WHITE PAPERSEPTEMBER 2020

REAL-WORLD USAGE OF PLUG-IN
HYBRID ELECTRIC VEHICLESFUEL CONSUMPTION, ELECTRICDRIVING, AND CO2 EMISSIONSPatrick Plötz, Cornelius Moll, Georg Bieker, Peter Mock, Yaoming Li

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WA S H I N G T O NACKNOWLEDGMENTS
This report greatly benefited from the input of several other individuals andorganizations. We thank Gil Tal and Scott Hardman from UC Davis for sharing data withus. Christian Weber and Erik Figenbaum from TØI also shared data and new results
and contributed to the present study. Norbert Ligterink from TNO provided helpfulcomments. We acknowledge valuable input and discussions with all members of the
UC Davis International Electric Vehicle Policy Council, including Frances Sprei and
Ahmet Mandev from Chalmers University of Technology in Gothenburg, Sweden, as
well as several members of the German National Platform Future of Mobility (NPM). We
also thank Jannis Gasmi and Ahmed El-Deeb from Fraunhofer ISI and Uwe Tietge, Zifei
Yang, Hui He, and Yoann Bernard from the ICCT for their support with the different
data sources and their review. Additionally, the authors thank all individuals andorganizations for time and effort to discuss the data and findings before publication of
the final report. We heartily acknowledge additional data provided by a large German
company as well as members of the automotive industry for sharing information on
their own findings.
For additional information:
ICCT – International Council on Clean Transportation Europe
Neue Promenade 6, 10178 Berlin+49 (30) 847129-102
communications@theicct.org | www.theicct.org | @TheICCT© 2020 International Council on Clean Transportation

Funding for this work was generously provided by the European Climate Foundation.

EXECUTIVE SUMMARY
Plug-in hybrid electric vehicles (PHEVs) combine an electric and a conventional
combustion engine drive train. They offer potential to reduce global greenhousegas (GHG) emissions and local air pollution if they drive mainly on electricity. PHEVs
account for about one third of the global electric vehicle fleet and their fleet is
expected to grow further (IEA 2020). However, there is limited evidence on how much
driving PHEVs actually do on electricity and how much conventional fuel they use
in real-world operation. The present report provides an analysis of real-world usage
and fuel consumption of approximately 100,000 PHEVs in China, Europe, and North
America. The analysis arrives at the following main findings:
PHEV fuel consumption and tail-pipe CO2 emissions in real-world driving, on
average, are approximately two to four times higher than type-approval values.
The deviation from New European Drive Cycle (NEDC) type-approval values spansmuch larger ranges than for conventional vehicles. Real-world values are two to fourtimes higher for private cars and three to four times higher for company cars (FigureES1). Making use of a limited dataset of PHEVs that are type-approved to the newly
introduced Worldwide Harmonized Light-Duty Vehicles Test Procedure (WLTP), thedeviation found is about the same as for PHEVs type-approved to the NEDC.
The real-world share of electric driving for PHEVs, on average, is about half theshare considered in the type-approval values. For private cars, the average utility
factor (UF)—an expression for the portion of kilometers driven on electric motor
versus kilometers driven on combustion engine—is 69% for NEDC type approval but
only around 37% for real-world driving. For company cars, an average UF of 63% for
NEDC and approximately 20% for real-world driving was found. Similar deviations areto be expected also for WLTP. There are noteworthy differences between the markets
analyzed, with the highest real-world UF found for Norway at 53% for private vehicles
and the United States at 54% for private vehicles. The lowest UFs were for China at 26%
for private vehicles, Germany with 18% for company cars and 43% for private vehicles,
and the Netherlands with 24% for company cars (Figure ES2).
China (n = 6,870)

0%

200%

400%

600%

Norway (n = 1,514)

Germany (n = 1,457)

0%

200%

400%

600%

Netherlands (n = 10,800)

0%

200%

400%

600%

US & Canada (n = 84,068)
User groupcompany car
private

0%

200%

400%

600%

0%

200%

400%

600%

Relation to NEDC fuel consumption
Figure ES1. Distribution of real-world fuel consumption in relation to NEDC test cycle. Shownis the distribution by country. The vertical dashed line at 100% corresponds to real = test
cycle. Private users in blue and company car users in red. Small rugs next to the x-axis indicate
individual observations at PHEV model variant level. Total number of vehicles in the sample isincluded by country.

i

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESPHEVs are not charged every day. Private users in Germany charge their PHEVs an
average of three out of four driving days. For company cars, charging takes place only
about every second driving day. The low charging frequency clearly reduces the shareof kilometers driven on electricity. The very low UF for PHEVs in China indicates low
charging frequency there, too, whereas PHEVs in Norway and the United States appear
to be charged more often than in Germany or China.
PHEVs show high annual mileage and many long-distance trips. In Germany, theaverage annual mileage of PHEVs is higher than the car stock average. While forcompany car PHEVs, the mileage of 30,000 km is similar to that of average company
cars, the mean annual mileage of private PHEVs of 21,000 km is significantly higher
than the approximately 14,000 km private-car average. This higher total mileage
indicates more-frequent long-distance car travel. As the all-electric range of mostcurrent PHEVs is limited to an average of around 50 km (according to NEDC), thisreduces the share of kilometers driven on electricity. In the United States, the averageannual mileage is similar to the national average.
PHEVs electrify many kilometers per year. Most PHEVs have type-approval all-electricranges of 30–60 km (NEDC) and electrify 5,000–10,000 km a year, increasing withrange. PHEVs with high all-electric ranges of 80 km or more achieve 12,000–20,000
km mean annual electric mileages, which are values comparable to the mean total
annual mileage of the car fleet in Germany and the United States. The high meanannual number of electric kilometers reflects high annual mileages of PHEVs despite
low UFs. If the fuel consumption of PHEVs at empty battery is assumed to be similar to
the fuel consumption of conventional cars, the share of kilometers that PHEVs electrify
on average results in a total of 15%–55% less tailpipe CO2 emissions compared toconventional cars. Such savings depend on the PHEV model, user group and country.
Overall, they are much lower than expected from type-approval values.
Decreasing combustion engine power while increasing all-electric range andfrequency of charging improve real-world fuel consumption and CO2 emissions of
PHEVs. Real-world fuel consumption and CO2 emission levels decrease by 2%–4% witheach 10 kW of system power taken out of a PHEV. At the same time, adding 10 km of
all-electric range improves real-world values by 8%–14%.

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ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESChina (n = 6,870)

100%

Germany (n = 1,457)

100%

Utility factor (UF)

NEDC

NEDC

NEDC

80%

80%

80%

60%

60%

60%

40%

40%

40%

20%

20%

20%

0%

0

25

50

75 100 125 150 175

Norway (n = 1,514)

100%

0%

0

25

50

NEDC

60%

60%

40%

40%

20%

20%

25

50

75 100 125 150 175

0%

25

0%

CountryCN

50

75 100 125 150 175

NL

USUser groupcompany car
0

25

50

75 100 125 150 175

NEDC range [km]Figure ES2. Utility factor of PHEVs with different all-electric range by country. The average
specific utility factors (UFs) of individual PHEV model-variants are shown as observed in our
sample for private vehicles (small triangles) and company cars (small circles) with the NEDC UF(dashed line). Total number of vehicles in the sample is included by country. The grey dots are thefull sample and each small plot emphasizes the data from one country by country specific color.

iii

0

NEDC

80%

0

75 100 125 150 175

US & Canada (n = 84,068)

100%

80%

0%

Netherlands (n = 10,800)

100%

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES

privateDENORECOMMENDATIONSPHEVs can electrify many kilometers if they provide sufficiently long all-electric ranges
and are driven mainly on electricity. However, current PHEV policies do not fully support
these aspects. Based on our findings, we provide the following recommendations:
Vehicle manufacturers should increase the all-electric range of their PHEVs from anaverage of about 50 km today to a level of about 90 km in future years. This would
be sufficient to cover the full daily distance driven electrically on about 85% of drivingdays or approximately 70% of total distances driven by German private car owners if
charged every day. Some PHEV models on the market today provide an all-electricrange on this order and already show mean UFs greater than 50%. Furthermore,manufacturers should limit the power of PHEV combustion engines. This could beachieved by deciding on a maximum ratio for electric motor power to combustion
engine power. It is important that any limitation on combustion engine power apply not
only for urban driving but also for extra-urban driving, which accounts for the majorityof annual mileage of a typical PHEV. Generally, manufacturers should make sure to
inform customers about the pros and cons of PHEVs and to encourage them to select a
PHEV only if the vehicle fits a customer’s driving and charging behavior.
Fleet managers should carefully assess which of their company car users’ driving andusage behavior is appropriate for PHEVs. They should incentivize frequent charging of
PHEVs, for example by allowing unlimited re-charging of electricity while limiting thebudget for gasoline or diesel on a fuel card provided by the company.
Regulators should revisit incentives for PHEVs to take into account real-world usage.

» At the European Union level, super credits are granted for vehicles with an emission
level of 50 grams of CO2 per kilometer (gCO2/km) and lower in WLTP terms. Basedon our findings, this translates into a real-world level of 100–200 gCO2/km tailpipe emissions of PHEVs, which is above the 2020-21 CO2 target and significantly
higher than the 2025 and 2030 benchmarks. The CO2 emission threshold for super
credits should be lowered, or the qualification of a specific PHEV model should bedemonstrated by using real-world usage data, for example collected from on-board
fuel consumption meters. Similarly, the threshold for providing Zero- and Low Emission
Vehicle (ZLEV) credits should be adapted to real-world data and the current multiplierof 0.7 should be removed to avoid any incentive for PHEVs with a low electric range. In
parallel, the testing procedures for PHEVs, and in particular the UF assumptions of the
WLTP, should be updated to better reflect real driving and usage patterns.

» At the national level, fiscal and other incentives should prefer PHEVs with a high allelectric range and a high ratio of electric motor power to combustion engine power.
Whenever possible, incentives should be tied to demonstrating proper real-world
performance of the vehicles, for example by using UF data collected from on-board
fuel consumption meters or during regular technical inspections. This applies toincentives at the time of purchase, such as for private vehicle buyers, as well as tax
incentives, such as for company cars. Furthermore, the legal and financial barriers
for the installation of home charging points should be reduced. In parallel, a portionof PHEV purchase incentives should be bound to the installation of a home orworkplace charging point or alternatively handed out as public charging vouchers.
At the same time, company-car PHEV incentives could be issued only to companies
that provide a sufficient workplace charging infrastructure or support employees
in home or public charging. The overall public charging infrastructure needs to be
expanded; there should be nondiscriminatory access to public charging stations;and the introduction of a universal charging card or simple and universal payment
methods such as credit cards should be further pursued. However, as publiccharging is most likely less than 20% of charging events for PHEVs, the impact on
the mean UF of such policies is probably limited. The attractiveness of driving onconventional fuel should be reduced by lowering charging costs, raising fuel prices,or limiting tax deductibility of costs for conventional fuels for organizations.

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ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESTABLE OF CONTENTS
Executive summary.................................................................................................................... i
Recommendations.................................................................................................................... iv
1. Introduction............................................................................................................................1
2. Data and methodology........................................................................................................ 2
2.1. Overview............................................................................................................................................. 2
2.2. Individual data sources................................................................................................................. 3
2.3. Methodology for UF calculation...............................................................................................6
3. Results: Real-world PHEV usage and fuel consumption............................................... 8
3.1. Average real-world PHEV usage..............................................................................................8
3.2. Impact of vehicle properties: All-electric range and system power....................... 15
3.3. Analysis of individual user behavior..................................................................................... 20
4. Discussion ...........................................................................................................................34
5. Policy recommendations..................................................................................................36
References................................................................................................................................38
Appendix A: Norwegian case study by C. Weber and E. Figenbaum............................ 41
Appendix B: Data and supplemental analysis....................................................................43v

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES

1. INTRODUCTION
Plug-in hybrid electric vehicles (PHEVs) can use electricity as well as conventional fuelfor propulsion (Bradley & Frank, 2009). Mass production PHEVs have been available foralmost ten years. In the first half of 2020, PHEVs accounted for about 3.5% of all new
passenger car registrations in Europe, about 1.1% in China and about 0.3% in the UnitedStates. As of 2019, PHEVs were about one third of the global plug-in electric vehicle
fleet and their total fleet is expected to grow further until 2030 (IEA, 2020).
The potential of PHEVs to reduce local pollutant and global greenhouse gas emissions
strongly depends on their real-world usage and the share of kilometers driven on
electricity, the so-called utility factor (UF) (Chan, 2007; Jacobson, 2009; Flath, Ilg,
Gottwalt, Schmeck, & Weinhardt, 2013). Assessing fuel consumption of PHEVs is
challenging as PHEVs use both electricity and conventional fuel for propulsion in a ratiothat depends strongly on the driving and charging patterns of vehicle users as well as
on vehicle characteristics. Despite growing PHEV market shares, little is publicly known
about their real-world usage. There has been no systematic investigation, at least for
Europe. PHEV fuel consumption values are commonly assessed in standardized testingprocedures, or test cycles, such as the New European Driving Cycle (NEDC) or theWorldwide Harmonized Light-Duty Vehicles Test Procedure (WLTP). But the UFs usedin the WLTP and NEDC test procedures are based on outdated information provided
largely by vehicle manufacturers and may overestimate UFs and underestimate the realemissions of PHEVs.
The aim of this study is to better understand the real-world usage of PHEVs in China,
Europe, and North America, with a focus on Germany, the largest PHEV market inEurope. For this purpose, data sources on PHEV usage are statistically evaluated.
Additionally, driving profiles of conventional combustion engine cars are taken, and thefuel consumption and emissions performance of PHEVs are simulated. Based on the
results, policy recommendations are identified and discussed.
Section 2 introduces the data sources for this report. The results are presented in
section 3, starting with an overview of average deviation between actual and test-cycle
PHEV fuel economy in Section 3.1, followed by a discussion of the impact of vehiclespecific factors on fuel economy, namely the all-electric range and the system power.
Section 3.3 analyzes more individual vehicle factors such as the frequency of longdistance driving, charging behavior, and ambient temperature, followed by a discussion
in section 3.4. We close with policy recommendations in section 4.

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2. DATA AND METHODOLOGY
The data and methodology section consists of three parts. First, we give a roughoverview of the data sources used for this study and depict their main characteristics.
Second, we describe in detail the individual data sources forming the basis for our
empirical dataset. Third, we outline our methodology for rounding up our dataset.

2.1. OVERVIEW
We collected data on real-world usage of PHEVs from existing literature, research
institutions, companies, and online databases. The focus of our data collection was
on gathering data providing information on real-world usage such as real-world fuelconsumption, annual vehicle kilometers traveled (VKT), UF, charging behavior, ambient
temperature, and others.
Our data covers six countries: Canada (CA), China (CN), Germany (DE), theNetherlands (NL), Norway (NO), and the United States (US). It includes data fromprivate and company cars, or vehicles owned by an organization that are assigned to
an individual user and can also be used for private purposes. Table 1 gives an overview
of the total sample sizes by country and user group.
Table 1. Overview of PHEV sample by country anduser group, numbers of vehicles.
User groupCountry

SamplePrivate

China

6,870

PrivateGermany

1,385

PrivateNorway1,514

PrivateUS & Canada

Company car

Germany

Company car

Netherlands

TOTAL

84,06872
10,800104,709

In total, we collected data from primary and secondary sources of more than 100,000
PHEVs. Our sample is dominated by North American vehicles, but the sample sizes
for individual countries are still sufficiently large to discern general patterns and draw
conclusions. For Germany, for example, our sample accounts for 1% of the total stockof PHEVs and for all of Europe, 1.5% of the total stock (EAFO 2020). While most of thevehicles in our sample are private, a substantial number of more than 10,000 PHEVs
are company cars, allowing significant analyses for this user group.
For about 13,000 PHEVs from Germany and North America, individual vehicle datasuch as real-world fuel consumption, annual mileage, or UF are available. This allows
us to study differences between individual users of the same PHEV model in thesame country. Thus, we gain deeper insights into the data and into individual usage
than by just analyzing summary statistics, such as mean or median, which might
conceal a distorted distribution of real-world usage. Table 2 gives an overview of theindividual sources. While aggregated data in the literature is mostly limited to few
specific PHEV models, online databases or sources such as Spritmonitor.de, MyMPG,
or Xiao Xiong You Hao include a large variety of models.1 In total, the data includes 66
1

In our data and our analyses, we specified PHEVs according to their brand name (e.g. Toyota, BMW, Volvo,etc.), model name (e.g. Prius, 3 series, XC60), model variant name (e.g. Prius 1.8 Plug-In Hybrid, 330e
iPerformance, XC60 T8 Twin Engine), and model year or period. This differentiation is required because it is
only at this detailed level that important vehicle parameters, potentially having impact on real-world usage(such as test-cycle fuel consumption, engine or system power, and all-electric range), are usually identical. If
necessary, a further differentiation using equipment or accessory packages or engine types was carried out.

2

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESmodels, among which 202 model variants can be differentiated. A full list of mean fuelconsumption and mean UF by PHEV model, country, and user group is in the appendix.
Table 2. Detailed view of data sources. Overview of individual and aggregated vehicle data sources. Characterization by numberof PHEV models and model variants covered, sample size, predominant user group, and country.
SourcePHEVmodelsPHEV model
variants

Sample size

User groupCountry

Individual vehicle data
Spritmonitor.de

27

51

1,385

privateDE

German company

14

21

72

company car

DE

Voltstats.net

1

3

11,073

privateUS & CA

MyMPG

10

20

326

privateUSUC Davis

3

495

privateUS

Aggregated data
Xiao Xiong You Hao

60

92

6,614

privateCN

Figenbaum & Kolbenstvedt, 2016

7

7

1,514

privateNO

Zhou et al., 2018

6

6

192

privateCN

Xu et al., 2016

1

1

50

privateCN

Mengliang et al., 2014

1

1

14

privateCN

Van Gijlswijk & Ligterink, 2018 / TNO

11

11

9,600

company car

NL

Ligterink & Eijk, 2014 / TNO

3

3

1,200

company car

NL

CARB, 2017 Appendix G / GM1

1

48,000

privateUS

INL, 201455

14,750

privateUSCARB, 2017 Appendix G / UCD

1

1

8,309

privateUS

Smart et al., 2014

1

2

1,405

privateUS

Raghavan & Tal, 2020

4

4

110

private

US

TOTAL

66

202

104,7092.2. INDIVIDUAL DATA SOURCES
In the following, we describe the individual data sources that we used for gathering
primary data.

Spritmonitor.de
Spritmonitor.de is a free German web service that allows users to track fuelconsumption of their vehicles. It was established in 2001 and provides users withan easy-to-use app and web tool to monitor the fuel consumption of their vehicles
and to compare their fuel consumption with that of other users. Additionally, thereal-world fuel consumption data are available to the public. The database comprises
almost 850,000 vehicles from more than 550,000 registered users. Spritmonitor.deis available in German, English, French, and Spanish. The predominant share of users,
however, are assumed to be located in Germany.
Spritmonitor.de requires a free registration with a unique user name. A single user canregister several vehicles. When registering a vehicle, the user must provide various
specifications, such as brand, model, model variant such as engine type or equipment
line, fuel type and build year, vehicle power, and transmission type.
Before starting to track fuel consumption, users are asked to fill the fuel tank as the first
fueling serves as the reference for calculations of fuel consumption. Users can provide
various data, such as main odometer reading, distance traveled since the last refueling,fuel volume added, type of tire, driving behavior, route type, and use of air conditioning.

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ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESData available for analysis includes total mileage, total fuel consumption, and theresulting real-world fuel consumption of each vehicle. Consumption is calculated as
the total fuel used by the vehicle divided by total mileage. For alternative-fuel vehicles
running entirely or partly on electricity, in some cases the total all-electric mileage andtotal electricity consumption were given by the users. The UF was calculated according
to the methodology explained in section 2.3.
After data cleaning, 2 the initial dataset of 3,376 users was reduced to a sample of
1,385 with annual VKT ranging from 2,500 km to 89,000 km and a mean of 21,000
km. The sample represents 1% of the German PHEV stock and thus can be considered
representative, except that the annual mileage in the sample is higher than the German
fleet average.

UC Davis field trial
The University of California Davis collected driving data of battery electric vehicles(BEVs), PHEVs, and internal combustion engine vehicles (ICEVs) in the “Advanced
Plug-in Electric Vehicle Travel and Charging Behavior” project (Tal et al., 2020). Data
collection took place in three phases between June 2015 and July 2018 in 264 California
households. In-vehicle global positioning system-enabled data loggers were used,allowing the automated collection of detailed data on driving and charging behavior.
Among other values, the number of observations days, total all-electric andconventional mileage, as well as average daily mileage were logged from on-board
metering, thus allowing the calculation of the UF by dividing all-electric mileage
by total mileage. Real-world fuel consumption (FC) was calculated by multiplying
charge-sustaining fuel consumption of each individual vehicle according to the U.S.
Environmental Protection Agency (EPA) 5-cycle test by 1 minus the real-world UF:
FCreal = FC EPA
× (1 - UFreal). The subsample received from UC Davis consisted of 95
cs
individual PHEVs from three models, the Chevrolet Volt, Ford C-MAX Energi, andToyota Prius PHEV. The observation periods ranged from half a year up to more than
one year, and total distance, between 7,000 km and 55,000 km, with a mean of 22,500
km. As UC Davis carefully selected representative households for its data acquisition
and made use of automated data logging, validity of the data is considered high.

Voltstats.net
Voltstats.net is an online database that automatically collects from an additional
device real-world fuel consumption data of Chevrolet Volt users in the United States
and Canada. Data for 11,703 Chevrolet Volts was obtained from registered users of thewebsite at the time of retrieval in January 2020. Every user profile contains individual
cumulative daily data on electric and gasoline-powered mileage, including the numberof gallons burned daily in driving, plus summary statistics on the UF, total average
miles per gallon (MPG) and charge-sustaining-mode MPG.
The dataset comprises information reported between April 2011 and January 2020,
with a total of 4.3 million driving days. The data was pre-processed, cleaned and2

For this study, Spritmonitor.de provided a dataset comprising all PHEV data entries. Data cleaning andvalidation consisted of several steps. In a first step, vehicle models for which fewer than five users provided
data were sorted out as well as users with fewer than seven observation days, total mileage of less than
1,500 km, total fuel consumption of less than 50 liters, or missing odometer readings—thus ensuring sounddata with an adequate level of comparability for subsequent analyses. In a second step, mild-hybridelectric vehicles (HEVs) that were incorrectly declared as PHEVs were sorted out. Several criteria were used
as an indicator for PHEV models: the specification of values for all-electric mileage driven or electricity
consumption, clear information in the model or model variant specification (“plug-in,” “PHEV” or according to
manufacturer’s classification) as well as build year or system power corresponding to PHEV models available
in Germany (see paragraph “PHEV model list” in section 2.2 for details). The VKT per vehicle were obtainedby dividing the total mileage (latest main odometer reading minus first entered main odometer reading)by the number of observation days (latest date minus first date) and by multiplying the result by 365 days.
Finally, test-cycle values for fuel consumption and all-electric range were assigned to the Spritmonitor.deuser’s individual vehicles based on the PHEV model list. The UF was calculated according to the methodology
explained in section 2.3.

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ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES

cumulative mileage values converted to daily driven kilometers. Data cleaning involved
the exclusion of values with daily VKT greater than 1,500 km and with higher electric
VKT than total VKT per day. After data cleaning, the average number of driving days
per vehicle was 410 with a median of 303 and maximum of 2,500 days.
Based on the available data, we calculated the following parameters: electric vehicle
kilometers traveled, gasoline vehicle kilometers traveled, and total vehicle kilometers
traveled. The average distance traveled was extrapolated to annual values. The
individual UF per user was obtained by dividing all-electric kilometers by total
kilometers driven during the observation period.

German company fleet data
From a large private company in Germany with more than 10,000 employees, we
obtained a comprehensive dataset of a corporate company car PHEV fleet. The data
covers leased PHEVs for which the leasing contract had already ended. The vehicles
were used by specific employees and only available to the specific employees. The
utilization period was between half a year and four years, covering 2016–2020.
Detailed vehicle specifications are available, such as vehicle brand, model, and model
variant. Driving data comprises the main odometer reading when returning the vehicle
after the end of the leasing contract and real-world fuel consumption over the entire
observation period. The UF was calculated according to the methodology explained in
section 2.3. For this study, a sample of 72 vehicles was available. Annual VKT ranged
between 12,000 km and 55,000 km, with a mean of 30,000 km.

MyMPG on Fueleconomy.gov
MyMPG is a tool allowing users to track their fuel consumption and to share andcompare it with that of other users or with official EPA fuel economy ratings. MyMPG
is embedded in Fueleconomy.gov, which is an official website of the U.S. government,
providing information for consumers on fuel-efficient driving and for making informed
vehicle purchasing decisions with respect to environmental effects. The website
is maintained by the U.S. Department of Energy, and data is provided by the EPA.
Currently, more than 100,000 users are active on MyMPG.
Users are asked to provide fuel log data by either monitoring the main odometerreading or trip odometer reading as well as added fuel volume when refueling. By
dividing the difference of main odometer readings at two subsequent refueling eventsor the trip odometer reading by the refueled fuel volume, the real-life fuel consumption
can be calculated. As an alternative, car-computed average fuel consumption andrespective trip odometer readings can be entered. Annual VKT and observation periodof the individual users are not publicly available. The UF was calculated according to
the methodology explained in section 2.3.
Because MyMPG users enter fuel consumption data on a voluntary basis, there is a riskof self-selection bias in the data for consumers who are particularly concerned about
fuel economy (Tietge, Diaz, Yang, & Mock, 2017). The mean fuel consumption in the
MyMPG sample could thus be lower than average and vehicles could be used more
intensely than on average.

Xiao Xiong You Hao data
Xiao Xiong You Hao (xiaoxiongyouhao.com) is an automobile information andevaluation company. It provides a mobile application for drivers to know their individual
real-world fuel consumption based on self-reported gas filling data. The application
was launched in 2010 and had more than 5.27 million downloads by the middle of2020. The company has real-world fuel consumption data for more than 1 million
drivers and nearly 32,000 vehicle models.

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ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESTo start using the application, users select their specific vehicle model version. Aftereach refueling, users record fuel volume and odometer readings. Based on users’ realworld average fuel consumption, the actual average fuel consumption of each vehicle
model is calculated and displayed in the application.
The dataset provided by Xiao Xiong You Hao has detailed information on the 32,000
vehicle models including average fuel consumption, number of samples, and thefollowing specifications: brand name, series name, model name, NEDC CO2 emissions,engine power, curb weight, and NEDC fuel consumption. For a few models, WLTP fuelconsumption values were also available.

PHEV model variant list
For the planned analyses, detailed vehicle specifications were required. One is system
power, or the maximum combined power of electric and combustion engine, which
is not necessarily the sum of the two values. Others are fuel type; fuel consumption,
including charge-depleting, charge-sustaining, and combined;3 and all-electric range
according to NEDC, WLTP, and EPA test cycles. Most data sources did not containall of this information. Using the ADAC-Autokatalog (ADAC, 2020), the required
information for PHEV models available on the European market was obtained showing
NEDC and WLTP values for combined fuel consumption and all-electric range of thelatest models. Additionally, Fueleconomy.gov provided a list of PHEV models for the
U.S. market showing EPA values for combined fuel consumption and charge-depleting
consumption derived from CO2 emissions. The Xia Xiong You Hao database provided
NEDC combined fuel consumption as well as NEDC all-electric range for PHEVs
available in China.

2.3. METHODOLOGY FOR UF CALCULATION
In cases where the actual UF is missing, we estimate the real-world UF from the realworld fuel consumption UFreal = 1 – FCreal / FC realcs with CS indicating charge sustaining
mode. Here, FC realcs is approximated by taking NEDC values with 50% addition for
NEDC
real-world driving: FC real
= 1.5 FCNEDC/ (1 – UFNEDC). In cases with very
cs = 1.5 FC cs
high real-world fuel consumption, this approach can lead to negative UF. We set theestimated UF to zero in these cases. If EPA values are available, we use EPA values
EPA
for charge-sustaining-mode fuel consumption: FC real
. Likewise, when the realcs = FC cs
UF is known, the real-world consumption can be estimated by inverting the above
real
equations: FCreal = FC real
) = 1.5 FC NEDC
(1 – UFreal). For all WLTP cases with UF
cs (1 – UFcsmissing, NEDC fuel consumption values were available and the NEDC imputation
procedure was applied.
We compared different approaches of estimating the UF from average fuelconsumption. An alternative method takes the largest average fuel consumptionof a larger sample of vehicles from one PHEV model and assumes this maximum isapproximately equal to the charge-sustaining-mode fuel consumption. However, thismethod is applicable only with a sufficiently large number of vehicles observed per
PHEV model and can also be biased when a sample is very large.
The method explained above is slightly optimistic as a 50% deviation from NEDC
is slightly above the fleet average deviation for HEV (cf. Tietge et al., 2019), as we
increase the denominator in the second term of UFreal = 1 - FCreal / FC realcs , thereby
making UF larger. This uniformed approach can have different level of impact on datafrom different regions as the gap between real-world and NEDC fuel consumption has

3

We distinguish in the following two PHEV operation modes: In charge-depleting mode the electric engine is
responsible for propulsion, and the combustion engine is switched off. In charge-sustaining mode (usuallyapplied when the battery has been fully depleted), the combustion engine and conventional fuels are (mainly)
used to keep the battery state-of-charge within a small window. In real operation, mixed and blended modes
are also possible for some PHEVs.

6

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESbeen estimated the be 37% for the EU in 2014 and 25% in China (Tietge, Díaz, Yang,& Mock 2017). So, this 50% assumption likely enlarges the UF factor more for China
than for EU. Finally, we compared the average UF estimated from both methods andobtained a mean UF of 40% from taking the largest in-sample fuel consumption as
charge-sustaining-mode fuel consumption compared with the estimate from NEDC
values, with a 50% increase for real-world driving in charge-sustaining mode leading to
39% average UF.

7

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES

3. RESULTS: REAL-WORLD PHEV USAGE AND
FUEL CONSUMPTION
Our analysis of real-world PHEV usage is divided into three parts. In the first part,
section 3.1, average values and distributions for UF and fuel consumption arepresented. In section 3.2, we then analyze the impact of vehicle properties, mainly
all-electric range and system power, on the UF and fuel consumption. Lastly, section
3.3 provides information on how external factors at the individual level, such as longdistance driving, charging behavior, and ambient temperature, affect the UF and fuelconsumption of PHEVs.

3.1. AVERAGE REAL-WORLD PHEV USAGE
In a first step, an analysis of the real-world usage of PHEVs is conducted. Real-world
fuel consumption and UF are employed as main indicators for real-world usage. In
descriptive analyses they are compared with test-cycle fuel consumption and test-cycle
all-electric range. This gives a good first indication of potential factors influencing UF
and real-world fuel consumption.

3.1.1.

Fuel consumption compared with test cycle

We use the full sample to compare actual real-world fuel consumption with test-cycle
values. Figure 1 shows the distribution of actual fuel consumption in relation to NEDC
values. 4 The dashed vertical lines at 100% represent perfect agreement between actualand test cycle values.
China (n = 6,870)

0%

200%

400%

600%

Norway (n = 1,514)

Germany (n = 1,457)

0%

200%

400%

600%

Netherlands (n = 10,800)

0%

200%

400%

600%

US & Canada (n = 84,068)
User groupcompany car
private

0%

200%

400%

600%

0%

200%

400%

600%

Relation to NEDC fuel consumption
Figure 1. Distribution of real-world fuel consumption in relation to NEDC test cycle. Shown is the
distribution by country. 100% (vertical dashed line) corresponds to real = test cycle. Private users inblue and company car users in red. Small rugs next to the x-axis indicate individual observations at
PHEV model variant level. Total number of vehicles in the sample is included by country.

We observe a broad distribution of actual real-world fuel consumption values, muchbroader than for conventional combustion engine vehicles (Tietge et al., 2019). The
average deviation from test-cycle values differs among countries, but on average, real

4

8

Data for the United States is only for PHEV models with NEDC fuel consumption available too(23 of 40 observations).

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES

fuel consumption is two to three times higher than the test-cycle values for private
cars and three to four times higher for company cars. Table 3 below summarizes themean relation between actual and test-cycle fuel consumption, and thus CO2 emissions.
For private vehicles, the mean relation is 300%–340% (the range indicates the mean
with two standard errors) and 135%–235% for the sample-size weighted mean. 5 The
latter is noticeably smaller as North American vehicles—mainly the Chevrolet Volt,
Toyota Prius, and BMW i3 REX, with small test-cycle deviation—dominate the fullsample. For company cars, with data from Germany and the Netherlands, the deviation
is even higher. The mean relation is 305%–395% and the sample-size weighted mean
relation is 340%–410% (including two standard errors).
Table 3. Overview of mean relation to NEDC fuel consumption.
Range of relation includes two standard errors.
Mean relation

Sample-size
weighted relation

Private300–340%

135–235%

Company car

305–395%

340–410%User group

For the country-specific analysis, the most recent data is from 2019 and 2020 for
Germany and China. For both countries, the average gap between the official NEDC
and real-world fuel consumption values for PHEVs of private owners is high. In China,
the difference is 395% ±20% and in Germany, 247% ±13%. Those compare with 195%±20% in the United States and 195% ±10% in Norway.6 The particularly large deviationfrom test-cycle values for China is noteworthy. China has a much smaller share of
the population living in detached or semi-detached houses, and fewer people havegarages compared with Western Europe or North America (Li, Plötz, & Zhang, 2020).
Accordingly, many PHEV users in China probably have no access to easy homecharging. This is consistent with the low UF in China (see below).
Company car data is available only for Germany and the Netherlands, with greater sample
sizes for the Netherlands. The distribution of real-world fuel consumption in Germany and
the Netherlands for company cars is comparable though, with a peak of around 400%, orfour times higher than according to the NEDC, with a broad distribution.
In Europe the NEDC has been replaced by the WLTP, which is assumed to moreaccurately reflect real-world fuel consumption. As the WLTP is rather new, the current
PHEV stock is still dominated by NEDC models. Real-world PHEV usage data wasavailable only for a limited number of WLTP models. Figure 2 shows the distributionof real-world fuel consumption as compared with test-cycle values for WLTP-certified
PHEV models. The total number of vehicles in the sample is limited—137 vehicles forGermany and 150 for China—but general patterns are clear. Similarly to the NEDC
models, the actual fuel consumption is four times higher in China and two times higher
in Germany.

5

Individual model variants are represented in the data by varying numbers of observations. Thus, to ensurecomparability of values on a model variant level (as represented in most graphs) the average values are
sample-size weighted.

6

All estimates are the unweighted mean ± two standard errors. The sample-size weighted result for China is412% ±21%. The sample-size weighted result for Germany is 240% ±10%. The respective results for company
cars in Germany are 350% ±45% for the unweighted mean and 375% ±34% for the weighted mean. The
sample-size weighted result for the United States is 160% ±33% and for Norway 204% ±14%. For company carsin the Netherlands the unweighted mean is 400% ±58%.

9ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESChina (n = 150)

0%

200%

400%

Germany (n = 137)

600%

0%

200%

400%

600%

Relation to WLTP fuel consumption
Figure 2. Distribution of real-world fuel consumption in relation to WLTP test cycle.
Distribution by country. 100% (vertical dashed line) corresponds to real = test cycle value.
Shown are only private users. Small rugs below the x-axis indicate individual observations.

The distribution of real-world fuel consumption is much broader for PHEVs thanfor ICEVs as the UF is an additional quantity that can vary substantially amongindividual vehicles. If the UF is fixed, the distribution is narrow, similar to fuelconsumption distribution in combustion engine vehicles. Figure 3 demonstrates thiseffect for 10,304 Chevrolet Volt vehicles from the Voltstats.net sample. We takesubsamples with approximately the same UF, allowing vehicle mean UF to fluctuate
by only ±2 percentage points, and in Figure 3 show the distribution of real-world
fuel consumption in liters/100 km for UF = 20%, 30%, and as high as 90%. For each
UF we observe a narrow distribution of actual consumption, similar to conventional
combustion engine vehicles (cf. Tietge et al., 2019).

NEDC fuel consumption
UF=90%

UF=80%
UF=70%

UF=60%
UF=50%
UF=40%
UF=30%

0

1

2

3

4

UF=20%

5

6

7

Fuel consumption [l/100 km]
Figure 3. Distribution of fuel consumption of Chevrolet Volt vehicles for fixed UF. The
distribution shows the average fuel consumption of Chevrolet Volt vehicles in the Voltstats.net
database for fixed UF. Here, UFs have been rounded by ±2 percentage points, so UF = 80% are
all vehicles with 78% < UF < 82%. The dashed vertical line indicates the NEDC fuel consumptionof 1.2 l/100 km.

10

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESIn summary, PHEVs in all countries in the sample show a clear deviation from testcycle values, irrespective of the cycle. The range of deviations is much larger than forconventional combustion engine vehicles due to the large range of UFs.

3.1.2. Utility factors
A key indicator of PHEV usage and the potential environmental benefit is the shareof kilometers driven on electricity, the UF. The UF is calculated as the total of electrickilometers divided by the total distance traveled by a vehicle.
Figure 4 shows the average UF as a function of all-electric range for all vehicles in thesample compared with UFs assumed by the NEDC and European WLTP test cycles.7
Almost all average real-world UFs are below test-cycle values. In the sample, PHEVs
with ranges below 60 km on the WLTP, or below 80 km on the NEDC, show particularlyhigh deviation from test-cycle values. Long-range PHEVs in the sample tend to comecloser to test-cycle values.
Comparing privately owned vehicles and company cars, we observe lower average UFs
for a given range for company cars throughout the sample. Accordingly, the deviationfrom test-cycle UF is even higher for company cars.
100%
WLTP

90%
80%

NEDC

Utility factor (UF)

70%
60%
50%
40%
30%
20%
10%
User group
0%

0

20

40

60

80

100

120

140

160

company car
180

200

220

private240

NEDC range [km]Figure 4. Average utility factors (UFs) of all PHEVs in the sample versus all-electric range.
The all-electric range is given as approximate WLTP range where the WLTP range is assumed tobe three-quarters of the NEDC range. Shown also are the WLTP UFs (solid line) and NEDC UFs(dashed line).

The deviation between average UF in the sample and test-cycle values shows
noteworthy differences among countries (cf. Figure 5 and Table 4). It shows the largest
deviation from test-cycle values in China and for company cars in the Netherlands but
is closest to test-cycle values for privately owned vehicles in Norway and the UnitedStates. Furthermore, UFs in most countries show a tendency to increase with allelectric range, as expected.

7

The NEDC UF is UFNEDC = AERNEDC / (AERNEDC + 25 km) where AERNEDC is the NEDC all-electric range. The WLTP
UF in Europe is given by UFWLTP = 1 - exp[-Σi = 1...10 ci(AERWLTP / dn)i] where AERWLTP is the WLTP all-electricrange and the numerical constants ci and dn for Europe are dn = 800, c1 = 26.25, c2 = -38.94, c3 = -631.05, c4 =5964.83, c5 = 25095, c6 = 60380.2, c7 = -87517, c8 = 75513.8, c9 = -35749, c10 = 7154.94 according to (EC 2017).

11

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESTable 4 shows mean UFs according to the NEDC, actual mean UFs, and the mean of theratio between real and NEDC UFs by country and user group. 8 Again, private vehicles in
Norway and the United States come closest to the NEDC values, whereas the deviation
is highest for private vehicles in China and for company cars. Globally, private vehicles
achieve only about half the NEDC UF, and company cars only a third. As the UF curves
for NEDC and WLTP are highly similar (Figure 4), similar deviations can be expectedfrom WLTP UFs.
China (n = 6,870)

100%

Germany (n = 1,457)

100%

Utility factor (UF)

NEDC

NEDC

NEDC

80%

80%

80%

60%

60%

60%

40%

40%

40%

20%

20%

20%

0%

0

25

50

75 100 125 150 175

Norway (n = 1,514)

100%

0%

0

25

50

NEDC

60%

60%

40%

40%

20%

20%

25

50

75 100 125 150 175

0%

0

25

50

75 100 125 150 175

NEDC

80%

0

75 100 125 150 175

US & Canada (n = 84,068)

100%

80%

0%

Netherlands (n = 10,800)

100%

0%

CountryCN

NL

US

User groupcompany car
0

25

50

75 100 125 150 175

NEDC range [km]Figure 5. Utility factors of PHEVs with different all-electric ranges by country. The average
specific UFs of individual PHEV model variants are shown as observed in our sample for private
vehicles (small triangles) and company cars (small circles) with the NEDC UF (dashed line). Total
number of vehicles in the sample is included by country. The grey dots are the full sample, andeach small plot emphasizes the data from one country by country-specific color.

8Note that the mean of the ratio is shown and not the ratio of the means. Sample-size weighted averages are missing
for the Netherlands as vehicle individual sample size was not available for company cars in the Netherlands.

12

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES

privateDENOTable 4. Overview of mean UF and relation to NEDC UF by country and user group. Shown aremeans and sample-size weighted means for the NEDC UF, the actual UF and the mean relation
between actual and NEDC UF.
MeansCountry

User group

UF

NEDC

UF

real

Sample size weighted means
UF

/UF

real

NEDC

UFNEDC

UFrealUFreal/UFNEDC

CN

Private

71%

26%

37%

72%

25%

34%

DE

Private

65%

43%

65%

66%

47%

71%

NO

Private

64%

53%

82%

67%

55%

82%

US

Private

69%

54%

80%

76%

69%

90%

All

Private

69%

37%

54%

75%

65%

86%

DE

Company car

62%

18%

27%

60%

12%

19%

NL

Company car

65%

24%

36%

-

-

-

All

Company car

63%

20%

31%

-

-

-

Some country-specific effects can partially be explained by country-specific factors
and the data sources. For the United States, only private vehicles are in the sample, andmany of the observations are from early adopters who purchased their PHEVs someyears ago when PHEVs were still quite uncommon. These users are expected to havebeen more likely to purchase a PHEV only if they had an option to recharge the vehicle
regularly. In addition, 21 of the 23 PHEV model variants in our U.S. sample are Chevrolet
Volt, BMW i3, and Toyota Prius PHEVs. Those are probably preferred options forvehicle buyers with above-average environmental concerns and tend to be frequently
charged. Lastly, the information on all-electric ranges and realistic fuel consumption
stems from EPA testing, which is closer to real-world values than the NEDC (Tietge etal., 2017). Accordingly, PHEV users in the United States are more likely to buy a PHEV
that actually fits their range requirements.
In Norway, battery electric vehicles receive higher incentives than PHEVs, so
PHEVs are probably not bought mainly to benefit from the lower purchase price ortaxation as could be the case for company cars, but only if the users intend to usethem appropriately. In addition, Norway has low electricity prices and high gasoline
prices, making electric driving particularly attractive from an economic point of view.
Furthermore, a small additional effect could come from the comparative ease of publiccharging in Norway, as there is a single card that enables charging at almost all publiccharging points across the country (Figenbaum & Kolbenstvedt, 2016).
The mean UFs in China show a large variation even for a fixed range and only aslight tendency to increase with range. Chinese authorities monitor the real-world
performance of PHEVs, but there is no enforcement or regulation that effectively
encourages car owners to increase electric driving or charging, and there are no
requirements on how frequently PHEV users should charge their vehicles. Furthermore,
the lower availability of garages and private parking spots in China makes it more likelythat PHEV users lack a regular night-charging option (Li et al., 2020). The restrictions
on driving and purchase of conventional-fuel vehicles in first- and second-tier cities
such as Beijing, Shanghai, and Hangzhou make PHEVs highly attractive irrespectiveof actual usage. When purchasing PHEVs in Shanghai, proof of having charging
conditions is required, which can be domestic charging points or public charging
points at workplaces.9 No further measures are provided to ensure that car-owners
charge their vehicles. According to the findings of the “2019 Shanghai New Energy
Vehicles Big Data Research Report,” (Eefocus.com, 2019) most PHEV users charge
their vehicles only one or two days a week, regardless of their weekly mileage.

9

At present, self-owned brands occupy an absolute dominant position in China’s PHEV market. Among them,
BYD and SAIC passenger cars account for a larger share of the market. Due to local protectionism, Shanghai
has the largest number of PHEVs in China.

13

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESMany PHEVs show high daily kilometers traveled (Eefocus.com, 2019). In summary,consumers’ motivations for purchasing PHEVs mainly include the government’s
preferential policies and low dependence on charging. In the case of inconvenientcharging, PHEVs will be used more directly as conventional-fuel cars.
The Netherlands had high incentives for PHEVs as company cars from 2012 to 2016,leading to a strong sales increase in PHEVs for company cars. However, no additional
incentives for company car charging were enacted. Instead, many PHEV company car
users in the Netherlands still have fuel cards that allow free refueling, while they haveto pay privately for charging their PHEVs at home. All PHEV users within the Dutch
sample are in possession of a fuel card (van Gijlswijk & Ligterink, 2018). Because of
these common financial disincentives, many PHEV company car users simply did notfrequently charge their PHEVs, resulting in particularly low UFs.
Lastly, for Germany the data is quite recent, mainly from 2019 and early 2020. Homecharging should not be a problem in Germany for the vast majority of PHEV users as
about three-quarters of passenger cars in Germany are parked in private garages or
car ports overnight (MiD, 2018). The proportion may be even higher for PHEV owners
reflecting higher household incomes required for covering higher costs of PHEVs.
(Plötz, Schneider, Globisch, & Dütschke, 2014b; Frenzel, Jarass, Trommer, & Lenz, 2015).
Company car users in Germany, on the other hand, have similar financial disincentives
as those in the Netherlands. Although they receive a tax benefit if they use a PHEV,
it is not conditional on electric driving, and many can be expected not to pay forconventional fuel as in the Netherlands.
In summary, real-world UFs are typically only half the test-cycle values for private
vehicles and are even lower for company cars. Yet some private users achieve as
much as 80% of the test-cycle UF, and users in Norway and the United States in our
sample are closer to test-cycle UF compared with other countries.

3.1.3. Annual electric mileage
The environmental benefit of PHEVs depends not only on the share of kilometers
driven on electricity but also on the total annual number of electric kilometers, as
this determines the amount of conventional fuel saved in comparison with driving
conventional combustion-engine cars. Annual mileage data in our sample is available
for vehicles in Germany and the United States.
Figure 6 shows the annual electric mileage for PHEVs in Germany and the United States
where annual mileage information is available as a function of NEDC all-electric range.
Also shown is a local regression, the shaded area, that indicates how the expectedannual electric mileage increases with all-electric range. Most PHEVs in the sample
have NEDC ranges between 30 km and 60 km with annual electric mileage around
5,000–10,000 km, which increases with range. PHEVs with high all-electric rangesof 80 km or more10 achieve 12,000–20,000 km mean annual electric mileages. Thosevalues are comparable to the mean total annual mileage of the car fleet in Germany,or about 14,000 km a year, and in the United States, or about 21,700 km a year (seebelow). The high mean annual electric kilometers despite low UFs are possible due tohigh annual mileages of PHEVs (see section 3.3.1 below). The same results hold whenannual electric mileage is analyzed separately for PHEVs and range-extended electricvehicles (see appendix).

10 These long-range PHEVs are technically also known as range-extended electric vehicles, such as the Chevrolet
Volt, Opel Ampera, and BMW i3 REX. They show much lower deviation between actual and test-cycle UF thanother vehicles in the sample and data from other countries.

14

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESAnnual electric mileage [km]20,00015,00010,0005,000

0Sample Size
1
10Country
Germany
0

20

40

60

80

100

120

100

1,000

United States
140

160

180

NEDC range [km]Figure 6. Annual electric mileage by NEDC range. Mean annual electric mileage by PHEV
model variant for the United States (squares) and Germany (circles). Every data point
corresponds to a PHEV model mean with different sample sizes (indicated by the size of thesymbol). The shaded area is a sample-size weighted local smoother (95% confidence bands ofgeneralized additive model).

In summary, we observe a clear expansion of annual electric kilometers withincreasing NEDC range. Long-range PHEVs can achieve as many as 15,000 km ofannual electric distance. This is consistent with earlier findings (Plötz, Funke, Jochem,& Wietschel, 2017).

3.2. IMPACT OF VEHICLE PROPERTIES: ALL-ELECTRIC RANGE AND
SYSTEM POWER
The comparability of different PHEVs is limited. Not only the all-electric range but also
the engine size and power influence fuel consumption and direct CO2 emissions, since
they affect fuel consumption during nonelectric driving mode. High power also acts
as a proxy for high vehicle mass (Plötz, Funke, & Jochem, 2018a) and is assumed toincrease the likelihood of more aggressive and thus fuel-consuming driving. Likewise,
different user groups may have different driving and charging behaviors, and different
countries could have different charging infrastructure and other framework conditions.
In the present section, we analyze the effects of different vehicle properties while
controlling for user group and country effects.
As a background to the impact of range, Figure 7 shows the all-electric ranges of
PHEVs, differentiated by the date of model introduction. Focusing on those vehicle
models introduced since 2018, most NEDC-certified PHEVs had 30–50 km of all-electricrange with a tendency toward 50 km. For WLTP-certified PHEVs, all-electric ranges are
40–50 km, with an increasing tendency. However, both NEDC and WLTP ranges do notcorrespond to average real-world ranges (Dornoff, Tietge, & Mock, 2020).

15

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES

300
Range extender
False

True

Electric range [km]Test procedure
NEDC

WLTP

200

100

0

2012

2014

2016

2018

2020

Date of model variant introduction
Figure 7. All-electric range of PHEV models by date of model variant introduction. Small
circles indicate individual model variants with NEDC ranges in blue and WLTP ranges in orange.
The solid lines are local regression plot smoothers.

3.2.1. Methodology
To separate the effect of different levels of vehicle power and all-electric range, we
regress the actual fuel consumption and UF on vehicle power and all-electric range.
The aim of the regression analysis is to quantify the effect size and to separate theeffects of vehicle range and power in our sample of PHEV models. As the different
subsamples have very different sample sizes and cover different models, we compare
sample-size weighted and unweighted regression models including user group andcountry as control variables. The regression model details are given in the appendix.
The data for the present section is the full sample as range and system power areavailable for all models.

3.2.2. Results
We start with the effect of system power and all-electric range on fuel consumption
and thus tail-pipe CO2 emissions. For all-electric range we use the NEDC range as itis readily available for almost all models. System power, or combustion engine powerplus electric motor power, is measured in kW.11 System power is used as a proxy forengine displacement, weight, and model-specific aggressiveness of driving. Table 5

11

Strictly speaking, the system power is the maximal power available for propulsion. For most PHEV models, thisis the sum of engine and electric motor power. Yet, for some vehicles, notably range-extended electric vehicles
such as the Chevrolet Volt or the BMW i3 REX, the engine is not directly used for propulsion but to charge thebattery, so the system power is smaller than the sum of engine and electric motor power.

16

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES

summarizes the impact of range and power on fuel consumption.12 The regression
results show relatively high goodness of fit (adjusted R² > 0.8).
Table 5. Factors impacting real-world average fuel consumption. Range of effects according
to aggregated data sources and regression analysis. Changes are in percent around expectation
values and controlled for user group and country-specific effects.13
Change in expected fuel consumption+10 km all-electric NEDC range+10 kW system power-8% to -14%
+2.5% to +3.5%

Controlling for user group and country-specific effects, we find that a 10 km increasein NEDC all-electric range decreases the fuel consumption by 8%–14% with all otherparameters fixed. The range of the estimate includes a 95% confidence interval fromthe regression. Within the range of our data, fuel consumption and thus direct CO2
emissions are reduced by 1.1% with each additional kilometer of range, so for every
63 km of all-electric range the direct fuel consumption and direct CO2 emissions arehalved, with 50–87 km as the 95% confidence interval.
As most PHEV models have about 50 km of NEDC all-electric range, a 20% increasein range, corresponding to 10 km, leads to a decrease of 8%–14% in fuel consumption.
Conversely, an increase in system power by 10 kW leads to an increase in fuelconsumption of about 3%. System power in the PHEV models in our sample covers a
range of 90–674 kW with a mean of 225 kW. So a 20% increase in system power—45
kW—would lead to an increase in fuel consumption of 11%–16%, keeping all otherfactors constant.
A positive effect of range on fuel consumption is expected as longer ranges imply more
electric driving and less use of combustion engines. But the effect of system power isalso comparatively strong in terms of percentage change. The effect of system poweris also clearly visible in an analysis of the mean fuel consumption by range and system
power in Figure 8. Between 25 km and 70 km of all-electric range, little impact of therange is visible, though it can be detected statistically, but higher power clearly leads
to higher fuel consumption.14

12 Since fuel consumption is strictly non-negative, we use an exponential function for the effect of range andpower and control for user group and country-specific effects with the following regression model FCreal= exp(β0 + β1Power + β2range + β3usergroup + β4country) + ε. Here, the system power (Power) in kW, hasbeen used as a proxy for engine displacement, weight, and model-specific aggressiveness of driving. The
chosen dependence on all-electric range and power are: For rangeg 0, the fuel consumption approaches afinite value (i.e. the fuel consumption in the charge-sustaining mode) and is decreasing to zero for rangeg∞(i.e. a negative β2). Likewise, the fuel consumption approaches zero for Powerg 0 and grows with increasing
power (i.e. positive β1). The regression is performed after taking logarithms of the above equation In (FCreal)= β0 + β1Power + β2range + β3usergroup + β4country + ε by ordinary least squares. The model itself and all
coefficients are significant (p < 0.05) and the coefficients have the expected signs (β1>0 and β2<0). The details
are given in the appendix.
13 We controlled for user group and country. Results for range and system power are highly significant (p<0.01).
Reference categories for categorical variables are “private” for user group and “Germany” for country. Change
is not significantly different from zero for Norway and the United States. The effect of company cars is
between +10 and +50%; the effect of China as compared with Germany is between +35 and +55%; and for the
Netherlands, between +10 and +30%.
14 As every circle in the figure corresponds to one PHEV model variant and the highest number of different PHEV
model variants is present in the Chinese Xiao Xiong You Hao sample, models in use in China dominate the figure
in terms of high fuel consumption and high number of models. More important than the visual impression are thestatistically significant regression results below, which also control for country-specific effects.

17

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESFuel consumption [l/100 km]

12
System
power
in kW

10

Sample
size

400
8

300

6

1
10
100

200

1,000

100

10,000

4

2

0

0

50

100

150

200

250

NEDC range [km]Figure 8. Fuel consumption of PHEVs by range and system power. The average fuelconsumption of PHEV model variants is shown as circles with the size of the circle indicating
the sample size and the color indicating the system power (from low in blue to high in red). The
full sample of all countries and user groups is included.

As the UF shows country-specific effects, we also analyze the impact of range on fuelconsumption by country in Figure 9.15 Consistent with the country-specific UF results,
we observe high fuel consumption for private vehicles in China and company carsin the Netherlands. Yet, all other countries clearly show the effect of decreasing fuelconsumption with increasing all-electric range.16
The model-variant average fuel consumption is also determined by the model-variant
average UF. We show the effects of increase in range or system power in Table 6.17

15 The regression results above already control for country-specific effects as the countries have been included
as control variables.
16 A comparison of selected PHEVs and corresponding ICEVs showed a 95% higher fuel consumption of ICEVs.
We used the Mitsubishi Outlander, VW Golf GTE, VW Passat GTE, BMW 225xe, and Audi A3 e-tron forcomparison. These were the five PHEV models with the highest sample size in the Spritmonitor.de data with arespective ICEV not having mild-hybrid function and a sample size of 10 of more. We selected the appropriate
ICEV model according to the PHEV model’s first build year and system power in hp (with a range of ±20 hp toaccount for inaccurate database entries).
17 The regression contains the same explanatory factors as before (range, power, user group, and country),
but the dependent variable is the UF. As the dependent variable is a percentage, we perform fractional logit
regression, and the table shows so-called marginal effects at mean.

18

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESFuel consumption [l/100 km]

China

Germany

Netherlands

12

12

12

10

10

10

8

8

8

6

6

6

4

4

4

2

2

2

0

0

50

100

150

200

250

0

0

50

Norway
12

10

10

8

8

6

6

4

4

2

2
0

50

100

150

150

200

250

0

0

50

150

200

250

NO

US

200

250

0

Country
CN

0

50

100

150

200

250

NEDC range [km]Figure 9. Fuel consumption by range and country. The average fuel consumption values of
PHEV model variants are shown as colored circles. The grey dots are the full sample, and eachsmall plot emphasizes the data from one country by country-specific color. The full sample of
all countries and user groups is included.
Table 6. Factors impacting average UF. Range of effects according to aggregated data sources
and regression analysis. Changes are in percentage points around mean values.18
Change in expected UF in percentage points+10 km all-electric NEDC range+3 to +5 percentage points+10 kW System power-1 to -3 percentage points

As expected, the results indicate that higher all-electric ranges lead to higher UFs.
More specifically, an increase in NEDC all-electric range by 10 km leads to an increasein UF of 3–5 percentage points at a 95% confidence interval. On the other hand,an increase in system power by 10 kW leads to a reduction in expected UF by 1–3
percentage points. The results for range are consistent with adding annual mileage
as control variable (see section 3.3.1 below), but the effect of system power is slightly
smaller when annual mileage is included, by -½ to -1 percentage point).
The decrease in UF from increasing system power might be surprising as the all-electricrange should be the main vehicle attribute impacting the share of electric driving.
However, vehicles with small electric motor power and higher combustion engine
power tend to be programmed and used differently. As extreme examples, a rangeextended electric vehicle could have a very small combustion engine power sufficient

18 Reference categories for categorical variables are “private” for user group and “Germany” for country.
Company cars have 10 to 30 percentage points lower UFs compared with private vehicles. PHEVs in China
have 10–30 percentage points lower UFs than in Germany and PHEVs in Norway have 4–12 percentage points
higher UF. The change in UF is not significantly different from zero for the Netherlands and the United States.

19

100

US & Canada

12

0

100

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESDENLto charge the battery during driving with the engine programmed to start only incases of empty battery. Compare this with the case of a sports-type vehicle where theelectric motor is used to provide additional torque and acceleration. Such a vehicle
would have very high system power and high fuel consumption as the combustion
engine would have high displacement and would be used often. Additionally, suchvehicles are more likely to be driven by users with sportive or aggressive driving styles,
further increasing real-world fuel consumption.
In summary, increasing all-electric range decreases fuel consumption and leads
to increasing UFs. On the other hand, system power has a smaller but significantly
negative effect on fuel consumption and UF.

3.3. ANALYSIS OF INDIVIDUAL USER BEHAVIOR
Not only PHEV model-specific factors impact the fuel consumption of PHEVs, but also
external and user-specific factors. The present section analyzes the effects of annual
mileage and long-distance driving (section 3.3.1), the difference between private andcompany car users (section 3.3.2), the impact of charging on the UF (section 3.3.3),
and the impact of ambient temperature (section 3.3.4). The aim in each section is toquantify the effect of these factors on the UF and thus PHEV fuel consumption.

3.3.1. Impact of long-distance and annual driving
Frequent long-distance driving leads to lower UFs if other factors such as annual
mileage and charging overnight are kept constant as the battery will be depleted on
longer tours. Thus, frequent long-distance travel can have a noteworthy impact on
average UF. High annual mileage is correlated with more frequent long-distance travel.
The aim of the present section is to measure the effect of annual mileage on the mean
UF and mean fuel consumption as well to indicate typical frequencies for long-distancetravel in conventional car and PHEV usage.

Data and method
We use all PHEV data sources mentioned in section 2 that contain information on
annual mileage either on an individual-vehicle level or on a model-aggregated level.
This includes Spritmonitor.de; Voltstats.net; the UC Davis data; MyMPG; Xu, Hewu,
and Minggao (2016); Figenbaum and Kolbenstvedt (2016); CARB (2017); INL (2014);
Raghavan and Tal (2020); Smart, Bradley, and Salisbury (2014); and the German
company car data.
We compare average annual VKT of PHEVs in Germany and the United States with car
stock averages in these countries. For PHEVs in all countries, regression analysis on an
individual-vehicle level and model averages are used to measure the impact of annual
VKT on average UF and average fuel consumption.

Average annual mileage
Table 7 shows the average and standard deviation of annual mileage in kilometers
by country and brand in the sample. The uncertainty of the average is given by thestandard error, or the standard deviation divided by the square root of sample size.
Accordingly, the standard errors for most of the brands covered in the table are lessthan 1,000 km.
The average annual mileage in the German PHEV sample is 21,400 km, or about 46%
higher than the average mileage of about 14,700 km for the German vehicle stock (MiT,2017). When separating the PHEV sample by private and company-car ownership, it
can be seen that the annual mileage of company-car PHEVs at 30,200 km is similar
to the average for conventional company cars at 30,700 km. For private PHEVs, the20

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESannual mileage of 20,950 km is higher than the average for conventional private cars of
14,700 km (MiT 2017).19
For U.S. and Canada PHEVs, the mean is 22,000 km and thus only marginally above theoverall U.S. average of 21,700 km (FHWA, 2020). For Norway, the mean annual mileageof PHEVs is similar to the national car fleet average and between 12,000 km and 16,500
km per year (Feigenbaum & Kolbenstvedt, 2016).
Since PHEVs in Germany show higher annual mileage than the German average, they
are likely to show more frequent long-distance driving resulting in lower UFs than
expected from simulation of the German passenger car stock in section 3.3.3. The
high annual mileage of PHEVs in Germany can lead to lower UFs than expected fromthe NEDC and WLTP test cycles as the test cycles are designed to cover European car
fleet averages.
Table 7. Average and standard deviation annual kilometers traveled by PHEVs in Germany
and the United States by brand. Shown are average (mean) and standard deviation (SD) bycountry and PHEV brand with the number of PHEVs in the sample and the number of models
covered by brand.
Country

Brand

Sample# Models

Averageannual km

SD annual km

Germany

Audi

90

1

18,8007,800

Germany

BMW

227

5

20,4009,600

Germany

Chevrolet

5

1

19,5009,700

Germany

Hyundai

97

1

21,5009,200

Germany

Kia

144

3

19,6009,800

Germany

Mercedes107

10

27,80011,800Germany

Mini

40

1

19,1007,600

Germany

Mitsubishi

316

1

20,20010,300Germany

Opel

39

1

20,80010,800Germany

Porsche16

5

19,6005,900Germany

Toyota117

1

21,90010,300Germany

VW

229

2

20,80010,500Germany

Volvo

133

8

26,00011,500U.S./Canada

Chevrolet

10‘152

1

21,970

10,200

U.S.

Ford

31

1

24,200

11,200U.S.

Toyota16

1

23,70011,50011,75941

21,90010,300TOTAL

Effect of annual mileage on UFs
To demonstrate the effect of annual mileage on UFs, Figure 10 and Figure 11 show the
relation between UF and annual mileage as scatter plots for individual vehicles. We
generally observe a large range of UFs even for fixed annual mileage. This is mainly for
two reasons (Plötz et al., 2018b). First, the same annual mileage can be achieved withhighly different distributions of daily kilometers traveled. For example, driving almost
the same short distance every day or making only a few long-distance trips a year can
result in the same total. Second, the UF is influenced by user charging behavior.

19 Interestingly, the annual mileage of private and company-car PHEVs in our sample resembles the averages
for private diesel cars of 20,100 km and company diesels of 33,000 km. By comparison, mileage for private
gasoline vehicle is 12,400 km and for company gasoline cars, 24,400 km. Correcting for vehicle age does notchange the picture much as the average annual mileage for private vehicles up to four years old in Germany
is 15,600 km compared with 14,000 km for older vehicles (MiT 2017). Company cars up to four years old drive
about 32,000 km a year compared with 24,400 km for older company cars.

21

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESThe local mean for given annual mileage (solid line in Figure 10) shows the overall
trend of UFs with changing annual mileage. For the given vehicle, a Chevrolet Volt /Opel Ampera with about 80 km of NEDC range, the UF of PHEVs with 10,000 km ofannual mileage is about 80% and decreases by about 6 percentage points with every
10,000 km of annual mileage. This leads to an average UF of about 60% for PHEVs with
40,000 km of annual mileage and a UF of about 30% for 80,000 km of annual mileage.
Similar trends can be observed for other PHEVs, including the BMW 225xe, Audi
A3 e-tron, VW Golf GTE, Hyundai Ioniq PHEV, Kia Niro, Mitsubishi Outlander, VW
Passat GTE, and Toyota Prius PHEV. There is a large range of individual UFs forgiven annual mileage but a clear decrease in average UFs with increasing annual
mileage for all models.
100%
Chevrolet Voltn = 10,304

Utility factor (UF)

80%

60%

40%

20%

0%

0

20,00040,00060,00080,000100,000

Annual mileage [km]Figure 10. UF versus annual mileage for Chevrolet Volt PHEV. The individual UF is shown
against annual vehicle kilometers traveled for more than 10,000 individual Chevrolet Voltvehicles (small crosses) together with local mean values for given annual mileage (solid line)
including 95% confidence interval (shaded area).

22

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESBMW 225xe
100%

Audi A3 e−tronn = 11080%

VW Golf GTE

n = 90Hyundai Ioniq PHEV

n = 114n = 9760%

Utility factor (UF)

40%
20%
0%
Kia Niro PHEV
100%

Mitsubishi Outlander PHEV

n = 100

80%

VW Passat GTE

n = 316

Toyota Prius PHEV

n = 115n = 133

60%
40%
20%
0%

0

20,000 40,000 60,000

020,000 40,000 60,000

020,000 40,000 60,000

Annual mileage [km]Figure 11. UF versus annual mileage for different PHEVs. The individual UF20 is shown against
annual vehicle kilometers traveled for several individual vehicles (small crosses) together with local
mean values for given annual mileage (solid line) including 95% confidence interval (shaded area).
Table 8. Typical change in UF when changing mileage, range, or system power. Shown arechanges in percentage points when keeping all other variables at mean values. The ranges
include two standard errors. 21
Variable

Change in UF in percentage points+1,000 km annual mileage–0.55 to –0.71+10 km all-electric range+3 to +5

+10 kW system power–0.5 to –1.0

Regression analysis for individual vehicle data has been performed to calculate
the effect of annual mileage on UF. Table 8 shows the average change in UF whenchanging annual mileage while controlling for all-electric range, system power,user group, and country. We observe a clear decrease of the UF by about 0.6 of apercentage point with every 1,000 km above average. Accordingly, if the 21,000 km
annual mileage of private PHEVs in Germany were similar to the 14,700 km average
for all private passenger cars (MiT, 2017), the UF would be 3.5–4.5 percentage points
higher, leading to an average UF of around 47% instead of the current 43%.

Frequency of long-distance driving
Not only the average annual mileage but also the distribution of daily kilometers
traveled is important for the share of electric driving. This leads to the frequency oflong-distance travel and its role in annual distance traveled. Longitudinal travel datawith several days of observation is required to understand the impact of a few longdistance events on annual mileage. We analyze the Voltstats.net PHEV driving data anddriving data of the German Mobility Panel (MOP, 2010), for which data was collected

20 Some PHEVs show a UF of zero, which is due to the methodology used for the determination of UF (seesection 2.3). PHEVs having real-world fuel consumption greater than 150% of the NEDC charge-sustaining fuelconsumption are assumed to have a UF of zero.
21 The underlying regression models can be found in the appendix. We also controlled for user group and foundthat private users had 20–30 percentage points higher UFs than company cars. The effect for all-electricrange is the same as for the regression without annual mileage in section 3.2, but the effect of system power issomewhat smaller after controlling for different annual mileages (cf. section 3.2).

23

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES

020,000 40,000 60,000

over the duration of one week of conventional cars in Germany, including 5,812 privateand 212 company cars. The results are summarized in Table 9.
Table 9. Long-distance driving in different datasets. We used a subset of the Voltstats.net
data with at least 100 observation days and sequences of missing values shorter than two
weeks (N = 3,098) as well as the German Mobility Panel dataset (MOP 2010).

Average annual km

Voltstats.net(n = 3,098)

Germany
private cars
(n = 5,812)

Germany
company cars
(n = 212)

21,97012,10027,800Above 100 km daily driving
mean share of observation days

18%

7%

24%

mean share of driving days

20%

9%

28%

mean share of annual mileage

42%

19%

47%

Above 200 km daily driving
mean share of observation days

4%

2%

9%

mean share of driving days

5%

3%

11%

mean share of annual mileage

16%

8%

25%

According to the German Mobility Panel data, company cars show higher annual
mileage and more long-distance trips of more than 100 km of daily distance thanprivately owned vehicles. For the Voltstats.net sample, we find that long-distancetravel occurs on 18% of the observation days but accounts for 42% of annual distance.
The Voltstats.net sample seems to have many long-distance drivers, potentiallywith long commutes of more than 50 km in one direction. In the German data, longdistance driving occurs on 7% of the days observed for private and 24% for company
cars but accounts for 19% of annual mileage for private autos and 47% for company
cars. 22 Despite the difference in samples and user groups, a few long-distance drivingdays clearly account for a noteworthy share of annual mileage.
If future PHEVs had real-world ranges of 75 km, corresponding to WLTP range of 90
km 23 , the mean share of driving days with more than 75 km would be 15% for privateand 39% for company cars. This would correspond to 11% of observation days forprivate vehicles and 32% for company cars and to 28% of annual kilometers driven for
private autos and 60% for company cars.
The share of long-distance driving in annual mileage has implications for the share ofkilometers driven on electricity, the UF. When reflecting on their vehicle usage, many
car users do not think of the days with exceptional driving and perceive their vehicle
usage as mainly electric. However, for the UF, the share of total kilometers and not theshare of days traveled on electricity is decisive. If 25% of annual mileage is from longdistance driving with a high share of nonelectric kilometers, this reduces the total UF.
For example, we have 22,000 km of mean annual mileage for the PHEVs in our sample.
If 19% of these annual kilometers, or 4,200 km, come from 25 days, or 7% of the daysin a year, and the vehicle has a real-world range of 36 km, the vehicle would drive 890
km of the 4,200 km on electricity and 3,310 km on fuel. If the same auto ran entirely
22 The values for MOP are likely to be slightly too low as the survey week explicitly excludes nonusual behavior
like annual vacations and is supposed to represent a regular working week. Furthermore, long-distance trips
could also be underrepresented in the MOP data as people on long-distance trips are less likely to participate
in the survey. The mean annual kilometers for the privately owned vehicles has been extrapolated from one
week of observation and is slightly below the national German average of 14,700 km, indicating that longdistance trips are underrepresented in the sample. However, the shares are consistent with one-day crosssectional share of a second survey for Germany where 5% of vehicles had more than 100 km daily VKT on thesurvey date and 2% more than 200 km on the day (MiD, 2018). Yet, the share of long-distance days by usergroup cannot be reproduced as the MiD distinguishes only private and commercial owners.
23 Assuming 20% higher real-world electricity consumption similar to 20% real-world fuel consumption according
to Dornhoff, Tietge & Mock (2020).

24

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESon electricity the rest of the year, these 3,310 km of nonelectric driving alone would
reduce the UF to 85%. Assuming that the daily distances in the rest of the year are
equally distributed and driven as much on electricity as charging once a day allows, theUF would be about 60%. 24
In summary, PHEVs in Germany show a higher annual mileage than conventional
passenger cars, largely due to the above-average annual mileage of private PHEVs,
while in the United States, PHEVs show similar annual mileage as the overall average
car fleet. Every additional 1,000 km of annual mileage reduces the UF by 0.6 ofa percentage point if all other factors remain constant. Furthermore, a few longdistance driving days significantly lower the actual UF.

3.3.2. Impact of user group: Company versus private cars
The deviations between test-cycle and real-world driving data show noteworthy
differences between private vehicles and company cars. Private vehicles are owned
by private individuals. Company cars are “vehicles provided to employees for theemployee to use for business and personal travel,” and “the vehicles are consideredpart of employee compensation and are therefore part of their tax liability.” (Hardman
et al., 2017). Company cars play a large role in passenger car sales in Europe, mainly incountries with a domestic car industry.
PHEVs have received significant tax incentives in several markets, for example in
the Netherlands from 2012–2016 but reduced thereafter, as well as in Germany andSweden (EAFO, 2020). Accordingly, a notable share of PHEVs can be expected to
be used as company cars in Europe. For Germany, the share of commercially owned
PHEVs in the car stock is about 58% (cf. Figure 12), most of which can be assumed tobe company cars. 25
Commercial

100%

Share of car stock

80%

51%

Private

42%

81%

60%
94%
40%

49%

20%

58%

19%
0%

6%
Gasoline

DieselBEV

PHEV

Figure 12. Passenger car stock in Germany by fuel and owner group. Passenger car stock inGermany as of April 1, 2020. Source: Calculation based on KBA (2020b).

24 Of course, this calculation is overly optimistic as the vehicle will not be used every day of the year and will not
drive exactly the same distance on the short-distance driving days or long-distance driving days. Also, the car
could not drive the rest of the year on electricity with one full charge per day, as the remaining 340 days could
only lead to 12,240 km, which together with the long-distance mileage is still less than the assumed annual
mileage. Charging 1.5 times per day would be needed if every day of the year were driven.
25 Commercially owned vehicles are company cars and fleet vehicles. Fleet autos cannot be used for private
purposes and are not assigned to a person within a company. As PHEVs receive much stronger incentives
via income taxation than fleet vehicles, we can assume that most commercially owned PHEVs in stock arecompany cars.

25

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESIn sections 3.1 and 3.2 we analyzed private and company vehicles separately. The sales
data for Germany demonstrates that PHEVs are an important group in company car
stock and will diffuse into private stock soon as holding times are typically short forcompany cars (Plötz et al., 2014a).
We performed simple nonlinear regression to analyze typical values for UF as afunction of real-world range. 26 Figure 13 shows the actual UF in our sample together
with the regression results.
100%
90%

NEDC

Utility factor (UF)80%

Sample Size
1

ate

70%

priv

10

60%

100

50%
40%

pany

com

30%

car

20%

Usergroup
Company car
Private

10%
0%

0

10

20

30

40

50
60 70
80
NEDC range [km]

90

100

110

120

Figure 13. UF regression by user group in Germany. Subsample mean UF on PHEV model
variant level for Germany for private vehicles (green) and company cars (red) with the sample
size indicated by the circle size and regressions (dashed lines).

As discussed above, average UFs increase with all-electric range for private andcompany cars, but the mean values for company cars are much lower for a given range
than for private vehicles. In the following section 3.3.3, we compare simulated UFs ofpassenger cars in Germany for both user groups with these local regressions.
There is a third group of vehicle usage, fleet vehicles. These are commercially owned
vehicles that cannot be used for private purposes. Electric vehicles have potential as
fleet vehicles, such as in postal services, delivery, and other usage scenarios (Gnann,Plötz, Funke, & Wietschel, 2015; Figenbaum, 2018), but there is a lack of real-world
usage data for PHEVs in commercial fleets. For a small sample of 19 PHEVs in Germany
that have been tracked with data logging devices, 14 were used in city or commercial
fleets (NOW, 2020). The average UF was 65% at an NEDC range of 50–70 km anda mean annual mileage of 14,120 km. The vehicles showed two charging events on
average per day (NOW, 2020). Apart from this, there is little data on PHEV usage incommercial fleets, and further research is required.

3.3.3. Impact of charging behavior: Empirical data and simulation
The present section reviews studies on PHEV charging behavior based on the U.S.
Voltstats.net sample and compares the observed UFs for private and company car
PHEVs in Germany with simulated UFs from different charging assumptions.

26 Similar to the regression models presented above, we choose the following function form:
UF = 1 - exp(-AERNEDC/L) where AERNEDC is the NEDC all-electric range and L is a user group specific parameter.
For private vehicles we obtain L = 80 ± 5 km (best fit ± 2 standard errors) and for L = 330 ± 160 km company
cars, which is obtained from numerical minimization of the sum of squared deviations.

26

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESEmpirical PHEV charging data
Mandev, Plötz, and Sprei (2020a & b) analyzed charging behavior in the Voltstats.net
sample in great detail. The dataset comprises information from April 2011 to January
2020 with 4.3 million driving days. The authors estimate the frequency of additional
daytime charging and the frequency of no overnight charging by comparisonof simulated and observed UF for each day and user. 27 Results for the Chevrolet
Volt show that additional over-day recharging happens on 3%–8% of days andno recharging overnight happens less often, on 3%–6% of days. Additionally, fuelconsumption increases by 0.75 liter of gasoline with every 10% of driving days withoutcharging, adding to tailpipe emissions. Charging more than once per day decreases
fuel consumption by 0.1 liter for every 10% of driving days with additional charging(Mandev, Plötz, and Sprei, 2020a & b).
In a comprehensive study of driving and charging behavior of 166 PHEVs in the UnitedStates, Tal et al. (2020) observe a high average number of charging events per day:
0.99 for Toyota Prius PHEV, 1.11 for Ford CMAX Fusion, 1.02 for Chevrolet Volt with 16
kWh battery, and 0.77 or Chevrolet Volt with 18 kWh battery. In a subsequent study,
Chakraborty, Hardman, and Tal (2020) show that reasons for PHEVs in the UnitedStates not to charge are lack of home charging availability, high price for charging, lowelectric driving range, and low electric motor power.
Actual data from vehicle manufacturers on PHEV charging is rare. One analysis forGermany finds a median of 0.5 charging events per day for several thousand small,compact, and SUV vehicles mainly driven by private users (NPM, 2020). The vehicles inthe sample have NEDC ranges of 31–92 km and achieve median UFs between 24% and42%. The median number of charging events per PHEV model range from 1.2–2.2 per
100 km of driving. The average number of charging events is thus lower than the values
from the comparison of mean UFs and simulated UFs discussed below, but the mean
UFs are in a comparable range.

PHEV charging behavior from simulations
We use driving data of conventional vehicles to simulate the full charging frequency
and range-depended UFs of PHEVs. Therefore, we use the vehicle driving data fromthe German Mobility Panel (MOP, 2010). The data contains the distance and purposeof trips reported by individuals who have been assigned to vehicles in the individual’shouseholds (cf. Plötz et al., 2014a). We thus obtain distance per vehicle and trip,
including trip purposes and additional vehicle and household information for about
6,000 German vehicles mainly representative of German passenger car stock.
To simulate the UFs, we vary the number of full charging events per day and vehicle
and assume that the PHEVs are driven as much as possible on electricity according
to the all-electric range. We convert NEDC all-electric ranges to real-world ranges
as requried in the simulation. 28 The simulation is performed for all seven days ofobservation for each vehicle in the sample, and the total UF is calculated.
Figure 14 shows the simulated mean UF for one full charge per day for private vehicles
and company cars. The simulated UFs quickly increase with all-electric range and reach

27 The simulated UF is simply given by UFsim = AER/daily VKT if daily VKT > AER and 1 otherwise. Here, AER
denotes the EPA all-electric range. This is compared with the observed UF. The simulation implicitly assumes
a full recharge overnight, as charging is not specifically simulated. If the observed UF is much higher than
the simulated UF, the vehicle must have had at least one additional recharge during the day. Similarly, for theoccurrence of no overnight charging, the observed UF must be much smaller than the simulated UF. For theoccurrence of an additional over-day charging event, the authors use the assumption that the observed UF fora vehicle for that given day is at least 1.5 times higher than the simulated UF. For no overnight charging they
use the assumption that the observed UF is smaller than half the simulated UF. The frequency of additional
day charging is defined as the share of days with an over-day charging event within the total number ofdriving days for a given user. Similarly, the frequency of no overnight charging is defined as the share of days
with no overnight charging within the total number of driving days.
28 Real-world all-electric range is assumed to be 71% of the NEDC all-electric range.

27

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75% for 40 km real-world all electric range, or about 56 km NEDC range, for privately
owned vehicles and about 50% for company cars. The mean simulated UFs are always
smaller for company cars than for private vehicles due to more frequent long-distancedriving and higher daily and annual mileage for company cars.
Table 10 summarizes the simulation results for private and company cars in Germany
for one full charge per day and charging three out of four days as well as the meanvalues by range as observed in Germany (cf. section 3.1). The simulated UFs for private
and company cars are shown in Figure 14.
0100%

Real−world range [km]40

20
User group

Private60

80

Company car

90%

Simulated utility factor (UF)

80%
70%
60%
50%
40%
30%
20%
10%
0%

0

20

40

60
NEDC range [km]

80

100

120

Figure 14. Simulated UFs for private and company cars in Germany. The figure shows thesimulated UFs for Germany passenger cars based on the German Mobility Panel data. The
simulation requires real-world ranges (top x-axis) and the figure shows an additional x-axis with
NEDC ranges, calculated as +40% compared with real-world ranges.
Table 10. Difference in simulated UF between private and company cars. The simulated andobserved UFs for private vehicles and company cars in Germany are shown. Simulation withcharging every driving day and charging three out of four driving days.
Real-world range in simulation
Approximate NEDC range
NEDC UF (in %)

20 km

30 km

40 km

50 km

60 km

70 km

80 km

28 km

42 km

56 km

70 km

84 km

98 km

112 km

53

63

69

74

77

80

82

Simulation with one full charge per driving day
Simulated UF private (in %)

54

67

74

80

84

87

89

Simulated UF company cars (in %)

32

42

50

57

62

67

71

Difference in percentage points

22

25

24

23

22

20

18

Simulation with one full charge in three out of four driving days
Simulated UF private (in %)

41

50

56

60

63

64

67

Simulated UF company cars (in %)

24

32

38

44

45

52

55

Difference in percentage points

17

18

18

16

18

12

12

Observed UF for Germany (Sample-size weighted regression ± 2 standard errors)
Observed UF private (in %)

30±2

41±2

50±3

58±3

65±3

71±3

75±3

Observed UF company cars (in %)

8±4

12±7

16±9

19±10

22±11

26±13

29±14

28

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESCompany car

100%

PrivateSimulated utility factor (UF)

90%

100%

80%

ys

f da

%o

70%

s

of day100

60%

f days

75% o

ys

f da

o
75%

50%
40%

f days

50% o

days
50% of

30%
20%
10%
0%

0

20

40

60

80

100

120

0

20

40

60

80

100

120

Real−world range [km]Figure 15. Effect of charging frequency on simulated UF. Shown are the simulated mean UFs
for German driving data (n = 6,309, German Mobility Panel data MOP, 2010) of one full charge
per day (solid line), in three out of four days (dashed line) and in one out of two days (dottedline) together with ranges of plausible mean UFs from the empirical data (shaded gray areas –cf. Figure 13, with real-world range = NEDC range / 1.4).

The simulated UFs with one full charge per day are close to the NEDC UFs (seeFigure 4), if corrected for more realistic ranges. That is, 40 km real-world range
PHEV simulated private UF corresponds well to 56 km NEDC range UF. However,the simulation shows that the long-distance driving of company cars leads to 18–25
percentage points lower UFs than for private vehicles if a full recharge every night isassumed for both user groups.
Charging frequency has a clear effect on the simulated UFs in both user groups. 29
Charging only three out four days on average leads to 13–23 percentage points lower
UFs for private and 8–17 percentage points lower UFs for company cars in comparison
with charging every day. This corresponds to simply 25% lower UFs as a full recharge
takes place during only 75% of the days.
But how do the simulated UFs compare with the actual UFs observed for PHEVs in
Germany? For private vehicles, the simulated UFs with charging during three out offour driving days are greater than the observed values for short ranges but larger forlong ranges. Taking into account that the high annual mileages in the actual PHEV
usage data as compared with the car fleet average annual driving in the simulation,
the simulated UFs should be about 6 percentage points lower to correct for the highannual driving in the actual PHEV sample. After that, the simulated UFs for charging on
three out of four days matches the actual UFs with an accuracy of 1 percentage point
for all ranges under consideration. Thus, our best estimate for the typical charging
frequency is about three out of four nights.
For company cars, actual UFs in Germany in our sample are below even the assumptionof charging three out of four days. The mean annual mileage of company car PHEVs
in the sample is 30,200 km, which is close to average of conventional company cars
(30,700 km acc. to. MiT (2017)) and about 2,000 km more than in the company cars
sample used for the simulation. Accordingly, the simulation overestimates company
car UFs by only about ca. 1 percentage point. After subtracting this, the actual UFs

29 The mobility panel data contains seven days of observation for each vehicle in the sample. But as seven is notdivisible by four, we treated charging as a binomial random variable in the simulation with success probabilityof 0.75 to decide for every day of every user for a full recharge with 75% probability.

29

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES

are still not close to the values simulated from charging three out of four driving days.
Accordingly, the actual charging of company cars in Germany is more likely to be about
once every second driving day.
The real-world UFs of private and company car PHEVs in Germany are clearly
below the UFs simulated for one full charge per driving day. They rather indicate acharging frequency of three out of four driving days for private and one out of two
driving days for company car PHEVs.

PHEV Charging Locations
Yet, the questions remains how much public charging infrastructure can support
electric driving of PHEVs. This leads to the question how much electricity is generally
charged in public places. The European Federation for Transport and Environment(T&E) assumes about 25% of public charging for PHEVs based on a consultationof experts in a scenario for the near future (T&E, 2020). In T&E (2018), however, anaverage share of 5% of public charging events are mentioned. In a survey of several
thousand California drivers who were asked to recall plug-in charging events in theprevious seven days, Tal et al. (2018) find PHEVs have 6%–11% of their charging eventsat publicly accessible installations. As the availability of public charging is likely toincrease in the future and with more PHEV users who do not have a home charging
option, the share of public charging could increase, but not quickly. Less than 20% of
public charging seems a plausible assumption.
For Germany, Scherrer, Burghard, Wietschel, and Dütschke (2019) published resultsfrom a survey in 2019 among 432 users in Germany of plug-in electric vehicles (PEVs),
including BEVs and PHEVs (cf. Figure 16). They found that 18% of all PEV charging
events used public facilities, but the rate was only 13% for PHEVs. As expected,
charging at work is much more important for company cars users, accounting for 60%of their charging events compared with 26% for all users.
More and easier access to public charging infrastructure is sometimes discussed as ameasure to increase the electric driving share for PHEVs. The distribution of current
charging locations, though, shows that for today’s users, home and work are thedominant charging locations (Scherrer et al., 2019). Public charging accounts for only
a minor share of charging, and even if public charging availability could be increased
through policies, the effect on mean UF can be only limited except for users without
access to home or work charging.

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ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES

at home
100%

1%

6%

100%

12%
22%

Share of charging events

Share of charging events

12%
80%
26%
60%

40%
65%

55%
20%

0%

All PEV

7%

12%

13%

60%

14%

32%

63%

55%

47%

20%

28%

Total

PrivateFullsamplePEV users intier 1 & 2 cities30

PEV users intier 3+ cities

House

19%

7%

33%

Building with up to six floors

29%

28%

28%

Building with more than six floors

37%

60%

36%

Dormitory

16%

5%

3%

Available

37%

72%

76%

Unavailable

51%

26%

19%

Not sure

12%

2%

5%

Available

30%

79%

50%

Unavailable

53%

14%

42%

Not sure

17%

7%

8%

3.3.4. Impact of ambient temperature
PHEVs and other plug-in vehicles have shorter ranges in cold weather as additionalenergy is required for heating the vehicle cabin, and the battery is less active. The
present section measures the effect of outside temperature on mean UFs of PHEVs.
The first part of the present section analyzes empirical daily driving data from30 Beijing, Shanghai, Guangzhou, Shenzhen, Chengdu, Hangzhou, Wuhan, Chongqing, Nanjing, Tianjin, Suzhou,
Xi’an, Changsha, Shenyang, Qingdao, Zhengzhou, Dalian, Dongguan, Ningbo, Xiamen, Fuzhou, Wuxi, Hefei,
Kunming, Harbin, Jinan, Foshan, Changchun, Wenzhou, Shijiazhuang, Nanning, Changzhou, Quanzhou,
Nanchang, Guiyang, Taiyuan , Yantai, Jiaxing, Nantong, Jinhua, Zhuhai, Huizhou, Xuzhou, Haikou, Ürümqi,Shaoxing, Zhongshan, Taizhou, Lanzhou.

31

8%

40%

Table 11. Housing, parking, and home charging in China. Results from a survey with 1,023
respondents including 139 PEV users. Source: Li, Plötz, & Zhang (2020)

Charging
near
home

4%
8%

60%

For China, charging in public locations is probably more important than for most partsof Europe and North America. In a recent survey of 1,023 participants including 129
PEV users (cf. Table 11), only about a third of respondents had access to home charging
and a quarter of PEV users did not have home charging (Li, Plötz, & Zhang, 2020).
There are differences between major cities (tier 1 and tier 2 cities) and smaller (tier 3
and higher) cities in China, but most passenger car PHEVs are owned in the large cities.

Charging
at homepublic fast

17%

26%

Figure 16. Share of public BEV and PHEV charging in Germany. Left: share of charging eventsby charging location and PEV type. Right: Share of charging events by location and vehicle
usage for all plug-in vehicles (private usage, company usage and mixed private/company use).
Source: Calculation based on Scherrer, Burghard, Wietschel, and Dütschke (2019)

Housing
type

public slow

80%

0%

PHEV

6%

at work

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES

Company

MixedChevrolet Volt vehicles in North America, and the second part looks at real-world
driving in Norway.

Empirical UF data for Chevrolet Volt vehicles
We use the highly detailed daily Voltstats.net data to look into the effect of ambient
temperature. We take a subsample of 1,738 Chevrolet Volts from the United States.
Each user has a personal website including the home location of the user. For eachday and user, we compare the observed UF to a simple simulation of UF. 31 We retrieve
the daily mean ambient temperatures for all locations and all driving days via web API
for US national climate data. 32 This information was available for 76,867 driving days.
We compare the difference between observed and simulated UF with the ambient
temperature in Figure 17. The figure shows the mean difference between observed
and simulated UF for different ambient temperatures, rounded to single digits indegrees Celsius.
The figure shows a clear effect of ambient temperature on UF. The UF is reduced
by about 1 percentage point per degree Celsius below 10°C. The UF reduction is
nonlinear and strongest for temperatures below 0°C, where additional cabin heating
demand and the effects on the battery are greatest.

Change in UF [percentage points]

20

10

0

−10

−20

−30

−40

−50

−20

0

20

40

Average temperature [°C]
Figure 17. Effect of ambient temperature on Chevrolet Volt UF. We compare the actual daily
UF with the simulated UF for 77,000 driving days of 1,738 Chevrolet Volt vehicles (cf. Plötz etal., 2018a). The average difference between observed and simulated UF in percentage points isshown as filled circles ± one standard error (i.e. SE=SD/√n) with a local scatter plot smoother(solid line).

PHEV usage in cold weather: The Norwegian caseA second example of real-world experience in cold weather is from Norway. Figenbaumand Kolbenstvedt (2016) compare mean UFs during summer and winter in a survey of
1,515 PHEVs in Norway. We reproduce their results below in Table 12.

31 UF is simulated as UF = all-electric range / daily km if daily km>all-electric range and equal to one otherwise.
32 Daily climate data from https://www.ncei.noaa.gov/. Original sample reduced to 31,193 since cases with perfectmatch between observed and simulated were excluded.

32

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESTable 12. Mean UF in summer and winter in Norway for different PHEV models. Summer and winter range in kilometers,
difference between UF in percentage points (pp). Source: Figenbaum and Kolbenstvedt (2016).
n

NEDC
range

Summerrange

Winter
range

Mean
UF

SummerUF

WinterUF

Difference

Audi A3

197

50 km

40 km

29 km

59%

64%

54%

10 pp

Opel Ampera

46

83 km

68 km

44 km

72%

77%

68%

9 pp

Mercedes C350e

11

31 km

23 km

18 km

41%

44%

38%

6 pp

VW Golf GTE

283

50 km

40 km

28 km

57%

62%

52%

10 pp

Mitsubishi Outlander

806

52 km

41 km

30 km

55%

62%

48%

14 pp

Toyota Prius PHEV

67

25 km

20 km

15 km

38%

43%

33%

10 pp

Volvo V60

104

50 km

46 km

37 km

51%

55%

48%

7 pp

Total

1,515

51 km

41 km

29 km

55%

61%

49%

12 pp

PHEV model

The Volvo data is consistent with other data sources. For example, data from theon-board computer in the Volvo V60 PHEV estimates a UF of 46.3% based on 341
Norwegian vehicles (Figenbaum & Kolbenstvedt, 2016). This is very close to theaverage for the German Volvo V60 D6 Hybrid from the Spritmonitor.de data with 42.4%
but above the 35% for the Volvo V60 T8 Twin Engine PHEV and 31% for the Volvo V60
T6 Twin Engine PHEV in the Spritmonitor.de data.
The Norwegian data shows the clear impact of ambient temperature on the all-electricrange of PHEVs. The summer range is typically 20% below the NEDC range, but the
winter range is more than 40% below the NEDC range, or 57% of NEDC range on
average. The actual mean UF varies significantly between summer and winter. The
mean UF is between 6 and 14 percentage points lower in winter, with a sampleweighted mean of 12 percentage points. This is consistent with the impact of ambient
temperature on UF of the Chevrolet Volt vehicles.
The impact of ambient temperature was also confirmed by laboratory measurements.
A short summary of the findings is given in Appendix A: “The impact of ambient
temperature on PHEV fuel consumption: A case study.”

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4. DISCUSSION
The observed large deviation between real-world and type-approval test-cycle fuelconsumption for PHEVs results from a 50% lower UF than expected based on test cycle
results (cf. section 3.1.2) and higher fuel consumption in charge-sustaining mode. For
combustion engine vehicles, there is an average gap of about 40% between NEDC
and real-world fuel consumption (Dornoff et al., 2020). If we apply the same 40% toconventional fuel driving of an average PHEV, it would consume 1.4 times as much as
expected in charge-sustaining mode operation. Taken together, the combustion engine
is used about twice as often as assumed in the NEDC or WLTP cycles and consumes
40% more fuel. This leads to 2.8 or almost three times higher fuel consumption andwould explain the observation of a real-world fuel consumption of two to four times
higher than in the NEDC or the WLTP.
The low UF and high fuel consumption has direct consequences for real-world CO2
savings from PHEVs. We observe 18%–54% mean UFs across user groups and countries.
Let us assume the fuel consumption of PHEVs at 0% UF is comparable to the fuelconsumption of a similar combustion engine vehicle. According to such an assumption,
the UF would directly correspond to 18%–54% fewer tailpipe CO2 emissions. A different
estimate of the actual fuel savings compared to conventional vehicles uses the mean
NEDC UF in our sample of 69%. With the assumption that the charge-sustaining
mode fuel consumption of PHEVs is the same as the fuel consumption of comparable
conventional cars, the NEDC fuel consumption of PHEV is thus equivalent to 31% of the
NEDC value of conventional cars. As mean fuel consumption of PHEVs is actually 2–4
times higher than their test-cycle values, the real-world fuel consumption of PHEVs isabout 60%–120% of the NEDC values of comparable conventional car models. Whentaking into account that the average deviation between real-world and NEDC fuelconsumption for conventional vehicles is about 40% (Dornoff et al., 2020), the fuelconsumption of PHEVs in real-world operation is 43%–86% of the fuel consumptionof comparable conventional vehicles. In summary, both calculations arrive at roughly
15%–55% savings in tailpipe emissions of PHEVs in real-world operation compared tosimilar-sized combustion-engine vehicles.
There are three main reasons why the observed real-world UFs are significantly lower
than expected based on NEDC type approval. First, PHEVs are charged much lessoften than the daily full charge assumed in the NEDC procedure. In Germany, privatecar PHEVs are found to be charged on about three out of four driving days while
company-car PHEVs are charged only every second driving day. Second, the actual
all-electric range is much shorter in real-world driving than according to the NEDC.
That is because real-world electricity consumption is likely to be higher than NEDC
electricity consumption, similar to the deviation between test approval and real-worldenergy consumption in combustion-engine vehicles (Dornoff, Tietge, and Mock, 2020).
Third, current PHEVs in use have high annual mileage. Although the annual mileagein the U.S. PHEV sample is similar to the U.S. fleet average, it is clearly above averagein Germany (see section 3.3.1 above). For Germany, annual PHEV mileage is 7,000 km
above the fleet average, reducing UFs by 3.5–4.5 percentage points compared withwhat would be expected based on fleet average (see chapter 3.3.1 for details).
Our sample includes data from five countries and two user groups covering a
total of more than 100,000 PHEVs, including literature values as well as recent
PHEV measurements. We lack data on other important PHEV markets such as theUnited Kingdom and Sweden. However, as framework conditions in these countries
are comparable to those of other Western European countries with respect tothe availability of home charging, typical driving distance, income, and financial
incentives for company cars, no fundamentally different PHEV usage can be
expected from these countries. Preliminary analysis of U.K. data shows a similar

34

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESpattern. 33 PHEV usage might be notably different in Japan or Korea, but no data wasavailable for those countries.
Our country samples show different distributions of car brands and models. The U.S.
sample, for example is dominated by the Chevrolet Volt, while the Chinese sample
is dominated by various domestic car brands. The Dutch, Norwegian and German
samples, on the other hand, show quite a broad distribution of brands and models.
Consequently, model-or model variant-specific analyses among different countries
would have been interesting. Sample sizes for single model variants, however, were
mostly too small for comparisons.
Company-car data was available only for Germany and the Netherlands, with a small
German company-car sample. Yet, the overall trends are the same for both countries,
and the qualitative differences among private and company cars in charging behavior
in particular can be expected from the existing financial framework conditions in bothcountries for PHEVs.
For most online data sources, including Spritmonitor.de, MyMPG and Xiao Xiong You
Hao, registration and monitoring of fuel consumption is voluntary. It can be assumed
that mainly those PHEV users who are sensitive to fuel economy register on theseplatforms. Thus, a certain self-selection bias overstating fuel economy could bepresent. Furthermore, on the Spritmonitor.de website most of vehicle specifications
are provided in free-text boxes and are not selected from a pre-defined list, leading toinaccuracies and thus difficulties in assigning correct vehicle characteristics from the
PHEV list. The rigid data cleaning process, however, assured a high level of accuracy.
Additionally, there are no required fields for data entries on Spritmonitor.de, whichmight lead in some cases to incomplete entries.
A noteworthy gap in the data, which warrants further research, are fleet vehicles. No
data source could be found that captures PHEV usage in purely commercial use.

33 We collected a small sample of fuel consumption values from PHEV user reports on carbuyer.co.uk, most
likely private vehicles. For 33 Mitsubishi Outlander PHEVs, the mean real-world fuel consumption was
4.8 ± 0.8 l/100 km (mean ± two standard errors) or 2.2–3.1 times higher than the NEDC value. For seven
Volkswagen Golf GTEs, the mean fuel consumption was 4.0 ± 1.0 l/100 km or 1.9–3.2 times higher than
NEDC, and for three Volvo XC90 T8 Twin Engine hybrids it was 7.9 ± 2.3 l/100 km or 2.7–4.9 times higher
than the NEDC. The mean annual driving distances were 22,000 km for the Outlander, 25,000 km for the
Golf GTE, and 19,000 km for the Volvo. All these numbers are consistent with the findings for Germany.

35

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5. POLICY RECOMMENDATIONS
Governmental support for PHEVs and their accounting in the EU CO2 targets
should better reflect their environmental benefit.
With two to four times higher fuel consumption than indicated by the test-cycle
values, the CO2 and pollutant emission benefits of PHEVs are lower than expected.
The inclusion of PHEVs in zero-emission vehicle (ZEV) mandates, such as inseveral U.S. states or the New-Energy Vehicle (NEV) credit system in China, as
well their contribution to reaching manufacturers’ CO2 fleet targets should be
carefully evaluated. Monitoring of real-world CO2 emissions in the EU, making use of
mandatory on-board fuel consumption meters, will shed further light on real-world
PHEV usage in coming years.
We find similar deviations of real-world fuel consumption and UFs for both the NEDC
and WLTP test cycles, although our WLTP sample is limited. Thus, further refinementsof the test cycles should be discussed. The EPA test cycle seems to represent realworld fuel consumption and all-electric range better than the NEDC or the WLTP.

Incentives need to set minimal electric ranges and favor longer ranges.
Government incentives should encourage long-range PHEVs, making purchase
incentives, company car taxation, and special depreciation rules range-dependent,
including sufficiently high minimal ranges to obtain incentives. Design options forsuch policies can also include low conventional fuel use, such as is already the casein China’s NEV credit policy (Cui, 2018). Longer ranges increase the proportion ofelectrified kilometers driven. However, they also require higher battery capacity andan increase in weight and carbon footprint of PHEVs. Consequently, maximum ranges
could be specified as well to prevent making PHEVs the heavier alternative to purely
battery-electric vehicles because of the dual drivetrains.

Access to charging points should be improved.
PHEV users should be offered comfortable and affordable home, workplace, and publiccharging options.

» Because most charging events correspond to home charging, the legal and financial
barriers to the installation of home charging points should be reduced, especiallyin multi-family houses. Purchase incentives could be combined with the freeinstallation of a home charging outlet or with charging cards or charging vouchers
for users without easy access to home charging. Charging cards and vouchers
would increase the social justice of purchase incentives for PHEVs. For company
cars, PHEV incentives might further be conditioned on providing a home charging
option to be installed either by the company or by the user. Because liability issues
for home chargers installed by an employer are difficult to handle and proceduresfor employees leaving a company are unclear, the user option would be preferred.
PHEV incentives might also be handed out as public charging vouchers, especially ifhome charging is not feasible or available.

» The access to charging points at the workplace, the second-most importantcharging location, could be increased by issuing incentives for PHEV company cars
only to companies that provide sufficient workplace charging options. Installing
workplace charging infrastructure on preferred parking spots close to a company’s
facilities would increase the attractiveness of workplace charging even more.

» Public charging makes up less than 20% of charging events for electric vehicles ingeneral. PHEV users do not require intermediate charging to reach their destination
as they can drive on conventional fuel. They cannot be expected to make additional
stops that would reduce the convenience of PHEVs. Accordingly, a significant
increase in public charging infrastructure could lead to higher UFs, but the effect

36

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESis most likely to be very limited compared with more frequent home charging.
At the same time, access to public charging infrastructure is necessary for PHEV
users without home or workplace charging. Nondiscriminatory access and a single
national charging card could make public charging easier and could expand electric
driving of PHEVs. The installation of public or semi-public charging infrastructure
could be incentivized for power providers. Registration authorities could provide
anonymized heat maps indicating where many PHEV users live.
In terms of upstream CO2 emissions from charged electricity, participation in load
management could increase the share of renewables charged and improve the lifecycle emissions of PHEVs.

Frequent charging should be incentivized.
To ensure high proportions of electric driving in the long term, purchase or tax
incentives for private as well as company car users could be linked to achieving asufficiently high electric driving share. For example, the tax benefit of PHEVs could
depend on reaching a significant share of the test-cycle UF in operation. 34
For company cars, charging at home and work either should be free from taxation on
fringe benefits and completely paid by the employer. Fuel cards for company-car users
could be limited to a number of liters per year, and PHEV company-car users could be
provided similar cards for free charging. Likewise, tax deductibility of conventional fueluse by companies could be reduced to stimulate demand-pull in fleet electrification.
It should be emphasized that companies hold company cars in their possession only
between one and four years, and the cars then diffuse into private ownership. A
high share of PHEVs as company cars is thus an important enabler for private PHEV
ownership. Consequently, for short-term effective impact, policy measures should focus
on company cars, also due to the high share of PHEV stock attributed to companies.
In addition, government incentives should be related to annual electric VKT. With
increasing mileage, long-distance travel rises, reducing UFs and expanding fuelconsumption. Denying incentives to these intense vehicle users, however, would waive
a high potential for electrified kilometers driven.

Conventional-fuel driving should be made less attractive.
Urban access regulations could limit driving on conventional fuel in cities and thusincrease electric driving of PHEVs. Yet such rules could be difficult to implement, andcompliance checks would be laborious. Technical issues such as ensuring a sufficientbattery charge are unresolved. Also, such a measure might lead users to charge thebattery via the combustion engine while driving before entering a city to ensuresufficient battery energy. Conventional driving could also be reduced by limiting thepower of the combustion engine, such as by requiring the electric power of PHEVs toalways be greater than the ICE power.

Car buyers need to be given realistic electric range and fuel consumption values.
The discrepancy between test-cycle and actual ranges needs to be reduced by using
real-world electric-energy consumption values. Car buyers and fleet managers needrealistic ranges to choose the right car. Vehicle dealers need to provide useful andrealistic information to customers. This could include stating fuel consumption for driving
purely on conventional fuel, which gives car buyers a more realistic view of PHEVs.
Training programs for vehicle dealers and fleet managers could prove useful, since it is ofhigh importance to select users with an appropriate driving profile for PHEVs.

34 Such a policy could be implemented via existing income tax. The owners declare their income tax and state
their UF if they apply for PHEV tax discounts. The financial offices could accept the statement as is or ask for
proof from a local vehicle repair shop that reads out the on-board diagnostics.

37

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ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESAPPENDIX A: CASE STUDY
THE IMPACT OF AMBIENT TEMPERATURE ON PHEV FUELCONSUMPTION: A CASE STUDY
A contribution by Christian Weber and Erik Figenbaum
Institute of Transport Economics (TØI), Gaustadalléen 21, 0349 Oslo, Norway
Fuel and electricity consumption of both conventional and electric vehicles depend,among other factors, on ambient temperatures and preconditioning of the vehicle. The
ambient temperature affects the operation of the combustion engine and the battery
and may require the use of auxiliaries such as heating and AC in the vehicle cabin.
Preconditioning of the vehicle cabin can mitigate these impacts to a certain extent.
Norway is an interesting test case to investigate the effect of ambient temperature on
PHEVs both due to its low winter temperatures and because it is one of the world’s
leading PHEV markets. The Institute of Transport Economics (Transportøkonomisk
institutt, TØI)—an independent institution for multidisciplinary transport research – hasconducted comprehensive laboratory measurements of selected vehicles to quantify
the impact of ambient temperature and preconditioning on fuel consumption of PHEVs.
The measurements were financed by the Norwegian public roads administration andwere conducted at the VTT’s emission measurement laboratory in Helsinki, Finland.
Three PHEV models with high sales shares in Europe were tested in different ambientconditions and driving cycles (NEDC, Artemis urban, and Helsinki City). All vehicles
where type-approved to NEDC. Although driving cycles do not fully capture real-world
usage, they provide useful insights into the order of magnitude of possible effects. See
Table A1 for key characteristics of the driving cycles.
Table A1. Key parameters of the driving cycles
Cycle

Focus

Distance (m)Duration (s)Averagespeed (km/h)

Maximum
speed (km/h)

Ratio of idling
(%)

NEDC

type approval

10,931

1,180

33

120

23

Helsinkirealistic citydriving

7,807

1,380

20

61

30

Artemis Urban

realistic citydriving

4,470

920

18

58

3

The NEDC is known to correspond to lower fuel consumption and CO2 emissions thanreal-world driving. In the following, we compare the CO2 emissions reported for eachvehicle in NEDC with the tailpipe CO2 emissions measured in the other laboratory tests.
The NEDC CO2 emission value is calculated from the test results for charge-sustaining
and charge-depleting test runs weighted by an electric range-dependent utility factor(EC, 2017). Here, however, we present the results of the individual measurements(CO2lab) of charge-depleting and charge-sustaining runs, compared to the weighted
NEDC value (CO2TA): Deviation CO2 = CO2lab / CO2TA .
Figure A1 shows the distribution of CO2 emissions in different testing conditions andtest cycles in relation to the NEDC value, with a value of one, when the actual fuelconsumption equals the reported fuel consumption. The x-axis shows the battery
state of charge (SoC) at beginning of the test. The data points have an offset in
x-direction in order to indicate the driving cycle: Artemis urban (AU) is shifted to theleft, NEDC is “on the line,” and the Helsinki City cycle (HEL) is shifted to the right(only 2 data points for the PHEVs, at “as is”). The color of the symbols indicatesthe ambient temperature: Blue is a test run at -7°C, and red at +23°C. The symbols
themselves indicate the driving mode of the vehicle: Circle markers for charge
depleting (EV) mode, triangle for charge-sustaining mode, and crosses for hybrid

41

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESmode. For comparison, diamond markers show the deviation for a diesel ICEV of thesame model class. If the vehicle was started with a cold engine, with the engine off
for more than three hours or overnight, the data point has an additional black circle.
All other data points are “warm starts” of the engine in which the test was driven
directly after the previous run. It has to be noted that several data points in the same
category, for example the same drive cycle, ambient temperature and driving modeindicate the results of the different vehicles.

HEL

NEDC

-7 °C ICEV
23 °C ICEV
AU

HEL

NEDC

-7 °C hybrid
23 °C hybrid
AU

HEL

NEDC

-7 °C charge sustaining
23 °C charge sustaining
AU

HEL

NEDC

10

AU

-7 °C EV
23 °C EV98
Deviation CO2

7
6
5
4
3
2
1
0

Empty

as is

Full

DieselICEV

SOC
Figure A1. Effect of ambient temperature on the CO2 emissions of PHEVs in different testing
conditions and drive cycles. Shown is the deviation of CO2 emissions from the NEDC value.

The laboratory measurements confirm the findings of the field data presented earlier
in this study: For the diesel ICEV, the deviation factor is clustered around 2 for all start
conditions and drive cycles. For low ambient temperatures, we see a tendency tohigher deviation.
For the PHEVs, the data shows a wider range in the deviation factor. In somecases, mainly with full battery, the deviation factor is less than 1, meaning that themeasured CO2 emissions in the laboratory test are lower than the value reported
from type approval. For the majority of runs, however, the deviation is higher, ranging
from around 2 up to a factor of 8. Note that even in EV mode (circle markers), thecombustion engine was running in some cases.
The highest deviation occurs in low ambient temperatures with an empty battery—theresults clearly demonstrate the influence of ambient temperatures and the needto frequently charge them to achieve low CO2 emission in real-world conditions.
In real life, users would use a mix of drive modes and carry out much of the daily
driving starting with a full battery, and driving would occur under variable ambient
temperatures and conditions and over variable distances. The average UF for the
PHEV fleet in Norway was assessed in table 12, based on data from a user survey, tobe 61% in summer and 49% in winter. The CO2 reduction will be lower than the UF as
the laboratory tests show that the engine was running in some cases even in EV mode,
especially under cold ambient conditions.

42

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESAPPENDIX B: DATA AND SUPPLEMENTAL ANALYSIS
FULL LIST OF FUEL CONSUMPTION VALUES
Table A2. Mean fuel consumption and UF by PHEV model, country, and user group. Fuel consumption (FC) in l/100 km, system
power in kW, range is NEDC range in km, sample size n, sources/variants is the number of sources or model variants included
Country

Model

User
group

n

Ø FC

NEDC FC

Ø UF

System
power

Range

Sources/
Variants

CN

Audi A6 40 e-tron

private

8

6.09

2.30

41%

180

50

1

CN

BMW 530Le

private

51

5.45

1.66

38%

185

63

5

CN

BMW X1 xDrive25Le

private

29

5.38

1.80

49%

170

77

3

CN

BYD F3 DM

private

9

7.10

2.70

48%

100

60

1

CN

BYD Qinprivate

50

4.29

1.60

53%

217

70

1

CN

BYD Qin 1.5T

private

504

7.01

1.60

23%

220

70

4

CN

BYD Qin 1.5T Plus

private

209

6.70

1.60

27%

223

70

1

CN

BYD Qin New Energy 1.5T

private

279

6.22

1.40

29%

223

80

1

CN

BYD Qin New Energy 1.5T 101

private

47

6.36

1.20

29%

223

100

1

CN

BYD Qin PHEV 1.5T

private

61

6.15

1.40

30%

223

80

1

CN

BYD Qin Pro DM 1.5TI

private

227

6.79

1.20

0%

225.5

68

4

CN

BYD Song DM 1.5TID

private

133

7.04

1.40

20%

333

80

1

CN

BYD Song MAX DM 1.5T

private

6

7.15

1.20

6%

228

81

1

CN

BYD Song Pro DM 1.5T

private

69

7.40

1.35

14%

293

81

2

CN

BYD Tang 2.0 TAWD 2015private

69

9.81

2.00

22%

371

80

1

CN

BYD Tang 2.0T

private

553

9.07

2.13

27%

371

73

3

CN

BYD Tang 2.0T 100

private

33

8.51

1.80

37%

371

100

1

CN

BYD Tang DM 2.0T

private

573

9.08

1.72

22%

413

89

5

CN

Changan CS75 1.5T PHEV

private

79

6.38

1.60

22%

265

60

1

CN

Changan Eado 1.0T PHEV

private

6

7.10

1.60

13%

346

60

1

CN

Dongfeng Scenery 580 1.8L PHEV

private

5

7.38

1.40

8%

166

70

1

CN

Ford Mondeo 2.0 PHEV

private

54

5.18

2.00

44%

197

52

1

CN

GAC Qizhi PHEV 1.5L

private

61

6.28

1.70

27%

201

59

2

CN

GAIC GA5 Elite 2015private

10

6.24

2.40

42%

106

50

1

CN

GAIC GA5 Exclusive 2015private

14

5.01

2.40

54%

106

50

1

CN

Geely Binyue 260T DCT

private

9

6.46

1.40

12%

190

62

1

CN

Geely Binyue 260T DCT Battle

private

5

6.26

1.40

14%

190

62

1

CN

Geely Borui GE 1.5T PHEV

private

550

5.80

1.57

27%

192

60

3

CN

Geely Emgrand GL 1.5L PHEV

private

67

7.39

1.50

5%

190

61

1

CN

Geely Emgrand GL 1.5T DCT

private

10

6.45

1.40

16%

190

66

1

CN

Geely Jiaji 1.5TD PHEV

private

70

6.68

1.60

14%

190

56

1

CN

Geely Xingyue New Energy 400T

private

32

6.11

1.40

20%

320

68

2

CN

Hyundai Sonata 2.0 PHE

private

5

5.03

1.30

36%

165

75

1

CN

Hyundai Sonata 2.0 PHS

private

6

5.58

1.30

28%

165

75

1

CN

Kia K5 2.0L

private

72

5.09

1.30

35%

165

75

1

CN

Lynk 01 1.5T PHEV

private

6

5.86

1.70

24%

192

51

1

CN

Lynk 01 1.5T PHEV

private

23

6.54

1.70

16%

192

51

1

CN

Lynk 01 1.5T PHEV Pro

private

173

6.50

1.70

16%

192

51

2

CN

MG 6 45T E-Drive

private

47

5.70

1.50

19%

167.7

53

1

CN

MG 6 45T E-Drive Pilot

private

46

5.11

1.50

27%

167.7

53

1

CN

MG 6 50T Pro

private

7

6.01

1.50

12%

224

51

1

CN

MG 6 50T Trophy 5

private

10

6.03

1.50

12%

224

51

1

CN

Porsche Cayenne S E-Hybrid 3.0T

private

6

12.37

2.90

0%

333

36

1

CN

Roewe 550 Plug-in

private

57

6.11

2.30

47%

147

58

2

43

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESCountryCN

Model
Roewe e550

User
group

n

Ø FC

NEDC FC

Ø UF

System
power

Range

Sources/
Variants

private

153

6.46

1.60

21%

147

60

1

CN

Roewe e950 1.4T

private

69

6.10

1.70

30%

169

60

2

CN

Roewe ei6 45T

private

101

5.62

1.50

20%

152

53

1

CN

Roewe i6 45T

private

216

5.61

1.50

20%

152

53

1

CN

Roewe i6 Plus 50T

private

17

5.83

1.50

15%

224

51

1

CN

Roewe i6 Plus 50T 4G

private

11

5.96

1.50

13%

224

51

1

CN

Roewe RX5 eRX5 50T

private

440

5.96

1.55

24%

177.5

60

2

CN

Toyota Corolla E+ 1.8L

private

274

4.87

1.30

22%

126

55

3

CN

Toyota Levin E+ 1.8PH GS CVTprivate

211

4.66

1.30

25%

126

55

3

CN

Toyota Prius PHEV

private

14

2.63

2.10

45%

100

25

1

CN

Trumpchi GA3S PHEV 1.5L

private

12

6.04

1.70

38%

201

70

1

CN

Trumpchi GA5 PHEV

private

57

6.70

2.40

38%

139

50

3

CN

Trumpchi GS4 1.5L PHEV

private

580

6.58

1.70

22%

201

58

2

CN

Volvo S60L T6 E

private

10

6.13

2.10

38%

238

53

1

CN

Volvo XC60 T8 E

private

8

7.16

2.30

31%

300

50

1

CN

VW Golf GTE

private

21

4.52

1.60

37%

190

50

1

CN

VW Passat 430 PHEV

private

110

4.99

1.50

37%

195

63

2

CN

VW Passat Variant GTE

private

25

5.19

1.80

36%

200

50

1

CN

VW Tiguan L 430 PHEV

private

63

5.41

1.90

38%

195

52

1

CN

WEY P8 2.0T

private

132

8.10

2.30

22%

257

50

1

CN

WEY VV7 PHEV 2.0T

private

6

8.26

1.60

9%

252

70

1

DE

Audi A3 e-tron

private

68

3.69

1.58

46%

150

47

5

DE

Audi A3 e-tron

company

11

6.83

1.55

8%

150

48

2

DE

BMW 225xe

private

109

4.34

2.05

48%

165

44

4

DE

BMW 225xe

company

1

7.09

2.00

10%

165

41

1

DE

BMW 330e

private

42

5.10

1.94

37%

191

44

5

DE

BMW 330e

company

2

6.60

2.00

18%

185

40

2

DE

BMW 530e

private

25

5.81

2.00

34%

185

48

2

DE

BMW 530e

company

2

6.09

1.93

29%

185

49

1

DE

BMW i3 REX

private

17

0.98

0.60

86%

125

170

1

DE

BMW i3 REX

company

1

0.60

0.60

94%

125

240

1

DE

BMW X5 xDrive40e iPerformance

private

7

9.20

3.37

19%

230

31

1

DE

BMW X5 xDrive40e iPerformancecompany

2

7.10

3.37

37%

230

31

1

DE

Chevrolet Volt

private

5

2.17

1.20

72%

111

83

1

DE

Hyundai IONIQ

private

97

2.76

1.10

53%

104

63

1

DE

Kia Niro PlugIn-Hybrid

private

100

3.15

1.30

51%

104

58

1

DE

Kia Optima

private

11

2.97

1.60

61%

151

54

1

DE

Kia Optima Sportswagon

private

33

3.99

1.40

45%

115

62

1

DE

Mercedes C 350 e

private

18

5.61

2.10

22%

205

31

1

DE

Mercedes C 350 e

company

14

7.78

2.10

4%

205

31

2

DE

Mercedes C 350 e T-Modell

company

8

7.71

2.12

4%

207.5

31

2

DE

Mercedes E 300 deprivate

14

4.91

1.55

27%

225

47

2

DE

Mercedes E 300 de T-Modell

private

11

5.08

1.60

28%

225

46

1

DE

Mercedes E 350 e

private

5

6.30

2.10

15%

210

33

1

DE

Mercedes E 350 e

company

11

8.13

2.10

8%

210

33

1

DE

Mercedes GLC 350 e

private

11

6.65

2.50

28%

235

34

1

DE

Mercedes GLC 350 e

company

5

6.19

2.50

30%

235

34

2

DE

Mini Countryman Cooper S E

private

36

4.44

2.13

48%

165

42

3

DE

Mitsubishi Outlander PHEV

private

316

4.37

1.81

49%

131.2554

444

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESModel

User
group

n

Ø FC

NEDC FC

Ø UF

System
power

Range

Sources/
Variants

DE

Opel Ampera E-REV

private

39

2.26

1.20

71%

111

83

1

DE

Toyota Prius PHEV

private

113

2.70

1.33

48%

92.5

44

4

DE

Volvo V60 D6 Hybrid

private

44

4.66

1.80

42%

206

50

1

DE

Volvo V60 D6 Twin Engine

private

17

4.85

1.80

40%

212

50

1

DE

Volvo V60 T8 Twin Engine

private

13

5.27

1.90

35%

293

46

2

DE

Volvo XC60 T8 Twin Engine

private

10

5.46

2.20

36%

288

40

2

DE

Volvo XC60 T8 Twin Engine

company

1

6.97

2.30

16%

288

35

1

CountryDE

Volvo XC90 T8 Twin Engine

private

8

6.80

2.10

21%

288

43

1

DE

Volvo XC90 T8 Twin Engine

company

9

10.40

2.10

0%

288

43

3

DE

VW Golf GTE

private

113

3.91

1.55

44%

150

50

2

DE

VW Golf GTE

company

1

5.77

1.50

15%

150

50

1

DE

VW Passat GTE

private

103

4.11

1.70

46%

160

50

1

DE

VW Passat GTE

company

4

4.75

1.70

38%

160

50

1

NL

Audi A3 e-tron

company

-

6.90

1.57

24%

150

48

1

NL

Chevrolet Volt

company

-

6.94

1.20

37%

111

95

1

NL

Ford C-Max Energi

company

-

6.58

-

23%

136

44

1

NL

Mercedes C 350 e

company

-

7.75

2.10

19%

205

31

1

NL

Mitsubishi Outlander PHEV

company

-

7.52

1.85

29%

120

54

2

NL

Opel Ampera E-REV

company

-

6.62

1.20

34%

111

83

1

NL

Toyota Prius PHEV

company

-

4.85

2.10

16%

100

25

2

NL

Volvo V60

company

-

5.32

1.80

16%

206

50

1

NL

Volvo V60 Twin Engine

company

-

6.41

1.80

17%

212

50

1

NL

Volvo XC90 T8 Twin Engine

company

-

10.00

2.10

25%

288

43

1

NL

VW Golf GTE

company

-

6.85

1.50

22%

150

50

1

NL

VW Passat GTE

company

-

7.04

1.70

24%

160

50

1

NO

Audi A3 e-tron

private

197

2.83

1.53

59%

150

50

1

NO

Mercedes C 350 e

private

11

4.16

2.10

41%

205

31

1

NO

Mitsubishi Outlander PHEV

private

806

3.96

1.86

55%

120

54

1

NO

Opel Ampera E-REV

private

46

2.18

1.20

72%

111

83

1

NO

Toyota Prius PHEV

private

67

3.91

2.10

38%

100

25

1

NO

Volvo V60

private

104

3.97

1.80

51%

206

50

1

NO

VW Golf GTE

private

283

2.90

1.50

57%

150

50

1

US

BMW i3 REX

private

8309

0.49

0.60

93%

125

170

1

US

Cadillac ELR

private

5

2.43

-

66%

162

-

1

US

Chevrolet Volt

private

62616

2.00

1.09

72%

111

96

16

US

Chrysler Pacifica Hybrid

private

10

5.10

-

31%

194

-

2

US

Ford C-Max Energi

private

5455

3.35

-

41%

136

44

5

US

Ford Fusion Energi

private

5872

3.14

-

44%

136

32

7

US

Honda Accord

private

189

-

-

22%

144

-

1

US

Honda Clarity

private

14

2.35

-

58%

158

-

1

US

Kia Niro PlugIn-Hybrid

private

6

2.93

1.30

55%

104

58

1

US

Mitsubishi Outlander PHEV

private

8

3.59

1.70

55%

120

54

1

US

Sonata plug-in hybrid

private

5

3.29

-

45%

153

-

1

US

Toyota Prius PHEV

private

1622

4.24

1.88

30%

98

30

5

45

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESANNUAL ELECTRIC MILEAGE BY NEDC RANGE FOR PHEVS AND
RANGE-EXTENDED ELECTRIC VEHICLESThe main text showed the effect of NEDC range on annual electric kilometers driven
for all PHEVs. The following figure distinguishes between PHEVs and range-extended
electric vehicles, the Chevrolet Volt, Opel Ampera, and BMW i3 REX. The depedenceof annual electric kilometers driven on range and the mean values of annual electrickilometers driven for fixed given ranges are the same as in the main text.

Annual electric mileage [km]20,00015,00010,000Sample size
10

100

1,000

5,000
Vehicle type
PHEV
0

0

20

40

60

80

100

120

REEV

140

160

180

NEDC range [km]Figure A2. Annual electric mileage by NEDC range. Mean annual electric mileage by PHEV
model variant for the range-extended electric vehicles (REEVs) (squares) and PHEVs (circles).
Every data point corresponds to a model mean with different sample sizes (indicated by thesize of the symbol). The shaded area is a sample size-weighted local smoother (95% confidence
bands of generalized additive model).

REGRESSION ANALYSIS – AGGREGATED VEHICLE DATA
Since the direct emissions are strictly non-negative, we use an exponential for theeffect of all-electric range (AER) FCreal = exp(β0 + β1Power + β2AER + β3usergroup+ β4country) + ε. Here, the system power (Power), i.e. combustion engine powerplus electric motor power measured in kW, has been used as a proxy for engine
displacement, weight and model-specific aggressiveness of driving. The chosen
dependence on AER and power are: For AERg0, the fuel consumption approaches
a finite value (i.e. the emissions in the charge-sustaining mode) and is decreasing tozero for AERg∞ (i.e. a negative β 2). Likewise, the fuel consumption approaches zerofor Powerg0 and grows with increasing power (i.e. positive β1). The inclusion of weightas an additional covariate does not alter the results shown below. The regression is
performed after taking logarithms of the above equation In (FCreal) = β0 + β1Power +β2AER + β3usergroup + β4country + ε by ordinary least squares. The model itself and all
coefficients are significant (p < 0.05) and the coefficients have the expected signs (β1> 0 and β2 > 0) for AERg 0 and are significantly different from zero. In the regression
for aggregated values, we compare averages of subsamples with different sample
sizes. We use weighted least squares to adjust for these differences in sample size.
More specifically, the uncertainty of the individual sample averages has nonconstantvariance (heteroskedasticity). To correct for the different variances in the observations,each observation is weighted by the inverse of its standard error. In our case of meanvalues, the weights are thus proportional to the reciprocal of 1/√N = √N. We thus use
the square roots of sample sizes as weights.

46

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESTable A3. Factors impacting average real-world fuel consumption. Dependent variable:(log of) real-world fuel consumption in l/100 km. Standard errors in parentheses.
Source: Own calculations. *p<0.05; **p<0.01; ***p<0.001.
Sample-size weighted

Not sample-size weighted

Change in real-world average fuel consumption
All-electric range [10 km]–13%*** (0.5%)–9% *** (0.6%)

System power [10 kW]+3.1% (0.3%)+2.8%*** (0.2%)

Company cars

+31%*** (11%)+32%*** (5.5%)

Country: China+44%*** (5%)

40%*** (4%)

Country: NL+20%*** (7.4%)

Country: NO–12% (7.7%)–17%* (8.6%)

Country: US–12% (5.4%)–19%* (5.2%)

Observations

211

225

R²

0.913

0.812

Adjusted R²

0.910

0.806

F-Statistics

355*** (df = 6; 204)

134*** (df = 7; 217)

Table A4. Regression results for UF real. Quasi-binomial regression model with robust
standard errors in parentheses. Source: Own calculations. *p<0.05; **p<0.01; ***p<0.001.
Dependent variable: UF
Sample-size
weighted

Not sample-size
weighted

Without
Chevrolet Volt

All-electric range [10 km]

0.232***(0.033)0.175***(0.016)

0.222***(0.019)System power [10 kW]-0.037***(0.006)-0.042***(0.006)-0.033***(0.005)Company cars

-1.333***(0264)-1.186***(0.287)-1.354***(0.261)Country: China-0.991***(0.088)-0.836***(0.089)-0.995***(0.082)0.163(0.290)Country: Netherlands
Country: NO

0.324***(0.082)0.335***(0.089)0.332***(0.078)Country: US

0.013(0.140)0.034(0.102)-0.255*(0.126)

Constant-0.781***(0.159)-0.426***(0.131)-0.790***(0.116)

Sample size weighted

Yes

No

Yes

Chevrolet Volt included

Yes

Yes

No

Observations

214

228

197

Null Deviance

507.5

44.0

279.5

Residual Deviance

93.4

16.7

68.3

47

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESRegression Analysis – Individual Vehicle Level Data
We performed regression analysis of the individual UFs on annual mileage,
all-electric range, system power, and other control variables (user group andcountry). We ran regression models with and without the Chevrolet Volt andwith and without an interaction between annual mileage and all-electric range,
as an increase in range could be expected to decrease with annual mileage.
Table A5. Fractional logit regression on UF for individual vehicle data. Quasi-binomial
regression model with robust standard errors in parentheses. Source: Own calculations. ***p<0.001, ** p<0.01, * p<0.05.
Dependent Variable: UF
(1)

(2)

(3)

(4)

Annual mileage [1000 km]-0.029***(0.001)-0.026***(0.002)-0.040***(0.003)-0.036***(0.009)All-electric range [10 km]-0.002(0.005)0.175***(0.013)-0.029***(0.009)0.128***(0.038)System power [10 kW]-0.049***(0.005)-0.031***(0.004)-0.048***(0.005)-0.031***(0.004)

Private users

1.245***(0.224)

1.219***(0.220)1.200***(0.225)1.200***(0.221)Country US

0.938***(0.037)-0.554***(0.140)0.954***(0.038)-0.554***(0.140)-0.001***(0.0003)

0.002(0.002)

Interaction annual VKT &all-electric range
Intercept
Chevy Volt data included?

-0.044(0.241)-1.265***(0.252)0.230(0.255)-1.004***(0.330)

Yes

No

Yes

No

Sample N

11,7591,602

11,7591,602

Null deviance2383.4352.062383.4352.06

Residual deviance1679.7242.321677.3242.08The regression results on the effect of annual mileage, system power, and user group are
fairly robust against exclusion of the Volt data and the inclusion of an interaction term.
Table A6. Typical change in UF by changing variables. Marginal effects at means (MEM) withstandard errors in parentheses. Source: Own calculations. *** p<0.001, ** p<0.01, * p<0.05.
Marginal effect on UF
(1)

(2)

+1,000 km annual mileage-0.63%***(0.04)-0.63%***(0.01)+10 km all-electric range-0.04%(0.21)+4.3%***(0.95)+10 kW system power-1.04%***(0.24)-0.76%**(0.28)

Private user insteadof company car+30.0%***(8.5)+25.5%***(5.9)

Country: U.S. insteadof Germany+22.0%***(2.0)-12.9%(7.2)

Chevy Volt data included?

Yes

No

Sample size N

11,7591,602

We also analyzed more complex regression models including interaction terms between
range and annual mileage, but the total effect of mileage and range remained the same.

48

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESTable A7. Regression of fuel consumption on impact factors in Germany and the UnitedStates. Dependent Variable: (log of) real fuel consumption in l/100 km. Standard errors inparentheses. Source: Own calculations. *** p<0.001, ** p<0.01, * p<0.05.
Change in real-world average fuel consumption
(1)

(2)

+1,000 km annual mileage+2.1%***(0.1%)+1.0%***(0.1%)+10 km NEDC all-electric range–1.5%***(0.3%)–11.4%***(0.6%)+10 kW system power+3.8%***(0.4%)+3.1%***(0.2%)

User group: private–20.4%***(8.5%)–23.4%***(5.2%)Country: U.S.

–68.3%***(2.7%)+1.2%(6.3%)

Chevrolet Volt included?

Yes

No

Observations

11,759

1,602

R²

0.246

0.379

Adjusted R²

0.246

0.377

F-Statistics

766.6***

194.9

Effect of Observation Period on Simulated UF
To measure the effect of short observation periods on simulated UF, we simulate thesame driving data with increasing observation period. To this end, we use the daily VKTof the large Chevrolet Volt driving dataset from the Voltstats.net sample. We use only
vehicles with at least 100 days of observation and no more than 25% missing values.
Each vehicle’s daily driving is simulated as PHEVs with 20, 40, 60, 80, and 100 km
real-world range. The mean UF over the sample is calculated.
90%

Simulated utility factor (UF)

80%

70%

60%

50%

40%
Observation period [days]
30%

20

40

60
Real-world range [km]

7

28
80

100

1000
100

Figure A3. Effect of observation periods on simulated UF. The simulated UF is shown as afunction of real-world range in km for different observation periods of the driving data in thesimulation: 7 days (red), 28 days (orange), 100 days (light green), and 1,000 days (blue). The
simulated UF for given range decreases with observation period as long-distance trips are rare
and appear only in long observation periods.

49

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLESThe simulation with increasing observation period clearly shows a decrease in simulated
UF by about 5 percentage points as the observation period increases. The difference isparticularly strong from 7 to 28 days, but still clearly visible from 28 to 100 days.

50

ICCT WHITE PAPER | REAL-WORLD USAGE OF PLUG-IN HYBRID ELECTRIC VEHICLES