
ico Institute of Mining and Technology, Socorro,
NM, USA in 2009 and 2011. He is a licensed
professional engineer in the states of Oregon and
Colorado and is currently pursuing a PhD degree
in mechanical engineering at Colorado State Uni-
versity, Fort Collins, CO, USA. His research focuses on assessing the
environmental impact and economic viability of new technologies.
David A. Trinko received the B.Sc. degree in
mechanical engineering from Colorado State Uni-
versity, Fort Collins, CO, USA, in 2017. He is
currently pursuing the M.S. degree in the Depart-
ment of Mechanical Engineering, Colorado State
University. His current research interests include
modeling and simulation for energy consumption
optimization of electrical and mechanical systems.
Bejamin Crabb is a Researcher I in the Wildland
Resources division of the S.J. & Jessie E. Quinney
College of Natural Resources at Utah State Uni-
versity.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 19
Regan Zane is a Professor of Electrical and Com-
puter Engineering and Founder and Director of the
Center for Sustainable Electrified Transportation
(SELECT) and Power Electronics Lab at Utah
State University. He leads a wide range of research
programs in power electronics for electric vehi-
cle drivetrains and charging infrastructure, battery
management systems, and dc and ac micro-grids including grid integration
of renewable energy sources and energy storage. His programs maintain a
strong emphasis on working with government and industry to develop and
transition technologies into the marketplace.
Thomas H. Bradley received the B.Sc. and
M.Sc.degrees in mechanical engineering from the
University of California at Davis, Davis, CA, USA,
in 2000 and 2003, respectively, and a Ph.D. de-
gree in mechanical engineering: system dynamics
from the Georgia Institute of Technology, Atlanta,
GA, USA, in 2008. He is currently an Associate
Professor with the Department of Mechanical Engineering, Colorado State
University, Fort Collins, CO, USA, where he is also the Associate Director
of Systems Engineering. His current research interests include the design of
automotive, aerospace, and energy systems, integrated controls and design
optimization, and the validation of engineering design methods.
Jason C. Quinn is an Associate Professor in the
Department of Mechanical Engineering at Col-
orado State University. His education and research
have always been centered on energy, with current
work focused on system evaluation of electric
transportation, spider silk, microalgae biofuels, and
fission power systems. Research efforts are dedi-
cated to the development of engineering system models validated through
experimentation and leveraged for techno-economic feasibility, life cycle
assessment, and resource demand of emerging technologies. Results from
modeling work are used to focus research and development efforts to high
impact areas. Dr. Quinn completed a masters degree at the University of
Wisconsin-Madison in nuclear engineering and engineering physics, and a
Ph.D. at Colorado State University.
