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Published: 8 April 2025
Citation: Kim, K.-H.; Kim, N.;
Vijayagopal, R.; Stutenberg, K.;
Hwang, S.-H. Impact of Regional and
Seasonal Characteristics on Battery
Electric Vehicle Operational Costs in
the U.S. Sustainability 2025,17, 3282.
https://doi.org/10.3390/
su17083282
Copyright: © 2025 by the authors.
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Article
Impact of Regional and Seasonal Characteristics on Battery
Electric Vehicle Operational Costs in the U.S.
Kyung-Ho Kim 1, * , Namdoo Kim 2, Ram Vijayagopal 2, Kevin Stutenberg 2and Sung-Ho Hwang 1,*
1Automotive Mechatronics Laboratory, Department of Mechanical Engineering, Sungkyunkwan University,
2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Republic of Korea
2Argonne National Laboratory, 9700 S. Cass Ave, Lemont, IL 60439, USA; nkim@anl.gov (N.K.);
rvijayagopal@anl.gov (R.V.); kstutenberg@anl.gov (K.S.)
*Correspondence: khkhh@g.skku.edu (K.-H.K.); hsh0818@skku.edu (S.-H.H.)
Abstract: This study investigates the operational cost competitiveness of battery electric
vehicles (BEVs) in the United States, considering regional climates, energy prices, and
driving patterns. By comparing BEVs with plug-in hybrid electric vehicles (PHEVs), hybrid
electric vehicles (HEVs), and the alternative use of BEVs and conventional vehicles (Convs),
the analysis incorporates thermal dynamometer tests, real-world vehicle miles traveled
(VMT), and state-specific energy prices. Using detailed simulations, the study evaluates
energy consumption across varying temperatures and driving distances. The findings
reveal that, while BEVs remain cost-effective for short trips in moderate climates, PHEVs
are more economical for long-range trips and cold environments, due to the excessive cost
of using external direct current fast chargers (DCFCs) and reduced BEV efficiency at low
temperatures. HEVs are identified as the most cost-efficient option in regions like New
England, characterized by high residential electricity prices. These insights are critical for
shaping vehicle electrification strategies, particularly under diverse regional and seasonal
conditions, and for advancing policies on alternative energy and fuels.
Keywords: electrified vehicles; operational cost; regional climate; energy consumption;
decarbonization
1. Introduction
Sales of battery electric vehicles (BEVs) have been consistently increasing in the United
States and worldwide [
1
]. Cost-effectiveness is considered one of the primary reasons
drivers choose BEVs [
2
,
3
]. Typically, BEVs are considered more economical in operation
than vehicles with internal combustion engines, primarily due to their higher energy
efficiency and the lower cost of electricity relative to fuel. However, the efficiency of
BEVs can be impacted by the characteristics of lithium-ion batteries, which perform less
effectively in colder climates [
4
–
6
]. Furthermore, unlike conventional vehicles, BEVs do not
utilize engine waste heat, therefore requiring additional energy for cabin heating, which can
lead to increased energy usage and diminished range in cold conditions. This correlation
between increased energy consumption and reduced range in low temperatures has been
well-established through various studies [7–12].
Beyond environmental conditions, operational costs for BEVs can also escalate when
charging occurs at commercial direct current fast chargers (DCFCs) rather than at residential
charging facilities or in regions where electricity costs are high [
13
]. Furthermore, if
the residential electricity price is not competitive, BEVs are not likely an economical
option. Sensitivity analyses were conducted in multiple cities worldwide to evaluate the
Sustainability 2025,17, 3282 https://doi.org/10.3390/su17083282
Sustainability 2025,17, 3282 2 of 20
profitability of electric taxis considering electricity prices [
14
–
17
]. The impacts of energy
prices on personal/shared electric vehicle operation have also been analyzed in several
studies [
18
–
22
]. The influence of these factors on operating costs is not visible through
standard fuel economy ratings. Instead, it requires an analysis of actual vehicle mileage
and regional specifics such as climate and energy costs.
Several studies have examined the influence of climatic conditions and driving be-
haviors on the energy needs of BEVs [
23
–
30
]. The impact of seasonal changes and usage
patterns on the electricity consumption of BEVs in Beijing, China, was explored in [
31
],
where driving patterns were classified using the k-means method, and energy consump-
tions were compared by application and season. The cost analysis of BEVs in Macau and
a comparison with conventional vehicles were carried out in [
32
]. A Belgian case study
for the emissions performance of BEVs was conducted in [
33
], considering the electricity
grid and charging and driving patterns. More recently, the operational costs of BEVs,
considering the regional factors of various cities, have been investigated as a part of a TCO
(total cost of ownership) analysis across the globe [
34
–
37
]. Focusing on North America,
Yuksel and Michalek [
38
] analyzed the energy consumption of BEVs across various regions
in the United States, taking regional temperatures and driving patterns into account to
study greenhouse gas emissions. The authors of [
39
,
40
] utilized the household travel GPS
data in Seattle to evaluate the annual operational costs of PHEVs and then compared them
with those of conventional vehicles. The authors of [
41
] compared the TCOs of different
powertrains of the same vehicle platform, Kia Niro, in North America. While individual
studies have introduced detailed analyses of specific characteristics such as climate, driving
patterns, and energy prices, a comprehensive approach that integrates these characteristics
is required.
This study aims to evaluate the competitiveness of BEVs in terms of operational costs,
considering factors such as climate, differences in electricity and gasoline prices, and the
distribution of miles traveled. Regional characteristics within the United States are gathered
from various sources and reflected in the analysis. For comparison, operating costs from
scenarios with multiple vehicle configurations were yielded. The thermal dynamo test
results of representative vehicles were utilized to make a realistic assumption of vehicle
efficiency in various climates. With this foundation, the novelty of this study lies in the
following aspects:
•
This study presents a methodology that can yield practical results by comprehensively
considering actual driving patterns, regional temperatures, and energy prices.
•
This study enables comparative analysis across different regions by targeting the entire
United States rather than a single region.
•
The study utilized results from dynamometer tests conducted under various temper-
atures, enabling the precise analysis of energy and fuel consumption across differ-
ent conditions.
This paper is structured as follows: The Second Section first explains the assumptions
made concerning vehicle configuration scenarios and trips, along with the methodology for
evaluating the operational costs. Data from multiple sources regarding annual and monthly
vehicle miles traveled (VMT), as well as daily trip lengths are combined to construct a
realistic driving scenario. The relationship between temperature and energy consumption
of different vehicle types, namely BEVs, PHEVs, HEVs, and Conv, is applied to evaluate
the electricity or fuel needs for each scenario. Consideration of electricity and fuel prices
depending on regions is followed to obtain the operational costs. The Third Section
addresses the result of the operational cost calculation for each scenario. The Fourth Section
compares the operational costs of different vehicle configurations to analyze the impact of
the regional and seasonal factors involved. Employing both U.S. map-based visualization
Sustainability 2025,17, 3282 3 of 20
and parameter-based X-Y plots enabled the identification of the factors contributing to
each vehicle option’s superiority under different conditions. Finally, the Fifth Section
summarizes the study and discusses potential future research directions and contributions.
2. Materials and Methods
This section is composed of step-by-step processes for the estimation of operational
costs for each vehicle configuration scenario. Figure 1shows the overall process of evaluat-
ing the operational costs. Section 2.1 explains the selected vehicle configurations. Section 2.2
proposes the assumptions adopted regarding the mileage requirements. Driving patterns
in each region and season and the overall traffic volume are combined to determine the
mileage requirements. Sections 2.3 and 2.4 address the impact of temperature on each
vehicle. The influence of different seasons is reflected in the operational costs through the
different monthly average temperatures of each region and season. Ultimately, regional elec-
tricity and gasoline prices are multiplied by the energy needs to yield the operational costs.
Sustainability 2025, 17, x FOR PEER REVIEW 3 of 20
Fourth Section compares the operational costs of different vehicle configurations to ana-
lyze the impact of the regional and seasonal factors involved. Employing both U.S. map-
based visualization and parameter-based X-Y plots enabled the identification of the fac-
tors contributing to each vehicle option’s superiority under different conditions. Finally,
the Fifth Section summarizes the study and discusses potential future research directions
and contributions.
2. Materials and Methods
This section is composed of step-by-step processes for the estimation of operational
costs for each vehicle configuration scenario. Figure 1 shows the overall process of evalu-
ating the operational costs. Section 2.1 explains the selected vehicle configurations. Section
2.2 proposes the assumptions adopted regarding the mileage requirements. Driving pat-
terns in each region and season and the overall traffic volume are combined to determine
the mileage requirements. Sections 2.3 and 2.4 address the impact of temperature on each
vehicle. The influence of different seasons is reflected in the operational costs through the
different monthly average temperatures of each region and season. Ultimately, regional
electricity and gasoline prices are multiplied by the energy needs to yield the operational
costs.
Figure 1. A step-by-step process for the estimation of operational costs.
2.1. Scenarios with Configuration of Vehicles
This study considers four scenarios with different vehicle configurations: BEV,
PHEV, BEV and Conv, and HEV. The vehicle is assumed to start fully charged daily using
residential charging equipment for both BEV and PHEV scenarios. In the BEV scenario, if
the total distance of all trips scheduled for a day exceeds the BEV’s range, it is assumed
that the vehicle needs to be recharged at a DCFC station to complete the trips. For PHEVs,
the vehicle initially operates in the charge-depleting (CD) mode to use all the electricity
charged overnight, and then switches to the charge-sustaining (CS) mode to complete the
remaining trips. The BEV and Conv scenario presumes the availability of both a BEV and
a conventional vehicle. The choice of vehicle depends on whether the total distance of all
scheduled trips for the day exceeds the BEV’s range; if it does, the conventional vehicle is
used; otherwise, the BEV is selected. In the HEV scenario, all daily trips are completed
using HEV alone. Figure 2 illustrates the trip management for each scenario. Note that the
Figure 1. A step-by-step process for the estimation of operational costs.
2.1. Scenarios with Configuration of Vehicles
This study considers four scenarios with different vehicle configurations: BEV, PHEV,
BEV and Conv, and HEV. The vehicle is assumed to start fully charged daily using res-
idential charging equipment for both BEV and PHEV scenarios. In the BEV scenario, if
the total distance of all trips scheduled for a day exceeds the BEV’s range, it is assumed
that the vehicle needs to be recharged at a DCFC station to complete the trips. For PHEVs,
the vehicle initially operates in the charge-depleting (CD) mode to use all the electricity
charged overnight, and then switches to the charge-sustaining (CS) mode to complete the
remaining trips. The BEV and Conv scenario presumes the availability of both a BEV and a
conventional vehicle. The choice of vehicle depends on whether the total distance of all
scheduled trips for the day exceeds the BEV’s range; if it does, the conventional vehicle
is used; otherwise, the BEV is selected. In the HEV scenario, all daily trips are completed
using HEV alone. Figure 2illustrates the trip management for each scenario. Note that the
Conv-only scenario is not addressed in this paper, as it is evident that HEVs will always be
superior in terms of operational costs under any conditions.
Sustainability 2025,17, 3282 4 of 20
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Conv-only scenario is not addressed in this paper, as it is evident that HEVs will always
be superior in terms of operational costs under any conditions.
Figure 2. Vehicle configurations in four scenarios.
2.2. Vehicle Miles Traveled (VMT) Assumptions
To accurately compute the operational costs of vehicles, it is essential to consider both
the total distance traveled monthly and the distance covered each day. Operational modes
can vary based on daily distances, significantly impacting costs. Data for annual VMT,
monthly VMT distribution, and daily travel distances were compiled by postprocessing
information from multiple sources. Operational costs are initially estimated using daily
travel data, which reflects the total distance traveled each day, assuming a single residen-
tial charging before traveling for BEVs and PHEVs. These daily estimates are then scaled
up to align with the monthly VMT, derived from combined annual VMT and monthly
distribution data, to ensure consistency and precision in the overall cost analysis across
broader temporal scales.
2.2.1. Household Travel Survey
In 2022, the Federal Highway Administration (FHA) conducted the National House-
hold Travel Survey involving 27,290 households nationwide [42]. Each household was
assigned a specific date to report on their travel activities for all household members. Data
associated with non-personal vehicles, public transit modes like airplanes and trains, and
pedestrian travel were excluded to maintain the focus on vehicle miles traveled. Addi-
tionally, reports from non-drivers were removed to prevent duplicate entries. Figure 3
presents a data histogram showing trip distances under 200 miles, categorized by regional
divisions. Figure 4a,b illustrate the monthly distribution of trip miles of the East South
Central Division and Mountain Division, respectively. Figure 3 shows that the occurrence
of trips decreases exponentially as the trip miles increase, regardless of divisions. How-
ever, the distribution of miles varies depending on the region, as shown in Figure 4. While
analyzing the correlation between region and seasonal factors and driving paerns, which
is beyond the scope of this study, the trip data were implemented separately for each re-
gion and season to reflect actual driving paerns.
Figure 2. Vehicle configurations in four scenarios.
2.2. Vehicle Miles Traveled (VMT) Assumptions
To accurately compute the operational costs of vehicles, it is essential to consider both
the total distance traveled monthly and the distance covered each day. Operational modes
can vary based on daily distances, significantly impacting costs. Data for annual VMT,
monthly VMT distribution, and daily travel distances were compiled by postprocessing
information from multiple sources. Operational costs are initially estimated using daily
travel data, which reflects the total distance traveled each day, assuming a single residential
charging before traveling for BEVs and PHEVs. These daily estimates are then scaled
up to align with the monthly VMT, derived from combined annual VMT and monthly
distribution data, to ensure consistency and precision in the overall cost analysis across
broader temporal scales.
2.2.1. Household Travel Survey
In 2022, the Federal Highway Administration (FHA) conducted the National House-
hold Travel Survey involving 27,290 households nationwide [
42
]. Each household was
assigned a specific date to report on their travel activities for all household members. Data
associated with non-personal vehicles, public transit modes like airplanes and trains, and
pedestrian travel were excluded to maintain the focus on vehicle miles traveled. Addi-
tionally, reports from non-drivers were removed to prevent duplicate entries. Figure 3
presents a data histogram showing trip distances under 200 miles, categorized by regional
divisions. Figure 4a,b illustrate the monthly distribution of trip miles of the East South
Central Division and Mountain Division, respectively. Figure 3shows that the occurrence
of trips decreases exponentially as the trip miles increase, regardless of divisions. However,
the distribution of miles varies depending on the region, as shown in Figure 4. While
analyzing the correlation between region and seasonal factors and driving patterns, which
is beyond the scope of this study, the trip data were implemented separately for each region
and season to reflect actual driving patterns.
2.2.2. Monthly VMT
Monthly VMT was derived by applying a monthly distribution factor to the annual
VMT. The net annual VMT by state was divided by the number of registered vehicles in
each state [
43
] to calculate the average annual VMT per vehicle by region. Figure 5shows
the average annual VMT per vehicle by state.
Sustainability 2025,17, 3282 5 of 20
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Figure 3. Distribution of trip distances across the U.S.
(a) (b)
Figure 4. Monthly distribution of miles driven by regions under 200 miles. (a) East South Central
Division. (b) Mountain Division.
2.2.2. Monthly VMT
Monthly VMT was derived by applying a monthly distribution factor to the annual
VMT. The net annual VMT by state was divided by the number of registered vehicles in
each state [43] to calculate the average annual VMT per vehicle by region. Figure 5 shows
the average annual VMT per vehicle by state.
Figure 5. Average annual VMT per vehicle by state.
Figure 3. Distribution of trip distances across the U.S.
Sustainability 2025, 17, x FOR PEER REVIEW 5 of 20
Figure 3. Distribution of trip distances across the U.S.
(a) (b)
Figure 4. Monthly distribution of miles driven by regions under 200 miles. (a) East South Central
Division. (b) Mountain Division.
2.2.2. Monthly VMT
Monthly VMT was derived by applying a monthly distribution factor to the annual
VMT. The net annual VMT by state was divided by the number of registered vehicles in
each state [43] to calculate the average annual VMT per vehicle by region. Figure 5 shows
the average annual VMT per vehicle by state.
Figure 5. Average annual VMT per vehicle by state.
Figure 4. Monthly distribution of miles driven by regions under 200 miles. (a) East South Central
Division. (b) Mountain Division.
Sustainability 2025, 17, x FOR PEER REVIEW 5 of 20
Figure 3. Distribution of trip distances across the U.S.
(a) (b)
Figure 4. Monthly distribution of miles driven by regions under 200 miles. (a) East South Central
Division. (b) Mountain Division.
2.2.2. Monthly VMT
Monthly VMT was derived by applying a monthly distribution factor to the annual
VMT. The net annual VMT by state was divided by the number of registered vehicles in
each state [43] to calculate the average annual VMT per vehicle by region. Figure 5 shows
the average annual VMT per vehicle by state.
Figure 5. Average annual VMT per vehicle by state.
Figure 5. Average annual VMT per vehicle by state.
Traffic volume distribution data were utilized to break down the annual VMT into
monthly VMT. Traffic data from approximately 5000 traffic counting locations nationwide
were monitored and used to estimate the total traffic volume every month by the Federal
Highway Administration [
43
]. For each state, the proportion of traffic in a single month to
Sustainability 2025,17, 3282 6 of 20
the total annual traffic was calculated. This ratio served as the monthly distribution factor
for the VMT. Figure 6displays the estimated monthly traffic volume by state, whereas
Figure 7shows the proportion of monthly traffic relative to annual totals in each state.
Sustainability 2025, 17, x FOR PEER REVIEW 6 of 20
Traffic volume distribution data were utilized to break down the annual VMT into
monthly VMT. Traffic data from approximately 5000 traffic counting locations nationwide
were monitored and used to estimate the total traffic volume every month by the Federal
Highway Administration [43]. For each state, the proportion of traffic in a single month to
the total annual traffic was calculated. This ratio served as the monthly distribution factor
for the VMT. Figure 6 displays the estimated monthly traffic volume by state, whereas
Figure 7 shows the proportion of monthly traffic relative to annual totals in each state.
Figure 6. Net traffic volumes by state over a year.
Figure 7. Distribution of annual traffic volume normalized per state.
2.3. Temperature Impacts on Vehicle Performances
This study used data from thermal dynamo tests conducted at the Advanced Mobil-
ity Testing Laboratory (AMTL) at Argonne National Laboratory [44–49]. The tests in-
volved six vehicles—two BEVs, two parallel PHEVs, one HEV, and one Conv—and were
carried out using a dynamometer equipped with a temperature controller. The tests were
conducted at various temperatures: 20 °F, 72 °F, and 95 °F. Additionally, data at 0 °F were
available for some vehicles. Table 1 shows the brief specs of the vehicles tested and 0 °F
test availability. Note that vehicles with similar levels of cabin volumes were chosen for a
reasonable comparison. The miles per gallon equivalent (MPGe) of BEVs, which assumes
1 gallon of gasoline is equivalent to 33.7 kWh of electricity, and the miles per gallon (MPG)
of other vehicles based on the EPA (United States Environmental Protection Agency)
standard are presented for reference as well [50,51]. To represent the consumption and
efficiency characteristics of BEV and PHEV, the data from each pair of BEVs and PHEVs
Figure 6. Net traffic volumes by state over a year.
Sustainability 2025, 17, x FOR PEER REVIEW 6 of 20
Traffic volume distribution data were utilized to break down the annual VMT into
monthly VMT. Traffic data from approximately 5000 traffic counting locations nationwide
were monitored and used to estimate the total traffic volume every month by the Federal
Highway Administration [43]. For each state, the proportion of traffic in a single month to
the total annual traffic was calculated. This ratio served as the monthly distribution factor
for the VMT. Figure 6 displays the estimated monthly traffic volume by state, whereas
Figure 7 shows the proportion of monthly traffic relative to annual totals in each state.
Figure 6. Net traffic volumes by state over a year.
Figure 7. Distribution of annual traffic volume normalized per state.
2.3. Temperature Impacts on Vehicle Performances
This study used data from thermal dynamo tests conducted at the Advanced Mobil-
ity Testing Laboratory (AMTL) at Argonne National Laboratory [44–49]. The tests in-
volved six vehicles—two BEVs, two parallel PHEVs, one HEV, and one Conv—and were
carried out using a dynamometer equipped with a temperature controller. The tests were
conducted at various temperatures: 20 °F, 72 °F, and 95 °F. Additionally, data at 0 °F were
available for some vehicles. Table 1 shows the brief specs of the vehicles tested and 0 °F
test availability. Note that vehicles with similar levels of cabin volumes were chosen for a
reasonable comparison. The miles per gallon equivalent (MPGe) of BEVs, which assumes
1 gallon of gasoline is equivalent to 33.7 kWh of electricity, and the miles per gallon (MPG)
of other vehicles based on the EPA (United States Environmental Protection Agency)
standard are presented for reference as well [50,51]. To represent the consumption and
efficiency characteristics of BEV and PHEV, the data from each pair of BEVs and PHEVs
Figure 7. Distribution of annual traffic volume normalized per state.
2.3. Temperature Impacts on Vehicle Performances
This study used data from thermal dynamo tests conducted at the Advanced Mobility
Testing Laboratory (AMTL) at Argonne National Laboratory [
44
–
49
]. The tests involved six
vehicles—two BEVs, two parallel PHEVs, one HEV, and one Conv—and were carried out
using a dynamometer equipped with a temperature controller. The tests were conducted at
various temperatures: 20
◦
F, 72
◦
F, and 95
◦
F. Additionally, data at 0
◦
F were available for
some vehicles. Table 1shows the brief specs of the vehicles tested and 0
◦
F test availability.
Note that vehicles with similar levels of cabin volumes were chosen for a reasonable
comparison. The miles per gallon equivalent (MPGe) of BEVs, which assumes 1 gallon of
gasoline is equivalent to 33.7 kWh of electricity, and the miles per gallon (MPG) of other
vehicles based on the EPA (United States Environmental Protection Agency) standard
are presented for reference as well [
50
,
51
]. To represent the consumption and efficiency
characteristics of BEV and PHEV, the data from each pair of BEVs and PHEVs were
averaged. Vehicle fuel economy and energy consumption values were interpolated or
extrapolated for temperatures between these test points.
Sustainability 2025,17, 3282 7 of 20
Table 1. Vehicle specs [52] and 0 ◦F test availabilities.
Name Vehicle Cabin Volume (ft3)EPA Fuel Economy (MPG(e)) 0 ◦F Test Data
BEV1 2020 Tesla Model 3 112 121 Available
BEV2 2020 Chevrolet Bolt 111 118 Available
PHEV1 2017 Toyota Prius Prime 111 133 Available
PHEV2 2016 Chevrolet Volt 109 106 Not available
HEV
2021 Hyundai Sonata Hybrid
121 47 Not available
Conv 2018 Toyota Camry 114 26 Not available
2.3.1. Variation in Energy Consumption, Range, and Fuel Economy
Thermal dynamo tests with UDDS and HWFET duty cycles were conducted across
different cell temperatures: 0
◦
F, 20
◦
F, 72
◦
F, and 95
◦
F. For tests at 0
◦
F and 20
◦
F, vehicles
were soaked in the cell temperature for enough time before initiating the driving tests,
and the target temperature of the HVAC system was set to 72
◦
F. In 95
◦
F tests, the target
temperature of the HVAC system was also set to 72
◦
F, and a light intensity of 850 W/m
2
was applied to simulate the effects of sunlight. As the ambient temperature increases or
decreases from 72
◦
F, the electricity consumption of the BEV and PHEV increases due to
the HVAC load. In contrast, the usable battery energy decreases, as shown in Figure 8a,b.
The change in vehicle range from these effects is described in Figure 8c. Below 25
◦
F, both
PHEVs operate the internal combustion engine even in the CD mode to prevent excessive
energy loss. Due to the uncertainty in the internal combustion engine operation strategies
of PHEVs, it is assumed that electricity and gasoline are consumed at constant rates during
CD mode operation below 10
◦
F, as seen in Figures 8a and 9a. Figure 9b shows the change
in PHEV, HEV, and Conv fuel economy depending on the ambient temperature. Note that
the electricity consumption and fuel economies were evaluated by an average weight of
55% on UDDs and 45% on HWFET, respectively.
Sustainability 2025, 17, x FOR PEER REVIEW 7 of 20
were averaged. Vehicle fuel economy and energy consumption values were interpolated
or extrapolated for temperatures between these test points.
Table 1. Vehicle specs [52] and 0 °F test availabilities.
Name Vehicle Cabin Volume (ft3) EPA Fuel Economy (MPG(e)) 0 °F Test Data
BEV1 2020 Tesla Model 3 112 121 Available
BEV2 2020 Chevrolet Bolt 111 118 Available
PHEV1 2017 Toyota Prius Prime 111 133 Available
PHEV2 2016 Chevrolet Volt 109 106 Not available
HEV 2021 Hyundai Sonata Hybrid 121 47 Not available
Conv 2018 Toyota Camry 114 26 Not available
2.3.1. Variation in Energy Consumption, Range, and Fuel Economy
Thermal dynamo tests with UDDS and HWFET duty cycles were conducted across
different cell temperatures: 0 °F, 20 °F, 72 °F, and 95 °F. For tests at 0 °F and 20 °F, vehicles
were soaked in the cell temperature for enough time before initiating the driving tests,
and the target temperature of the HVAC system was set to 72 °F. In 95 °F tests, the target
temperature of the HVAC system was also set to 72 °F, and a light intensity of 850 W/m2
was applied to simulate the effects of sunlight. As the ambient temperature increases or
decreases from 72 °F, the electricity consumption of the BEV and PHEV increases due to
the HVAC load. In contrast, the usable baery energy decreases, as shown in Figure 8a,b.
The change in vehicle range from these effects is described in Figure 8c. Below 25 °F, both
PHEVs operate the internal combustion engine even in the CD mode to prevent excessive
energy loss. Due to the uncertainty in the internal combustion engine operation strategies
of PHEVs, it is assumed that electricity and gasoline are consumed at constant rates dur-
ing CD mode operation below 10 °F, as seen in Figures 8a and 9a. Figure 9b shows the
change in PHEV, HEV, and Conv fuel economy depending on the ambient temperature.
Note that the electricity consumption and fuel economies were evaluated by an average
weight of 55% on UDDs and 45% on HWFET, respectively.
(a) (b)
(c)
Figure 8. Vehicle performance variation by temperature (BEV and PHEV in the CD mode):
(a) electricity consumption of BEV and PHEV in the CD mode; (b) usable battery energy of BEV and
PHEV; (c) range of BEV and PHEV in the CD mode.
Sustainability 2025,17, 3282 8 of 20
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Figure 8. Vehicle performance variation by temperature (BEV and PHEV in the CD mode): (a) elec-
tricity consumption of BEV and PHEV in the CD mode; (b) usable baery energy of BEV and PHEV;
(c) range of BEV and PHEV in the CD mode.
(a) (b)
Figure 9. Vehicle performance variation by temperature (PHEV, HEV, and Conv): (a) gasoline con-
sumption of PHEV in the CD mode; (b) fuel economy of PHEV in the CS mode, HEV, and Conv.
2.3.2. Energy Loss from Cold Start
During the initial stages of operation, vehicles exhibit reduced energy efficiency due
to the suboptimal temperature of their powertrain components and cabin. Powertrain
components function less efficiently at lower temperatures, and additional energy is re-
quired to adjust the cabin temperature to the target level, whether warming or cooling.
The impact of these conditions, known as a cold start, was analyzed by comparing the
energy consumption in the initial duty-cycle test, which includes the cold start effect, with
that in the warm start tests. Figure 10 shows the additional energy or fuel consumed by
BEV, PHEV, and Conv because of the cold start effect.
Figure 10. Cold start effect of BEV, PHEV, and Conv.
2.4. Electricity and Fuel Consumption
2.4.1. BEV Scenario
To determine the amount of external charging, the monthly mileage is divided into
miles driven using residential charging and external charging based on range. It is as-
sumed that a cold start occurs once daily, reducing the miles driven using residential
charging to account for this effect. Figure 11 shows the total cumulated distance of January
in Illinois from the household travel survey and the portion of distance covered by the
BEV range as an example. Days when the total traveled distance was below the range are
fully covered with green, while the days on which the distance was higher are partially
green, indicating the partial contribution of the residential charged electricity. The
Figure 9. Vehicle performance variation by temperature (PHEV, HEV, and Conv): (a) gasoline
consumption of PHEV in the CD mode; (b) fuel economy of PHEV in the CS mode, HEV, and Conv.
2.3.2. Energy Loss from Cold Start
During the initial stages of operation, vehicles exhibit reduced energy efficiency due
to the suboptimal temperature of their powertrain components and cabin. Powertrain
components function less efficiently at lower temperatures, and additional energy is re-
quired to adjust the cabin temperature to the target level, whether warming or cooling. The
impact of these conditions, known as a cold start, was analyzed by comparing the energy
consumption in the initial duty-cycle test, which includes the cold start effect, with that
in the warm start tests. Figure 10 shows the additional energy or fuel consumed by BEV,
PHEV, and Conv because of the cold start effect.
Sustainability 2025, 17, x FOR PEER REVIEW 8 of 20
Figure 8. Vehicle performance variation by temperature (BEV and PHEV in the CD mode): (a) elec-
tricity consumption of BEV and PHEV in the CD mode; (b) usable baery energy of BEV and PHEV;
(c) range of BEV and PHEV in the CD mode.
(a) (b)
Figure 9. Vehicle performance variation by temperature (PHEV, HEV, and Conv): (a) gasoline con-
sumption of PHEV in the CD mode; (b) fuel economy of PHEV in the CS mode, HEV, and Conv.
2.3.2. Energy Loss from Cold Start
During the initial stages of operation, vehicles exhibit reduced energy efficiency due
to the suboptimal temperature of their powertrain components and cabin. Powertrain
components function less efficiently at lower temperatures, and additional energy is re-
quired to adjust the cabin temperature to the target level, whether warming or cooling.
The impact of these conditions, known as a cold start, was analyzed by comparing the
energy consumption in the initial duty-cycle test, which includes the cold start effect, with
that in the warm start tests. Figure 10 shows the additional energy or fuel consumed by
BEV, PHEV, and Conv because of the cold start effect.
Figure 10. Cold start effect of BEV, PHEV, and Conv.
2.4. Electricity and Fuel Consumption
2.4.1. BEV Scenario
To determine the amount of external charging, the monthly mileage is divided into
miles driven using residential charging and external charging based on range. It is as-
sumed that a cold start occurs once daily, reducing the miles driven using residential
charging to account for this effect. Figure 11 shows the total cumulated distance of January
in Illinois from the household travel survey and the portion of distance covered by the
BEV range as an example. Days when the total traveled distance was below the range are
fully covered with green, while the days on which the distance was higher are partially
green, indicating the partial contribution of the residential charged electricity. The
Figure 10. Cold start effect of BEV, PHEV, and Conv.
2.4. Electricity and Fuel Consumption
2.4.1. BEV Scenario
To determine the amount of external charging, the monthly mileage is divided into
miles driven using residential charging and external charging based on range. It is assumed
that a cold start occurs once daily, reducing the miles driven using residential charging to
account for this effect. Figure 11 shows the total cumulated distance of January in Illinois
from the household travel survey and the portion of distance covered by the BEV range as
an example. Days when the total traveled distance was below the range are fully covered
with green, while the days on which the distance was higher are partially green, indicating
the partial contribution of the residential charged electricity. The proportion of miles
driven with residential electricity and electricity charged from external chargers, referred
to as
λresidential(M,S)
and
λexternal (M,S)
, are utilized to evaluate the amount of electricity
Sustainability 2025,17, 3282 9 of 20
charged from residential chargers and external chargers,
EBEV,residential
and
EBEV,exter nal
,
as follows:
EBEV,residential(M,S)=ecBE V (T(M,S)) ·mmonthly V MT (M,S)·λresidential (M,S)(1)
EBEV,exter nal (M,S)=ecBEV (T(M,S)) ·mmonthly V MT (M,S)·λexternal(M,S)(2)
where
ecB EV (T(M,S))
and
mmonthly V MT (M,S)
refer to the electricity consumption of the
BEV in a specific temperature Tand the monthly VMT of a specific month, M, and state,
S, respectively.
Sustainability 2025, 17, x FOR PEER REVIEW 9 of 20
proportion of miles driven with residential electricity and electricity charged from exter-
nal chargers, referred to as (, ) and (, ), are utilized to evaluate
the amount of electricity charged from residential chargers and external chargers,
, and ,, as follows:
,( , )= ( , ) ⋅ (, )⋅ (, )
(1)
,( , )= (, ) ⋅ (, )⋅ (, )
(2)
where (, ) and (, ) refer to the electricity consumption of the
BEV in a specific temperature T and the monthly VMT of a specific month, M, and state,
S, respectively.
Figure 11. Miles covered by BEV range.
2.4.2. PHEV Scenario
For PHEVs, we calculated the consumption of electricity and gasoline separately by
dividing the daily distances driven into two portions, as in Figure 12: those within the
PHEV range, which is the maximum distance that can be traveled using electric power
stored in the PHEV baery, and those beyond the PHEV range. The vehicle was assumed
to be driven in the charge-depleting mode (CD mode) within the PHEV range.
Figure 12. Miles covered by the PHEV range.
Figure 11. Miles covered by BEV range.
2.4.2. PHEV Scenario
For PHEVs, we calculated the consumption of electricity and gasoline separately by
dividing the daily distances driven into two portions, as in Figure 12: those within the
PHEV range, which is the maximum distance that can be traveled using electric power
stored in the PHEV battery, and those beyond the PHEV range. The vehicle was assumed
to be driven in the charge-depleting mode (CD mode) within the PHEV range.
Sustainability 2025, 17, x FOR PEER REVIEW 9 of 20
proportion of miles driven with residential electricity and electricity charged from exter-
nal chargers, referred to as (, ) and (, ), are utilized to evaluate
the amount of electricity charged from residential chargers and external chargers,
, and ,, as follows:
,( , )= ( , ) ⋅ (, )⋅ (, )
(1)
,( , )= (, ) ⋅ (, )⋅ (, )
(2)
where (, ) and (, ) refer to the electricity consumption of the
BEV in a specific temperature T and the monthly VMT of a specific month, M, and state,
S, respectively.
Figure 11. Miles covered by BEV range.
2.4.2. PHEV Scenario
For PHEVs, we calculated the consumption of electricity and gasoline separately by
dividing the daily distances driven into two portions, as in Figure 12: those within the
PHEV range, which is the maximum distance that can be traveled using electric power
stored in the PHEV baery, and those beyond the PHEV range. The vehicle was assumed
to be driven in the charge-depleting mode (CD mode) within the PHEV range.
Figure 12. Miles covered by the PHEV range.
Figure 12. Miles covered by the PHEV range.
However, gasoline is consumed in addition to electricity at low ambient temperatures,
as shown in Figure 9a, so that the two types of energy consumption are combined. Once the
Sustainability 2025,17, 3282 10 of 20
daily distance exceeds the PHEV range, only gasoline is consumed as the charge-sustaining
mode (CS mode) is engaged. With the proportion of miles driven in the CD mode and
CS mode,
λCD (M,S)
and
λCS (M,S)
, the electricity consumption
EPH EV
, and gasoline
consumption GPH EV are obtained as follows:
EPH EV (M,S)=ecP HEV (T(M,S)) ·mmonthly V MT (M,S)·λC D(M,S)(3)
GPH EV (M,S)=gpmP HEV ,CD (T(M,S)) ·mmonthly VMT (M,S)·λCD (M,S)+λCS (M,S)
mpgP HEV ,CS (T(M,S)) (4)
where
ecPH EV
,
gpmPHEV
, and
mpgPHEV,CS
refer to electricity consumption in the CD mode,
fuel consumption in the CD mode, and fuel economy in the CS mode of the PHEV, respectively.
2.4.3. BEV and Conv Scenario
In the Bev and Conv Scenario, it is assumed that BEVs are used on days when the total
miles driven are within their range, while conventional vehicles are employed on days
exceeding this range. An example of the portion of distances driven with the BEV and
Conv is shown in Figure 13. It can be seen that the miles driven by the BEV in the current
scenario are less than the miles driven by residential electricity in the BEV scenario. This
is because, in the BEV scenario, vehicles began operating with residential electricity even
when total miles exceeded the BEV range, whereas BEVs were not utilized at all in such
cases in the BEV and Conv scenario. The electricity consumed byBEV, EBEV&Conv, and the
gasoline consumed by the conventional vehicle,
GBEV&Conv
, in this scenario are calculated
as follows:
EBEV&Conv (M,S)=ecBEV (T(M,S)) ·mmo nthl y VMT (M,S)·λBEV,BEV&Conv (M,S)(5)
GBEV&Conv (M,S)=mmonthly VMT(M,S)·λConv,BEV&Co nv(M,S)
mpgConv(T(M,S)) (6)
where
λBEV,BEV&Conv
,
λConv,BEV&Conv
, and
mpgConv
refer to the proportion of distances
driven with BEV and Conv, and the fuel economy of the conventional vehicle, respectively.
Sustainability 2025, 17, x FOR PEER REVIEW 10 of 20
However, gasoline is consumed in addition to electricity at low ambient tempera-
tures, as shown in Figure 9a, so that the two types of energy consumption are combined.
Once the daily distance exceeds the PHEV range, only gasoline is consumed as the charge-
sustaining mode (CS mode) is engaged. With the proportion of miles driven in the CD
mode and CS mode, (, ) and (, ) , the electricity consumption , and
gasoline consumption are obtained as follows:
(, )= (, ) ⋅ (, )⋅ (, )
(3
)
(, )= ,(, ) ⋅ (, )⋅ (, )+(, )
,(, )
(4)
where , , and , refer to electricity consumption in the CD
mode, fuel consumption in the CD mode, and fuel economy in the CS mode of the PHEV,
respectively.
2.4.3. BEV and Conv Scenario
In the Bev and Conv Scenario, it is assumed that BEVs are used on days when the
total miles driven are within their range, while conventional vehicles are employed on
days exceeding this range. An example of the portion of distances driven with the BEV
and Conv is shown in Figure 13. It can be seen that the miles driven by the BEV in the
current scenario are less than the miles driven by residential electricity in the BEV sce-
nario. This is because, in the BEV scenario, vehicles began operating with residential elec-
tricity even when total miles exceeded the BEV range, whereas BEVs were not utilized at
all in such cases in the BEV and Conv scenario. The electricity consumed byBEV,
&, and the gasoline consumed by the conventional vehicle, &, in this sce-
nario are calculated as follows:
&(, )= (, ) ⋅ (, )⋅ ,&(, )
(5
)
&(, )= (, )⋅ ,& (, )
(, )
(6)
where ,& , ,& , and refer to the proportion of distances
driven with BEV and Conv, and the fuel economy of the conventional vehicle, respec-
tively.
Figure 13. Miles driven by BEV and Conv in the BEV and Conv scenario.
Figure 13. Miles driven by BEV and Conv in the BEV and Conv scenario.
Sustainability 2025,17, 3282 11 of 20
2.4.4. HEV Scenario
In the HEV scenario, all the daily distances were driven by a single type of vehicle.
Fuel consumption GHEV was obtained as follows:
GHEV (M,S)=mmonthl y VMT (M,S)
mpgH EV (T(M,S)) (7)
where mpgH EV refers to the fuel economy of HEV.
2.5. Operational Costs
State-specific residential and external DCFC electricity prices [
53
,
54
], denoted as
epresidential(S)
and
epexternal (S)
, along with gasoline prices [
55
],
gp(S)
, were employed
to finally obtain the monthly operational costs for each scenario. Figure 14 shows the
distribution of electricity and gasoline prices in January 2024 across the U.S. The monthly
operational costs were then calculated by converting the electricity and gasoline consumed
into their respective costs for each scenario.
CBEV (M,S)=epresidential (S)·EBEV ,residential (M,S)+epexternal (S)·EBEV,external (M,S)(8)
CPH EV (M,S)=epresidential(S)·EPH EV (M,S)+gp(S)·GPH EV (M,S)(9)
CBEV&Conv (M,S)=epresidenti al (S)·EBEV&Conv (M,S)+gp(S)·GBEV&Conv(M,S)(10)
CHEV (M,S)=g p(S)·GH EV (M,S)(11)
Sustainability 2025, 17, x FOR PEER REVIEW 11 of 20
2.4.4. HEV Scenario
In the HEV scenario, all the daily distances were driven by a single type of vehicle.
Fuel consumption was obtained as follows:
(, )= (, )
(, )
(7)
where refers to the fuel economy of HEV.
2.5. Operational Costs
State-specific residential and external DCFC electricity prices [53,54], denoted as
() and (), along with gasoline prices [55], (), were employed
to finally obtain the monthly operational costs for each scenario. Figure 14 shows the dis-
tribution of electricity and gasoline prices in January 2024 across the U.S. The monthly
operational costs were then calculated by converting the electricity and gasoline con-
sumed into their respective costs for each scenario.
(, )= ()⋅ ,(, )+ ()⋅ ,(, )
(8)
(, )= ()⋅ ( , )+ ()⋅ (, )
(9)
&
(, )= ()⋅
&
( , )+ ()⋅
&
( , )
(10)
(, )= ()⋅ (, )
(11)
Figure 14. Electricity and gasoline prices in Jan 2024 across the U.S.
3. Results
3.1. Operational Costs in BEV Scenario
Figure 15 shows the monthly costs for the BEV scenario, broken down by month and
state. The average monthly operational cost across the nation is USD 56.43, while elements
with the highest and the lowest operational cost are January, Indiana, at USD 134.38, and
May, Montana, at USD 21.83, respectively. Factors such as long-range trips, cold weather,
and high electricity prices contribute to increased costs in specific regions and seasons.
For instance, California consistently shows higher monthly costs than neighboring areas,
yielding USD 81.45 of average monthly cost, which is 44.33% higher than the national
average, primarily due to relatively high residential electricity and gasoline prices. Figure
16 shows the distance driven beyond the BEV range each month and state. Some elements
in Figure 15 corresponding to the bright areas in Figure 16 are brighter than neighboring
regions, showing the increased charging cost due to the higher rates at DCFC chargers.
The impact of long-range trips on operational costs is demonstrated in the Discussion Sec-
tion.
Figure 14. Electricity and gasoline prices in Jan 2024 across the U.S.
3. Results
3.1. Operational Costs in BEV Scenario
Figure 15 shows the monthly costs for the BEV scenario, broken down by month
and state. The average monthly operational cost across the nation is USD 56.43, while
elements with the highest and the lowest operational cost are January, Indiana, at USD
134.38, and May, Montana, at USD 21.83, respectively. Factors such as long-range trips, cold
weather, and high electricity prices contribute to increased costs in specific regions and
seasons. For instance, California consistently shows higher monthly costs than neighboring
areas, yielding USD 81.45 of average monthly cost, which is 44.33% higher than the national
average, primarily due to relatively high residential electricity and gasoline prices. Figure 16
shows the distance driven beyond the BEV range each month and state. Some elements
in Figure 15 corresponding to the bright areas in Figure 16 are brighter than neighboring
regions, showing the increased charging cost due to the higher rates at DCFC chargers. The
impact of long-range trips on operational costs is demonstrated in the Discussion Section.
Sustainability 2025,17, 3282 12 of 20
Sustainability 2025, 17, x FOR PEER REVIEW 12 of 20
Figure 15. Monthly costs in the BEV scenario.
Figure 16. Miles beyond BEV range.
3.2. Operational Costs in PHEV, BEV and Conv, and HEV Scenarios
Figures 17–19 show the differences in the monthly costs of PHEV, BEV and Conv,
and HEV scenarios compared to those of the BEV scenarios. Regions and months where
corresponding scenarios exhibit less operational costs are indicated as green, and where
they require more operational costs are shown as red. The operational costs of the PHEV
and BEV and Conv scenarios appear to be comparable to those of the BEV, with PHEVs
typically incurring lower expenses than the BEV and Conv scenario. The operational cost
difference between the BEV and PHEV ranged from USD −60.83 to USD 21.50, with neg-
ative values indicating that PHEV was more economical. For the BEV and Conv scenario,
the difference relative to BEV varied from USD −37.22 to USD 38.58. The HEV scenario
generally incurred higher operational costs than the BEV scenario, with cost gaps between
USD −48.80 and USD 40.74. However, in the New England division, encompassing Con-
necticut, Maine, Massachuses, New Hampshire, Rhode Island, and Vermont, the HEV
scenario exhibited lower costs despite the absence of long-range trips requiring external
charging. This can be aributed to the distribution of residential electricity prices. While
California experiences a similar level of high residential electricity costs with New Eng-
land, as shown in Figure 14, since gasoline prices are also high in California, the HEV
option was not competitive in California.
Figure 15. Monthly costs in the BEV scenario.
Sustainability 2025, 17, x FOR PEER REVIEW 12 of 20
Figure 15. Monthly costs in the BEV scenario.
Figure 16. Miles beyond BEV range.
3.2. Operational Costs in PHEV, BEV and Conv, and HEV Scenarios
Figures 17–19 show the differences in the monthly costs of PHEV, BEV and Conv,
and HEV scenarios compared to those of the BEV scenarios. Regions and months where
corresponding scenarios exhibit less operational costs are indicated as green, and where
they require more operational costs are shown as red. The operational costs of the PHEV
and BEV and Conv scenarios appear to be comparable to those of the BEV, with PHEVs
typically incurring lower expenses than the BEV and Conv scenario. The operational cost
difference between the BEV and PHEV ranged from USD −60.83 to USD 21.50, with neg-
ative values indicating that PHEV was more economical. For the BEV and Conv scenario,
the difference relative to BEV varied from USD −37.22 to USD 38.58. The HEV scenario
generally incurred higher operational costs than the BEV scenario, with cost gaps between
USD −48.80 and USD 40.74. However, in the New England division, encompassing Con-
necticut, Maine, Massachuses, New Hampshire, Rhode Island, and Vermont, the HEV
scenario exhibited lower costs despite the absence of long-range trips requiring external
charging. This can be aributed to the distribution of residential electricity prices. While
California experiences a similar level of high residential electricity costs with New Eng-
land, as shown in Figure 14, since gasoline prices are also high in California, the HEV
option was not competitive in California.
Figure 16. Miles beyond BEV range.
3.2. Operational Costs in PHEV, BEV and Conv, and HEV Scenarios
Figures 17–19 show the differences in the monthly costs of PHEV, BEV and Conv,
and HEV scenarios compared to those of the BEV scenarios. Regions and months where
corresponding scenarios exhibit less operational costs are indicated as green, and where
they require more operational costs are shown as red. The operational costs of the PHEV and
BEV and Conv scenarios appear to be comparable to those of the BEV, with PHEVs typically
incurring lower expenses than the BEV and Conv scenario. The operational cost difference
between the BEV and PHEV ranged from USD
−
60.83 to USD 21.50, with negative values
indicating that PHEV was more economical. For the BEV and Conv scenario, the difference
relative to BEV varied from USD
−
37.22 to USD 38.58. The HEV scenario generally incurred
higher operational costs than the BEV scenario, with cost gaps between USD
−
48.80 and
USD 40.74. However, in the New England division, encompassing Connecticut, Maine,
Massachusetts, New Hampshire, Rhode Island, and Vermont, the HEV scenario exhibited
lower costs despite the absence of long-range trips requiring external charging. This can be
attributed to the distribution of residential electricity prices. While California experiences a
similar level of high residential electricity costs with New England, as shown in Figure 14,
since gasoline prices are also high in California, the HEV option was not competitive
in California.
Sustainability 2025,17, 3282 13 of 20
Sustainability 2025, 17, x FOR PEER REVIEW 13 of 20
Figure 17. Monthly costs in the PHEV scenario.
Figure 18. Monthly costs in the BEV and Conv scenario.
Figure 19. Monthly costs in the HEV scenario.
4. Discussion
Detailed regional and seasonal plots in the previous section provided an overview of
the impact of long-range trips on operational costs. On the other hand, the effects of cli-
mate and energy prices are not clearly distinguished because they are mixed with the in-
fluence of long-range trips. This section first compares the results obtained with findings
Figure 17. Monthly costs in the PHEV scenario.
Sustainability 2025, 17, x FOR PEER REVIEW 13 of 20
Figure 17. Monthly costs in the PHEV scenario.
Figure 18. Monthly costs in the BEV and Conv scenario.
Figure 19. Monthly costs in the HEV scenario.
4. Discussion
Detailed regional and seasonal plots in the previous section provided an overview of
the impact of long-range trips on operational costs. On the other hand, the effects of cli-
mate and energy prices are not clearly distinguished because they are mixed with the in-
fluence of long-range trips. This section first compares the results obtained with findings
Figure 18. Monthly costs in the BEV and Conv scenario.
Sustainability 2025, 17, x FOR PEER REVIEW 13 of 20
Figure 17. Monthly costs in the PHEV scenario.
Figure 18. Monthly costs in the BEV and Conv scenario.
Figure 19. Monthly costs in the HEV scenario.
4. Discussion
Detailed regional and seasonal plots in the previous section provided an overview of
the impact of long-range trips on operational costs. On the other hand, the effects of cli-
mate and energy prices are not clearly distinguished because they are mixed with the in-
fluence of long-range trips. This section first compares the results obtained with findings
Figure 19. Monthly costs in the HEV scenario.
4. Discussion
Detailed regional and seasonal plots in the previous section provided an overview
of the impact of long-range trips on operational costs. On the other hand, the effects of
climate and energy prices are not clearly distinguished because they are mixed with the
influence of long-range trips. This section first compares the results obtained with findings
Sustainability 2025,17, 3282 14 of 20
from a previous study. Then, the most economical choices were determined and displayed
with their temperature and energy price parameters to analyze those effects.
4.1. Comparison of Operational Costs Result with a Previous Study
The TCOs of Kia Niro 2024 were analyzed in [
41
] regarding Windsor, Ontario, Canada,
with three different powertrain options. While [
41
] focuses on evaluating TCOs with fixed
fuel economy and energy prices, it ensures a fair comparison of BEVs, PHEVs, and HEVs
by referring to the vehicles on the same platform. Since both [
41
] and the present study
conducted a comparative analysis of these three powertrains, we compared the operational
cost results from our study with those of [
41
]. Considering that the medium electricity price
(8.9 cents/kWh) is chosen among low (6.4 cents/kWh), medium (8.9 cents/kWh), and high
(13 cents/kWh) in [
41
], to ensure a fair comparison, residential and DCFC charging prices
were adjusted to the low and high prices, respectively. Gasoline prices were also aligned
with the values used in [41]. The differences in assumptions are summarized in Table 2.
Table 2. Assumptions adopted in the present study and [41].
Assumptions Present Study [41]
Annual VMT 6007–17,378 miles 12,400 miles
Fuel economy Depends on the temperature OEM information
Electricity prices USD 0.10/kWh–USD 0.31/kWh (residential) *
USD 0.17/kWh–USD 0.54/kWh (DCFC) * USD 0.09/kWh
Gasoline price USD 2.69/gal–USD 4.54/gal ** USD 4.28/gal
Vehicle
BEV 2020 Tesla Model 3 and 2020 Chevrolet Bolt 2024 Kia Niro EV Wind
PHEV 2017 Toyota Prius Prime and 2016 Chevrolet Volt 2024 Kia Niro PHEV EX
HEV 2021 Hyundai Sonata 2024 Kia Niro HEV LX
* For comparison, residential and DCFC electricity prices were adjusted to USD 0.064/kWh and USD 0.13/kWh,
respectively. ** Gasoline price was adjusted to USD 4.28/gal for comparison.
Figure 20a compares operational cost results under the same assumption of electricity
and gasoline prices as the previous study. Although discrepancies in operational costs exist
due to differences in vehicle performance, overall values remain consistent. In the present
study, regional climate and driving patterns are considered, resulting in a distribution of
values rather than a single estimate. In Figure 20b, as the variations in regional electricity
and gasoline prices are reflected, the operational cost of BEVs increases significantly, and
the regional disparities lead to an expansion in the size of the interquartile box.
Sustainability 2025, 17, x FOR PEER REVIEW 14 of 20
from a previous study. Then, the most economical choices were determined and displayed
with their temperature and energy price parameters to analyze those effects.
4.1. Comparison of Operational Costs Result with a Previous Study
The TCOs of Kia Niro 2024 were analyzed in [41] regarding Windsor, Ontario, Can-
ada, with three different powertrain options. While [41] focuses on evaluating TCOs with
fixed fuel economy and energy prices, it ensures a fair comparison of BEVs, PHEVs, and
HEVs by referring to the vehicles on the same platform. Since both [41] and the present
study conducted a comparative analysis of these three powertrains, we compared the op-
erational cost results from our study with those of [41]. Considering that the medium elec-
tricity price (8.9 cents/kWh) is chosen among low (6.4 cents/kWh), medium (8.9
cents/kWh), and high (13 cents/kWh) in [41], to ensure a fair comparison, residential and
DCFC charging prices were adjusted to the low and high prices, respectively. Gasoline
prices were also aligned with the values used in [41]. The differences in assumptions are
summarized in Table 2.
Table 2. Assumptions adopted in the present study and [41].
Assumptions Present Study [41]
Annual VMT 6007–17,378 miles 12,400 miles
Fuel economy Depends on the temperature OEM information
Electricity prices USD 0.10/kWh–USD 0.31/kWh (residential) *
USD 0.17/kWh–USD 0.54/kWh (DCFC) * USD 0.09/kWh
Gasoline price USD 2.69/gal–USD 4.54/gal ** USD 4.28/gal
Vehicle
BEV 2020 Tesla Model 3 and 2020 Chevrolet Bolt 2024 Kia Niro EV Wind
PHEV 2017 Toyota Prius Prime and 2016 Chevrolet Volt 2024 Kia Niro PHEV EX
HEV 2021 Hyundai Sonata 2024 Kia Niro HEV LX
* For comparison, residential and DCFC electricity prices were adjusted to USD 0.064/kWh and USD
0.13/kWh, respectively. ** Gasoline price was adjusted to USD 4.28/gal for comparison.
Figure 20a compares operational cost results under the same assumption of electric-
ity and gasoline prices as the previous study. Although discrepancies in operational costs
exist due to differences in vehicle performance, overall values remain consistent. In the
present study, regional climate and driving paerns are considered, resulting in a distri-
bution of values rather than a single estimate. In Figure 20b, as the variations in regional
electricity and gasoline prices are reflected, the operational cost of BEVs increases signifi-
cantly, and the regional disparities lead to an expansion in the size of the interquartile box.
(a) (b)
Figure 20. Comparison of operational costs between the present study and the previous study [41]:
(a) result obtained with electricity and gasoline prices adjusted to the assumption from the previous
study; (b) result obtained with varying electricity and gasoline prices by regions.
Figure 20. Comparison of operational costs between the present study and the previous study [
41
]:
(a) result obtained with electricity and gasoline prices adjusted to the assumption from the previous
study; (b) result obtained with varying electricity and gasoline prices by regions.
Sustainability 2025,17, 3282 15 of 20
4.2. Impact of Seasonal and Regional Factors and Driving Patterns on Cost Competitiveness
In Figure 21, elements where BEV, PHEV, HEV, and BEV and Conv were the most
economical choice were indicated as green, blue, yellow, and red, respectively. The elements
with long-range trips were again distinguished by PHEVs being the most economical choice.
However, even if we set aside the impact of long-distance driving, we can see that most
economic choices change depending on the season. For example, in the western U.S. in July
and December, the BEV was determined to be the most economical choice in July and the
PHEV in December, even though the amount of long-distance driving was similar. Another
characteristic point would be the New England division, where HEV has been the most
economical choice almost every month.
Sustainability 2025, 17, x FOR PEER REVIEW 15 of 20
4.2. Impact of Seasonal and Regional Factors and Driving Paerns on Cost Competitiveness
In Figure 21, elements where BEV, PHEV, HEV, and BEV and Conv were the most
economical choice were indicated as green, blue, yellow, and red, respectively. The ele-
ments with long-range trips were again distinguished by PHEVs being the most econom-
ical choice. However, even if we set aside the impact of long-distance driving, we can see
that most economic choices change depending on the season. For example, in the western
U.S. in July and December, the BEV was determined to be the most economical choice in
July and the PHEV in December, even though the amount of long-distance driving was
similar. Another characteristic point would be the New England division, where HEV has
been the most economical choice almost every month.
Each pair of state and month in Figure 21 is represented as a point in Figure 22 with
the same color legend. In Figure 22, the x-axes are the temperature of the corresponding
region and month, and the y-axes are the ratio of gasoline price to the residential electricity
price. Three subplots show the data filtered as having monthly miles below the BEV range,
below the BEV range plus 50 miles, and the BEV range plus 100 miles. The last subplot
includes all the elements. As seen in Figure 21, the HEV is the most cost-efficient option
in the New England region. Specifically, the HEV was the most cost-efficient option in
most regions where gasoline’s dollar-per-gallon price is smaller than 13 times the dollar-
per-kilowa-hour price of residential electricity. In other regions, the BEV or PHEV was
generally the most cost-efficient option. By comparing the four subplots in Figure 22, it
was found that the farther the vehicle is driven beyond the BEV range, the more cost-
efficient option PHEV was likely to be. The percentage of elements where the PHEV was
the most economical was 8.9% for driving within the BEV range and 15.3% for miles be-
yond BEV less than 50 miles. This percentage increased to 22.6% for miles beyond the BEV
range of less than 100 miles and 35.2% among all elements, respectively.
Figure 21. Choice of vehicles for the least operational costs (U.S. map).
Figure 21. Choice of vehicles for the least operational costs (U.S. map).
Each pair of state and month in Figure 21 is represented as a point in Figure 22 with
the same color legend. In Figure 22, the x-axes are the temperature of the corresponding
region and month, and the y-axes are the ratio of gasoline price to the residential electricity
price. Three subplots show the data filtered as having monthly miles below the BEV range,
below the BEV range plus 50 miles, and the BEV range plus 100 miles. The last subplot
includes all the elements. As seen in Figure 21, the HEV is the most cost-efficient option
in the New England region. Specifically, the HEV was the most cost-efficient option in
most regions where gasoline’s dollar-per-gallon price is smaller than 13 times the dollar-
per-kilowatt-hour price of residential electricity. In other regions, the BEV or PHEV was
generally the most cost-efficient option. By comparing the four subplots in Figure 22, it was
found that the farther the vehicle is driven beyond the BEV range, the more cost-efficient
option PHEV was likely to be. The percentage of elements where the PHEV was the most
economical was 8.9% for driving within the BEV range and 15.3% for miles beyond BEV
less than 50 miles. This percentage increased to 22.6% for miles beyond the BEV range of
less than 100 miles and 35.2% among all elements, respectively.
Furthermore, while the elements where PHEVs were the most economical were clus-
tered in small gasoline–residential electricity price ratios and low-temperature areas for
miles beyond the BEV range of less than 100 miles, they were distributed broader beyond
that range. Meanwhile, the elements where BEVs were the most economical appeared in
high-temperature or high gasoline–residential electricity price areas. This visualization
allows for the quantitative confirmation of the qualitatively known vulnerabilities of BEVs
in low temperatures and long-distance driving. For example, over 95% of the elements
included in segment B, indicated in Figure 23, are where the PHEV is the most economical
choice regardless of the daily miles. Likewise, for over 96% of the elements in segment
A, HEV exhibited the lowest operational cost. Segment C is initially dominated by BEVs
Sustainability 2025,17, 3282 16 of 20
when miles beyond range are small, but as shown in Figure 24, the ratio of PHEV-dominant
elements increases up to 17% as the miles beyond the BEV range increase.
Sustainability 2025, 17, x FOR PEER REVIEW 16 of 20
Figure 22. Choice of vehicles for the least operational costs (scaer plot).
Furthermore, while the elements where PHEVs were the most economical were clus-
tered in small gasoline–residential electricity price ratios and low-temperature areas for
miles beyond the BEV range of less than 100 miles, they were distributed broader beyond
that range. Meanwhile, the elements where BEVs were the most economical appeared in
high-temperature or high gasoline–residential electricity price areas. This visualization
allows for the quantitative confirmation of the qualitatively known vulnerabilities of BEVs
in low temperatures and long-distance driving. For example, over 95% of the elements
included in segment B, indicated in Figure 23, are where the PHEV is the most economical
choice regardless of the daily miles. Likewise, for over 96% of the elements in segment A,
HEV exhibited the lowest operational cost. Segment C is initially dominated by BEVs
when miles beyond range are small, but as shown in Figure 24, the ratio of PHEV-domi-
nant elements increases up to 17% as the miles beyond the BEV range increase.
Figure 23. Segmentation of the regional and seasonal elements.
Figure 22. Choice of vehicles for the least operational costs (scatter plot).
Sustainability 2025, 17, x FOR PEER REVIEW 16 of 20
Figure 22. Choice of vehicles for the least operational costs (scaer plot).
Furthermore, while the elements where PHEVs were the most economical were clus-
tered in small gasoline–residential electricity price ratios and low-temperature areas for
miles beyond the BEV range of less than 100 miles, they were distributed broader beyond
that range. Meanwhile, the elements where BEVs were the most economical appeared in
high-temperature or high gasoline–residential electricity price areas. This visualization
allows for the quantitative confirmation of the qualitatively known vulnerabilities of BEVs
in low temperatures and long-distance driving. For example, over 95% of the elements
included in segment B, indicated in Figure 23, are where the PHEV is the most economical
choice regardless of the daily miles. Likewise, for over 96% of the elements in segment A,
HEV exhibited the lowest operational cost. Segment C is initially dominated by BEVs
when miles beyond range are small, but as shown in Figure 24, the ratio of PHEV-domi-
nant elements increases up to 17% as the miles beyond the BEV range increase.
Figure 23. Segmentation of the regional and seasonal elements.
Figure 23. Segmentation of the regional and seasonal elements.
Sustainability 2025, 17, x FOR PEER REVIEW 17 of 20
Figure 24. Change in the ratio of dominant powertrains in segment C.
5. Conclusions
This study comprehensively analyzes operational costs for different vehicle configu-
rations, considering regional climates, driving paerns, energy prices, and vehicle effi-
ciency in various temperatures. The analysis used an operational cost map of the U.S. for
each vehicle to show the variations in operational costs. The specific impact of each factor
was discovered by comparing the most economical options by temperature and energy
price. By categorizing the 576 elements (48 states and 12 months) into four groups, where
BEV, BEV and Conv, PHEV, and HEV were the most economical, and visualizing their
distribution, the influence of regional and seasonal factors is demonstrated. Due to the
characteristics of each powertrain, the distributed elements could be segmented into three
sections, as shown in Figure 23. The HEV and PHEV consistently exhibited the lowest
operational cost in segments A and B. In contrast, the most economical option in segment
C varied depending on the distance driven beyond the BEV range.
Two possible directions can be considered as a further development of this research.
On the one hand, the assumptions on electric chargers can be made more concrete and
applied to the analysis. For example, external chargers can be categorized into workplace
chargers, private company chargers, etc. In addition, it is possible to consider the different
characteristics of charging infrastructure in different regions and convert the time spent
on detours to a monetized cost for operation. On the other hand, there could be future
studies where the proposed methodology can be applied. By designing a household-level
scenario with multiple vehicles, this methodology can be used to analyze the cost of vehi-
cle operation at the household level. Another application of the proposed methodology
could be to analyze the operating costs of certain vehicle models on a regional basis, which
could provide consumers and stakeholders with more detailed insights. These directions
for future research could further support the development of vehicle electrification poli-
cies and alternative energy policies, contributing to a more comprehensive understanding
of cost-effective and sustainable transportation solutions.
The analysis methodology employed in this study provides a realistic approach to
assessing operational costs for different vehicle types. This methodology and findings are
expected to contribute to formulating vehicle electrification policies and alternative en-
ergy and alternative fuel policies for future vehicles.
Author Contributions: Conceptualization, R.V. and S.-H.H.; methodology, R.V. and K.-H.K.; soft-
ware, K.-H.K.; validation, R.V., N.K. and K.-H.K.; formal analysis, R.V., K.S. and K.-H.K.; investiga-
tion, R.V., K.S., and K.-H.K.; resources, N.K., K.S. and S.-H.H.; data curation, R.V., K.S. and K.-H.K.;
writing—original draft preparation, K.-H.K.; writing—review and editing, R.V., N.K. and S.-H.H.;
visualization, K.-H.K.; supervision, R.V. and S.-H.H.; project administration, R.V. and N.K.; funding
acquisition, R.V. and N.K. All authors have read and agreed to the published version of the manu-
script.
Figure 24. Change in the ratio of dominant powertrains in segment C.
Sustainability 2025,17, 3282 17 of 20
5. Conclusions
This study comprehensively analyzes operational costs for different vehicle configura-
tions, considering regional climates, driving patterns, energy prices, and vehicle efficiency
in various temperatures. The analysis used an operational cost map of the U.S. for each
vehicle to show the variations in operational costs. The specific impact of each factor was
discovered by comparing the most economical options by temperature and energy price. By
categorizing the 576 elements (48 states and 12 months) into four groups, where BEV, BEV
and Conv, PHEV, and HEV were the most economical, and visualizing their distribution,
the influence of regional and seasonal factors is demonstrated. Due to the characteristics
of each powertrain, the distributed elements could be segmented into three sections, as
shown in Figure 23. The HEV and PHEV consistently exhibited the lowest operational
cost in segments A and B. In contrast, the most economical option in segment C varied
depending on the distance driven beyond the BEV range.
Two possible directions can be considered as a further development of this research.
On the one hand, the assumptions on electric chargers can be made more concrete and
applied to the analysis. For example, external chargers can be categorized into workplace
chargers, private company chargers, etc. In addition, it is possible to consider the different
characteristics of charging infrastructure in different regions and convert the time spent
on detours to a monetized cost for operation. On the other hand, there could be future
studies where the proposed methodology can be applied. By designing a household-level
scenario with multiple vehicles, this methodology can be used to analyze the cost of vehicle
operation at the household level. Another application of the proposed methodology could
be to analyze the operating costs of certain vehicle models on a regional basis, which could
provide consumers and stakeholders with more detailed insights. These directions for
future research could further support the development of vehicle electrification policies
and alternative energy policies, contributing to a more comprehensive understanding of
cost-effective and sustainable transportation solutions.
The analysis methodology employed in this study provides a realistic approach to
assessing operational costs for different vehicle types. This methodology and findings are
expected to contribute to formulating vehicle electrification policies and alternative energy
and alternative fuel policies for future vehicles.
Author Contributions: Conceptualization, R.V. and S.-H.H.; methodology, R.V. and K.-H.K.; software,
K.-H.K.; validation, R.V., N.K. and K.-H.K.; formal analysis, R.V., K.S. and K.-H.K.; investigation, R.V.,
K.S. and K.-H.K.; resources, N.K., K.S. and S.-H.H.; data curation, R.V., K.S. and K.-H.K.; writing—
original draft preparation, K.-H.K.; writing—review and editing, R.V., N.K. and S.-H.H.; visualization,
K.-H.K.; supervision, R.V. and S.-H.H.; project administration, R.V. and N.K.; funding acquisition,
R.V. and N.K. All authors have read and agreed to the published version of the manuscript.
Funding: The authors would like to acknowledge the financial support of the Office of Nuclear
Energy (NE) within the U.S. Department of Energy (DOE) to conduct this work. The submitted
manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory
(Argonne). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under
Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its
behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare
derivative works, distribute copies to the public, and perform publicly and display publicly, by or on
behalf of the Government.
Data Availability Statement: The raw data supporting the conclusions of this article will be made
available by the authors upon request.
Acknowledgments: The authors would like to acknowledge Argonne National Laboratory for
supporting this work. Related ongoing efforts at Argonne include the development of thermal
Sustainability 2025,17, 3282 18 of 20
management models in Autonomie to improve energy consumption and range estimation under
varying ambient temperatures, as well as a broader study using TechScape to evaluate the national-
level impact of regional, climatic, and operational patterns on vehicle energy use across different
classes, including not only light-duty vehicles, which are the focus of the present study, but also
mediumduty and heavy-duty vehicles.
Conflicts of Interest: The authors declare no conflicts of interest.
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