Traffic & Transport
Anna van Buerenplein 1
2595 DA Den Haag
P.O. Box 96800
2509 JE The Hague
The Netherlands

TNO report
TNO 2022 R10409

www.tno.nl

Real-world fuel consumption and electricity
consumption of passenger cars and light
commercial vehicles - 2021

T +31 88 866 00 00

Date

4 March 2022

Author(s)

Copy no
Number of pages
Sponsor
Project name
Project number

2022-STL-REP-100343882
56 (incl. appendices)
Ministry of Infrastructure and Water
Management Maatwerkadvies Verkeersemissies
060.39406

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Samenvatting
Inleiding
Dit rapport is de 2021-editie uit een serie rapporten over praktijkverbruik van
lichte voertuigen die de afgelopen jaren door TNO zijn geschreven voor het
Ministerie van Infrastructuur en Waterstaat. De voor dit rapport uitgevoerde analyse
van verbruikswaarden is gebaseerd op de data van tankpassen en laadpassen die
verkregen is van Travelcard Nederland BV. De conclusies van de analyse hebben
betrekking op bestuurders die een tankpas/laadpas van hun werkgever ter
beschikking hebben gekregen, en die hun brandstofkosten hiermee vergoed
krijgen. Een kwart van de totale Nederlandse personenautokilometers wordt met
zakelijke auto’s verreden.
De voor dit rapport uitgevoerde analyse van de tankpas/laadpasdata geeft inzicht
in het praktijkverbruik en werkelijke CO2-emissies uit de uitlaat, en in het
elektriciteitsgebruik in de praktijk van personenauto’s en bestelauto’s.
De beschikbare data over het elektriciteitsgebruik is flink toegenomen ten opzichte
van het vorige rapport. Het praktijkverbruik is in het algemeen hoger dan de officiële
typekeurwaarden van de fabrikanten; deze kloof verandert wel door de tijd heen,
en is in dit rapport gemonitord. De effectiviteit van het Nederlandse en Europese
klimaatbeleid, dat in belangrijke mate gebaseerd is op typekeurwaarden, is
afhankelijk van een goede correlatie tussen typekeur en praktijk.
Ook zijn aanvullende analyses opgenomen voor benzine- en dieselauto’s om het
effect van de toegenomen gemiddelde voertuigmassa te beoordelen. In een
toegevoegd hoofdstuk zijn de resultaten opgenomen van een eerste analyse van
het effect van weersomstandigheden op brandstofverbruik. Een beter begrip
hiervan helpt de voorspelling van (de variatie in) praktijkverbruik te verbeteren.
Dataset en representativiteit
De dataset bevat tankpasdata en laadpasdata, zoals verkregen van Travelcard
Nederland BV. Na filtering op validiteit van de data, bevat de dataset gegevens voor
275.000 benzineauto’s, waarvan 36.700 (plug-in) hybrides, 273.000 dieselauto’s,
waarvan 3.800 (plug-in) hybrides, en 54.000 bestelauto’s. Het elektriciteitsgebruik
kon worden vastgesteld voor 6.200 elektrische auto’s.
De brandstofverbruiksdata dekt de periode januari 2004 – juni 2021.
Het jaarkilometrage van de voertuigen varieert enorm, wat betekent dat de
database niet alleen voertuigen bevat van typische zakelijke veelrijders, maar ook
voertuigen van rijders die slechts een klein aantal kilometers per jaar afleggen.
Ongeveer 45% van de nieuw verkochte auto’s in Nederland zijn leaseauto’s.
Omdat deze leaseauto’s gemiddeld een hoger jaarkilometrage hebben dan auto’s
in particulier bezit, bepalen deze leaseauto’s in grote mate het gemiddeld
brandstofverbruik van de Nederlandse personenautovloot. De resultaten van de
monitoring worden daarom van belang geacht voor de Nederlandse vloot.
Omdat beleid gericht op het zuiniger maken van de voertuigvloot uiteindelijk tot doel
heeft om de CO2-emissies van mobiliteit te reduceren, worden alle resultaten in dit
rapport uitgedrukt in CO2-emissies uit de uitlaat.

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Vanwege het toenemende aandeel (bio-)ethanol in benzine verandert zowel de
samenstelling als de dichtheid van de brandstof, en als gevolg daarvan het
brandstofverbruik in liter per 100 km enigszins. Deze effecten, die elkaar
grotendeels opheffen bij de berekening van CO2-emissies uit de uitlaat, zijn hier niet
meegenomen: de CO2-emissie per liter wordt constant verondersteld over de tijd.
Resultaten
De WLTP-typekeurwaarden voor nieuwe personenauto’s zijn in 2020 lager dan in
2019, terwijl de CO2-praktijkemissies gelijk gebleven zijn. Er zijn significant minder
nieuwe diesels ingestroomd; de nog wel verkochte diesel-personenauto’s waren
relatief zwaar, en stootten gemiddeld meer uit dan de nieuwe diesels van 2019.
Dit ondanks een daling van de typekeurwaarden. De kloof tussen praktijk en
WLTP-typekeurwaarden was gemiddeld ongeveer 20 g CO2/km voor zowel benzine
als diesel, waarbij de praktijkwaarde dus hoger was. In relatieve termen uitgedrukt,
was de praktijk-CO2-uitstoot 1,13x zo hoog als de WLTP-waarde, voor zowel
benzine- als dieselpersonenauto’s. Als plug-in hybrides in de gemiddelden worden
meegenomen, nemen deze waarden toe tot 25 g CO2/km (1,17x).
De gemiddelde werkelijke CO2-emissie van nieuwe personenauto’s in Nederland
lag in 2020 op 156 g/km voor benzine en 164 g/km voor diesel (beide cijfers
inclusief (plug-in) hybrides). Als we apart naar de plug-ins kijken, is de gemiddelde
praktijkuitstoot 147 g/km: een kloof van 240% met de gemiddelde WLTPtypekeurwaarde van 43 g/km.
Om de ontwikkeling van brandstofverbruik te bekijken zonder de invloed van een
veranderende voertuigmassa, werd het brandstofverbruik uitgedrukt per ton
leeggewicht. Uit de analyse volgt dat de brandstofefficiëntie (in liter per 100 km
per ton) bij lichte benzineauto’s substantieel verbeterd is in de afgelopen 10 jaar.
De efficiëntiewinst was slechts beperkt voor zwaardere benzineauto’s, inclusief
plug-in hybrides, alsmede voor alle dieselauto’s.
Uit laadpasdata en kilometerregistraties is voor elektrische auto’s (EVs) een
gemiddeld energiegebruik berekend. In de getallen zit het laadverlies inbegrepen.
De meest efficiënte modellen gebruiken 16 kWh per 100 km, de minst efficiënte
modellen tot 30 kWh/100 km. Het gemiddelde voor de Travelcardvloot is niet
veranderd ten opzichte van het vorige rapport: 20,2 kWh/100 km. Als de
verbruiksgemiddelden van de individuele automodellen gewogen worden naar de
Nederlandse EV-vlootsamenstelling, dan is het resulterende gemiddelde
elektriciteitsgebruik bijna gelijk: 20,1 kWh/100 km.
Het praktijk-elektriciteitsgebruik is gemiddeld 19% hoger dan de WLTPtypekeurwaarde, maar de verschillen onder de voertuigmodellen zijn groot:
sommige modellen hebben een afwijking van +40% of meer.
Er is een voorspellend model gemaakt waarmee het praktijk-elektriciteitsgebruik
van EVs voorspeld kan worden. Het is gebaseerd op massa, luchtweerstand en
accucapaciteit, en is gefit naar de werkelijke gemiddelden per voertuigmodel. De
gemiddelde afwijking van het model is 8%, daarmee is het model een betere
voorspeller dan de WLTP-waarde. Uit analyse van omzettings- en accuverliezen,
die optreden bij (AC-)laden en ontladen van de accu, blijkt dat deze significant zijn,
in totaal in de orde van 10-20%.

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De relatie tussen jaarkilometrage en elektriciteitsgebruik (kWh/100 km) is
onderzocht. Hoewel de energieconsumptie bij snelwegkilometers hoog is bij
elektrische auto’s, en veel kilometers doorgaans een groot aandeel snelweg
betekent, is de trend tegenovergesteld. Hoe hoger het jaarkilometrage, hoe lager
het gemiddelde elektriciteitsgebruik. Bij 40.000 km/jaar valt het verbruik bij de vijf
meest verkochte EVs ca. 12% lager uit dan bij dezelfde auto’s die 10.000 km/jaar
afleggen.
Voor het eerst kon ook een gemiddeld waterstofverbruik berekend worden voor
brandstofcelpersonenauto’s. De waarde is gebaseerd op tankpasdata en is
een gemiddelde van 23 voertuigen. Het gemiddelde waterstofverbruik lag op
1,24 kg/100 km. Uitgedrukt in energie-inhoud is dit ruwweg twee keer zoveel als
de elektrische energie die per 100 km geladen werd door EVs.
Voor plug-in hybride personenauto’s is per model het aandeel elektrische
kilometers geschat; waarden variëren van 12% tot 34%, hetgeen niet significant
afwijkt van het vorige rapport.
Voor bestelauto’s is een sterke opwaartse trend te zien in het leeggewicht:
6-7% in twee jaar tijd. De brandstofefficiencyverbetering zette tegelijkertijd door:
het brandstofverbruik per ton leeggewicht daalde geleidelijk sinds 2010, met
gemiddeld 1,25% per jaar.
De kloof tussen WLTP typekeurwaarden en praktijkverbruik is nog niet stabiel
voor bestelauto’s, vanwege een nog beperkt aantal WLTP-voertuigen in de dataset.
De CO2-emissie in de praktijk lijkt onveranderd, ca. 220 g/km.
De CO2-emissies van de Travelcardvloot varieert door het jaar heen, van -4% tot
+4% ten opzichte van het jaargemiddelde. Een poging is gedaan om deze
seizoensvariatie te koppelen aan individuele omgevingsfactoren. Uit deze analyse
volgt dat in totaal een variatie van -1,5% tot +1,5% kan worden voorspeld uit
variaties in temperatuur, wind en zoninstraling (in volgorde van belangrijkheid).
Een deel van het effect is indirect, door veranderingen in gebruikspatroon en
veranderingen in bestuurdersgedrag als gevolg van variatie in temperatuur, wind en
zon. De resterende variatie van -2,5% tot +2,5% kan diverse oorzaken hebben,
waaronder non-lineaire effecten van weer en seizoensvariatie in
brandstofsamenstelling en banden.
Er is ook een inschatting gemaakt van het effect van een toenemende
bijmengverhouding van ethanol in benzine. Benzinevoertuigen vertoonden
gemiddeld 2% toename in brandstofverbruik vanaf de zomer van 2020, na correctie
voor normale seizoensvariatie. De toename kan hoogstwaarschijnlijk worden
toegeschreven aan de intreding van E10. Om het effect van E10 in de toekomst
correct mee te nemen moet meer onderzoek gedaan worden naar
brandstofeigenschappen, waaronder dichtheid en koolstofverhouding en de variatie
daarin.

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Summary
Introduction
This is the 2021 issue of a series of reports on real-world fuel consumption of
light vehicles, written by TNO for the Dutch Ministry of Infrastructure and Water
management. The primary data basis of this report is tank pass and charge pass
data, obtained from Travelcard Nederland BV. The conclusions from this analysis
relate to drivers who have a tank pass/charge pass available from their employer
and who are reimbursed for their fuel costs in this way. A quarter of the total
mileage of passenger cars in the Netherlands is driven with business cars.
The analysis of this data provides insight in real-world fuel consumption and tailpipe
CO2 emissions, as well as electricity consumption of passenger cars and vans.
The real-world consumption and emissions are generally higher than the associated
type approval values provided by the manufacturers; this gap changes over time,
and was monitored in this report. The effectiveness of greenhouse gas policies,
which are to a large extent based on type approval values, is dependent on a good
correlation between real-world and type approval.
In this report, more space was dedicated to electric vehicles. Based on the data
available, which has increased significantly since last year’s report, a model has
been formulated which allows the prediction of the average electricity consumption
of new vehicle models. Also, information about charging losses was included.
For vehicles fuelled with petrol and diesel, additional analyses were done to
understand the effect of increased mass on fuel consumption. In a new chapter,
the results are presented of a first analysis of the effect of weather conditions.
A better understanding allows a better prediction of the (variation in) real-world fuel
consumption.
Dataset and representativeness
The dataset includes tank pass data and charge pass data, as obtained from
Travelcard Nederland BV. After filtering for valid data, the dataset encompasses
275,000 petrol passenger cars, of which 36,700 (plug-in) hybrids, 273,000 diesel
passenger cars, of which 3,800 (plug-in) hybrids, and 54,000 vans. The electricity
consumption could be determined for 6,200 electric passenger cars.
Fuel consumption data covers the period of January 2004 up to June 2021.
The annual mileage of the vehicles in the fleet varies greatly, which means that the
database does not only contain vehicles of business drivers with high annual
mileage, but also vehicles of drivers that cover a relatively small mileage each year.
Around 45% of all new cars sold in the Netherlands are leased vehicles. As these
leased vehicles on average also have higher annual mileages than privately owned
vehicles, their effect on the average real-world fuel consumption across the Dutch
fleet is large. The results of the monitoring are therefore considered meaningful for
the Dutch passenger car fleet.
As the objective of policies for improving the fuel efficiency of the fleet is to reduce
the CO2 emissions from mobility, all results in this report are expressed in terms of
CO2 emissions from the exhaust. Due to the increasing level of ethanol in petrol,
the fuel consumption in litres per 100 km as well as the C/H/O-ratio and density of
the fuel change slightly.

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These effects, that largely cancel each other out in the calculation of tailpipe CO2
emissions, are not taken into account here: the CO2 emission factor per litre kept
constant.
Results
For new petrol cars, the WLTP type approval values decreased in 2020 compared
to 2019, while the real-world CO2 emissions were the same as those sold in 2019.
The inflow of new diesel cars decreased considerably; the remaining new diesels
were relatively heavy, and their real-world CO2 emissions were higher than in 2019,
while the type approval values decreased slightly at the same time. For new
vehicles, the WLTP type approval values are on average almost 20 g CO2/km
lower than the real-world emissions, for both petrol and diesel. Expressed in relative
terms, the real-world CO2 value is 1.13 times the WLTP type approval value for
both new petrol and new diesel cars. If plug-in hybrids are included, this gap
increases to 25 g/km for petrol cars (real world CO2 = 1.17 times WLTP value).
The average real-world CO2 emissions of new passenger cars in 2020 were
156 g/km for petrol including (plug-in) hybrids, and 164 g/km for diesel including
(plug-in) hybrids. New petrol plug-in hybrids in 2020 have average real-world CO2
emissions of 147 g/km. That is a 240% gap to the average WLTP value of 43 g/km.
An analysis was made of the fuel consumption per tonne of empty vehicle weight, to
cancel out the changes in average mass over time. This was done for new
registrations. For petrol cars, this fuel efficiency (litre per 100 km per tonne) has
improved considerably for the lightest cars, over the last 10 years. For heavier
petrol cars, including plug-in hybrids, as well as for diesel passenger cars, the
improvement is limited.
For electric passenger car models the average electricity consumption was
calculated from charge pass records and odometer registrations. The numbers
include charging losses. The most efficient models consume 16 kWh per 100 km,
the least efficient up to 30 kWh/100 km. The Travelcard fleet average has not
changed over time: 20.2 kWh/100 km. If the consumption numbers of the individual
car models are weighted to match the Dutch EV fleet composition, the resulting
average consumption is almost the same: 20.1 kWh/100 km.
The real-world electricity consumption is on average 19% higher than the WLTP
type approval value, but the differences among vehicle models is large: some have
an upward deviation of 40% or more.
A prediction model was made to estimate the real-world electricity consumption of
battery electric cars. It is based on mass, air drag and battery capacity, and is fitted
to the actually observed electricity consumption averages in the dataset.
The average deviation of the model is 8%, so the model is a better predictor than
the WLTP value. Analysis of conversion losses and battery losses that occur during
(AC) charging and discharging of the battery, pointed out that these are significant,
in the order of 10-20%.
The relation between annual driven distance and electricity consumption
(kWh/100 km) was investigated. Although driving on the highway is relatively
energy consuming for electric cars, and high mileage usually means a high share of
highway driving, the trend is the other way.

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The higher the annual distance, the lower the average energy consumption.
At 40,000 km/year the five most common car models in the dataset have an
electricity consumption that is around 12% lower than for vehicles of the same
models which are driven 10,000 km/year.
For fuel cell electric vehicles for the first time an average real-world hydrogen
consumption value was calculated based on tank pass data for 23 vehicles. The
hydrogen consumption was found to be 1.24 kg/100 km. In terms of energy content
in hydrogen this is roughly twice as much as the energy in charged kilowatt-hours
for battery electric vehicles.
For plug-in hybrid passenger cars, the share of electric driving was estimated for
individual models; values range between 12% and 34%, which is not significantly
different from the values in the previous report.
For light commercial vehicles, a strong upward trend was seen in the empty mass:
6-7% in two years. The fuel efficiency has continued to improve at the same time:
the fuel consumption per tonne of empty weight dropped 1.25% per year on
average, ever since 2010.
The WLTP type approval-to-real world gap is not stable yet in the data for vans, due
to a limited number of such vehicles in the dataset. The real-world average CO2
emissions seem unchanged at around 220 g/km.
The CO2 emissions of the Travelcard fleet vary by -4% to +4% throughout the year.
This seasonal variation was attempted to be linked to individual weather aspects.
In total -1.5% to +1.5% can be predicted using variations in temperature, wind, and
solar irradiation (in order of magnitude of their effect). A part of the effect is indirect,
by changes in the way people use their vehicles and changes in driver behaviour as
a result of the variation in temperature, wind and sun. The remaining variation of 2.5% to +2.5% can have many causes, including nonlinear effects of weather and
seasonal variation in fuel composition and tyres.
The effect of an increasing admixture of ethanol in petrol was estimated. Petrol
vehicles on average showed an increase in fuel consumption of 2% ever since
summer 2020, after compensation for normal seasonal variation. The increase can
most likely be attributed to the introduction of E10. To incorporate the effect of E10
correctly in future analyses, further study of fuel properties, including density and
carbon content and variations therein, is required.

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Contents
Samenvatting ........................................................................................................... 2
Summary .................................................................................................................. 5
1
1.1
1.2
1.3
1.4

Introduction .............................................................................................................. 9
Context ...................................................................................................................... 9
Vehicle database ..................................................................................................... 10
Methodology ............................................................................................................ 11
Representativeness of the results ........................................................................... 15

2
2.1
2.2
2.3

Real-world fuel consumption of passenger cars ............................................... 18
Introduction .............................................................................................................. 18
Real-world consumption by fuelling date ................................................................. 18
Real-world consumption by vehicle registration year .............................................. 22

3
3.1
3.2
3.3
3.4

Trends ..................................................................................................................... 25
Introduction .............................................................................................................. 25
Mass trends ............................................................................................................. 25
Fuel consumption per ton ........................................................................................ 27
Power-to-mass ratio................................................................................................. 30

4
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8

Real-world electricity consumption and hydrogen consumption of batteryelectric and fuel cell passenger cars ................................................................... 32
Introduction .............................................................................................................. 32
Average electricity consumption per model ............................................................. 32
Comparison with WLTP declared values ................................................................ 35
Prediction model ...................................................................................................... 36
Consistency of results ............................................................................................. 37
Charging losses ....................................................................................................... 38
Annual mileage and energy consumption ............................................................... 39
Hydrogen consumption of fuel cell electric vehicles ................................................ 39

5
5.1
5.2

Real-world fuel consumption of plug-in hybrid passenger cars ...................... 41
Theory ...................................................................................................................... 41
Results ..................................................................................................................... 42

6

Real-world fuel consumption of vans.................................................................. 44

7

Effects of weather on fuel consumption ............................................................. 47

8

The transitions to E5 and E10 petrol ................................................................... 51

9

Conclusions and discussion ................................................................................ 53

10

Signature ................................................................................................................ 56

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Introduction
On an annual basis, TNO studies the fuel and electricity consumption of vehicles,
based on data made available by Travelcard Nederland BV. The work is
commissioned by the Ministry of Infrastructure and Water management.
This year’s issue focuses more than previously on the impact of recent regulatory
changes intended to make official fuel consumption and electricity consumption
numbers better resemble the real-world averages.

1.1

Context
Over recent years there have been many regulatory developments affecting the
type approval as well as real-world CO2 emissions and fuel / energy consumption
of conventional vehicles, electric vehicles (EV) and plug-in hybrid electric vehicles
(PHEV, or OCV-HEV: off-vehicle charging hybrid electric vehicles). A new CO2 test
procedure was enforced: the Worldwide Harmonised Light Vehicles Test Procedure
(WLTP). Also a transition is taking place from the EU 2021 CO2 target for
manufacturers, to the new targets for 2025 and 2030. The regulatory changes move
manufacturers to modify their vehicles to meet the targets on a fleet level. This has
a direct effect on the fuel consumption and CO2 emission values as reported by the
manufacturers. The intention of the regulations is to cause a decrease of the realworld fuel consumption and CO2 emissions. However, whether the introduction of
the WLTP ensures that these values decrease at an equal rate as the type approval
values, remains to be seen.
Already for many years the CO2 targets and the reduction of officially reported
numbers have shown a limited impact on real-world CO2 emissions and fuel
consumption of conventional vehicles. Additionally, there is a transition ongoing to
PHEV and electric vehicles, which not only provides additional opportunities for the
manufacturers to meet the CO2 targets, but also affects trends with respect to the
average gap between real-world and type approval. For PHEV the real-world fuel
consumption reduction compared to similar non-plug-ins has been small in the
Netherlands, because of the way the vehicles are used, with a large fraction of
motorway driving for new vehicles, and a slack charging behaviour of many of the
drivers.
This report is one in a long series of reports on the fuel consumption of light duty
vehicles1. For this year’s issue, sufficient data was collected since 2017 to be able
to use WLTP-based type approval values as reference. NEDC-based analyses are
continued as well, however. Secondly, as the electric vehicle fleet keeps expanding,
the analysis of real-world energy consumption of electric vehicles gains importance.
Thirdly, a first analysis of the influence of weather conditions on fuel consumption
was included. Also, the effect of the transition from E5 petrol to E10 was analysed
for a longer period of time. And finally, Covid-19 has had a significant influence on
the way vehicles were used in the last one and a half year.
1
Previous reports in this series can be found here: https://www.tno.nl/en/focus-areas/traffictransport/roadmaps/sustainable-traffic-and-transport/sustainable-mobility-and-logistics/improvingair-quality-by-monitoring-real-world-emissions/overview-of-reports-of-actual-fuel-consumption-bypassenger-cars/

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At the same time (March 2020), a 100 km/h speed limit was enforced. Fuel
consumption data was analysed chronologically to visualise the effect of both
factors.
1.2

Vehicle database
For the purpose of this report, fuel and electricity transaction data was made
available by Travelcard Nederland BV. For each fuelling event, the amount of
tanked fuel, the odometer reading and the time and date were recorded. For each
charging event the kilowatt-hours and the time and date were recorded. After
filtering for plausibility and completeness, the dataset encompasses 52 million fuel
transactions and 1.8 million charging events. Rigorous filtering was necessary,
because the odometer settings are entered manually by the drivers after fuelling
and contain many errors and missing records. For more information on the
methodology for data processing and analysis see for example reports TNO 2016
R11258 and TNO 2013 R11165.
For electric cars, odometer readings are generally not registered at charging
events. Therefore odometer data, needed for the calculation of fuel consumption
per kilometre, had to be sourced elsewhere. On request of the Ministry, information
was sourced from the Netherlands Vehicle Authority (RDW) specifically for this
purpose. Around 71,500 odometer readings were collected for the 23,922 electric
vehicles in the dataset.
Table 1-1 shows the number of vehicles for which fuel consumption or electricity
consumption results could be determined.
Table 1-1: Number of vehicles with valid energy consumption data

Passenger cars

Vans

Drivetrain type
Petrol
Petrol hybrid
Petrol plug-in hybrid

Number of vehicles
238,403
24,480
12,197

Diesel
Diesel hybrid
Diesel plug-in hybrid

269,113
1,826
1,987

Electricity

6,210

Hydrogen

23

Other (natural gas, LPG; not
further considered in this report)

5,540

Diesel
Diesel hybrid

54,044
17

Electricity

77

Other (petrol, natural gas, LPG;
not further considered in this
report)

596

Data is available since 2004. The dataset is complete until end of June 2021 for
tank events, and until end of May 2021 for charging events.

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Vehicles stay in the database for as long as they are present in the fleets that use
Travelcard passes. The total mileage during which a vehicle was followed therefore
also varies from zero to over 200,000 km. For petrol and diesel vehicles, the
distribution of the monitored mileage per vehicle is displayed in Figure 1-1.

Figure 1-1: Histogram of the number of vehicles as a function of the total mileage over which the
vehicle is monitored.

The differences with the analysis of 2020 are small. Regarding plug-in hybrids, a
large fleet change can be expected next year. Then the last shift of vehicles for
which the drivers still benefit from a 60-month low additional tax liability (‘bijtelling’),
namely plug-ins registered in 2016, will have disappeared from the fleet. In the
meantime new plug-in sales have picked up due to manufacturer responses to
tightened CO2 targets.
1.3

Methodology
The methodology followed in this report is largely the same as in the 2020 report2.
Petrol/diesel
For petrol and diesel cars, consecutive fuelling events are combined with the
distance driven in between these fuelling events. Fuel consumption is calculated
by dividing the amount of total tanked litres by the total driven distance.
The average fuel consumption for a group of vehicles is determined by dividing the
sum of all tanked litres by the sum of all kilometres driven for the selection of tank
events of all vehicles for which the average is determined. This produces the
average over a selection of vehicles whereby the results for individual fuel events or
vehicles are weighted over the kilometres driven.
2

Real-world fuel consumption of passenger cars and light commercial vehicles, TNO report 2020
R11664, 30 October 2020.

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The ‘direct’ CO2 emissions, in other words the emissions of CO2 from the tailpipe,
can be directly related to the fuel consumption. The amount of CO2 emitted per litre
of fuel is related to the carbon content of the fuel and more or less fixed. Per litre of
petrol about 2370 grams of CO2 are emitted, while for diesel this value is 2650 gram
CO2 per litre.
These so-called emission factors are based on the relation between fuel
consumption and CO2 from the type-approval information, are representative for
100% fossil petrol and diesel, and have been assumed constant for the period over
which data is available. This ensures consistency with previous reports, and forms
a constant reference for the difference between real-world and type approval CO2
emissions. The emission factors have been assumed constant for the period over
which data is available. This may cause a slight difference with the actual tailpipe
emissions, because the increasing biofuel admixture has an effect on both the fuel
consumption in litres per 100 km and the tailpipe CO2 emissions per litre.
The increase in fuel consumption is now quantified in chapter 8 of this report,
and the decrease in CO2 emissions per litre can be calculated from changes in the
C/H/O-ratio in the fuel, and the change in density, due to the added biofuel.
Although these effects largely cancel each other out, a net effect remains. Based on
the new information in chapter 8, this effect will be accounted for in the next report.
In older reports on this topic, up to 2016, real-world fuel consumption and
CO2 emissions were compared to type approval values as tested over the New
European Driving Cycle (NEDC) to analyse trends in the real-world vs.
type approval ‘gap’. Since September 2017 (new vehicle models) and
September 2018 (all new cars) the Worldwide Harmonised Light Vehicle Test
Procedure (WLTP) is in force, replacing the NEDC. For large vans the introduction
dates are later. In the 2020 report a new series was started, analysing the gap
between real-world and WLTP-based fuel consumption and CO2 emissions.
Approximately 186,000 vehicles in the dataset have WLTP-based type approval
values. In this report WLTP is the basis for chapter 2. However, the NEDC-based
series of the previous reports is also continued. This is possible, because the 2021
EU target of 95g CO2 per kilometre is tied to the NEDC evaluation method.
Therefore, for new vehicles the WLTP CO2 values are translated mathematically
into a derived NEDC value, or are determined with double testing the vehicles on
WLTP and NEDC, usually referred to as NEDC 2.0.
The real-world fuel consumption of petrol/diesel plug-in hybrid passenger cars was
also calculated in the abovementioned way. Furthermore, fuelling data of PHEVs
was arranged per vehicle model for further analysis. The “low end” of the
distribution of fuel economy in km/l, achieved by sorting fuelling events for a
particular model by fuel economy, reveals a fuel consumption value for hybrid mode
(‘empty battery’ situation). The difference between the most common value (mode)
and the mean can be used to deduce an average share of electric driving for that
particular model.
A more detailed explanation can be found in chapter 5. The method is an extension
of approaches reported in reports TNO 2013 R10703 and TNO 2016 2016 R10938.

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Electric vehicles
Because the odometer reading is not available on a charge-to-charge basis, the
approach taken to calculate electricity consumption does not resemble the way fuel
consumption is calculated for fuelled cars.. The goal of the method is to calculate
the electricity consumption per kilometre for each vehicle individually. This is done
by matching charging data with odometer data; charging data and odometer
readings are obtained from two different sources and are generally not available on
matching dates. Also, in some instances charging data is available intermittently,
due to holidays, service, or possibly the temporary use of a different card or of
unmetered charging points. All in all, extensive filtering and validity checking is
necessary to select reliable series of data for the electricity consumption calculation.
The first step is to filter the electric vehicles in the database for the presence of at
least two odometer readings.
Subsequently, implausible charging data are removed. To this end, for each vehicle
brand, model and version (‘uitvoering’ in RDW vehicle registration data) a net
usable battery capacity was determined. Data on battery capacity were retrieved
from several sources (ev-database.nl, autoweek.nl, manufacturer specifications,
RDW data, and Wikipedia). Considering charging losses, and taking an additional
margin, charging events larger than 1.25 times the battery capacity of the car are
removed. The correctness of the battery capacities was verified by checking the
frequency of the occurrence of charging events exceeding the factor 1.25 rule for
individual models/versions.
Starting point for determining the electricity consumption of individual EVs is
clustering of the recorded charging events which are most likely subsequent,
i.e. not interrupted by unregistered charging events. This is done by setting a
maximum allowed gap between two charging events, which is dependent on the
average charging frequency of the user and the length of the charging event
sequence so far. Sequences are considered valid if they comply to two criteria:
a) it spans a period of at least 14 days, b) it consists of at least 10 charging events.
The kilometres to be associated with the total electricity consumed in a sequence
of charging events are then determined by matching odometer readings with the
sequence. For this purpose a graph was plotted for each vehicle with at least two
odometer readings (available for almost 6,300 vehicles). Two approaches are
followed.
Approach 1 is illustrated in Figure 1-2, using a real-world case. Two sets of
odometer readings and -dates are used (orange in the graph),
one close to the start date of a charging sequence, and one close to the end date
of this charging sequence. In the illustration these are plotted as orange dots on the
left bottom and right top of the graph. The charging sequence is plotted as a row
of blue dots. To align the kWh-data and odometer data in time, the cumulative kiloWatthour-level is predicted on the dates of the odometer readings. To this end, the
two closest charging samples are extrapolated (or interpolated) towards the date of
the odometer reading.

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In the illustrated case, extrapolation was needed, because the odometer reading
dates were outside the date range of the charging sequence.
The extrapolation is indicated by small green lines. Now the data are time aligned,
the calculation is simple: the electricity consumption equals ΔkWh / Δkm.

Figure 1-2: Example of approach 1 to determine electricity consumption per km.

If this approach does not succeed for a particular vehicle, for instance because the
odometer readings are too few, approach 2 is attempted. Approach 2 determines
the slope (in cumulative kWh) of the longest sequence of a vehicle and the slope
of the odometer readings. For both the regression coefficient must be over 0.9 to
be considered valid. In other words, if there are more than two odometer readings,
the annual mileage of the vehicle has to have been more or less constant to be
valid for this approach. Also, a minimum of 3000 km must be covered, and a
minimum of 25 charging events. The electricity consumption determined with this
method equals kWh/year / km/year.
Approach 2 is illustrated in Figure 1-3.

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Figure 1-3: Example of approach 2 to determine electricity consumption per km.

The procedures described above yielded 7,010 sets of charging data-distance
combinations for 6,287 vehicles.
Further filtering is done in two steps. First, for each vehicle a lower limit of energy
consumption was calculated, being 70% of a calculated kWh/100 km-value derived
from the WLTP value of the vehicle. All results below this threshold are removed.
Secondly, for each vehicle an initial average and standard deviation is calculated
for its energy consumption. All results outside two times the standard deviation are
removed. Also, results over 40 kWh/100 km are removed. The remaining results are
averaged per vehicle brand and model (every vehicle has an equal weight), and for
some vehicles the version as well. Only vehicle models with ten valid observations
(sequences) or more are included in the results. Models with too few valid results,
in other words with too few cars with valid results in the database, are left out.
This includes some models that are new on the market.
1.4

Representativeness of the results
Because the data was derived from tank passes and charge passes, which are
commonly used by drivers of leased cars, it is useful to see how the use patterns
relate to the Dutch average car use. The vehicles in the Travelcard fleet have a
high average annual mileage compared to the Dutch fleet, which may mean a
higher share of highway kilometers and thereby possibly a bias towards higher fuel
consumption. However, the distribution of annual mileages in Figure 1-4 shows that
the spread is large: a considerable share of vehicles are driven less than 20,000 km
per year.

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Figure 1-4: Distribution of annual mileages in the Travelcard database, per fuel/drivetraincombination.

Table 1-2 shows the average annual mileage for the Travelcard data in this report,
and the average annual mileage of the Dutch fleet, for each fuel/drivetrain
separately. The average annual mileage of young vehicles in the Dutch fleet is
shown separately, because the Travelcard database contains a moving fleet of
mostly relatively new vehicles. Because the Travelcard dataset contains not only
data for the last year, but also for all the years before (since 2004), the reference for
the Dutch fleet was selected to be 2019, a relatively normal year before the
influence of Covid-19.
Also shown is the most common mileage in the Travelcard data; for every fuel in the
table this value is lower than the average mileage, which means that a group of
high-mileage drivers inflates the average. This is indeed visible as a ‘tail’ in
Figure 1-4.
For all fuels/drivetrains in the table the decline in average annual mileages has
continued in 2020/2021, probably also reinforced by the lockdown for Covid-19.
The battery-electric vehicles in the dataset drive on average about 15% less than
the petrol cars in the dataset. However, this is influenced by the fact that most of the
charging data for electric vehicles is recent, which means that the lockdown has a
larger relative (downward) effect on the numbers for this category.
The table has an extra entry for battery-electric vehicles, shown as ‘Battery electric
NL’. This one shows the average and most common mileage for all 78,000 electric
vehicles in the Netherlands that have at least two odometer readings that are at
least half a year apart. This row was added to show that the average annual
mileage seems to have increased considerably since 2019.

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The small differences between the entries “Battery electric” and “Battery electric NL”
show that the dataset for electric cars is representative for the Dutch fleet in terms
of mileage.
Table 1-2: Annual mileages per fuel/drivetrain-combination

Average annual mileage (km)

Petrol
Petrol
plug-in

Most
common,
modal
annual
mileage (km)

This report
(2021)

2020
report

2018
report

Netherlands 2019
[CBS]

Netherlands
2019, 0-4
years
[CBS]3

22,500
20,000

23,600
24,200

24,700
24,700

26,700
33,900

10,900
22,600

17,000
22,800

Diesel

32,500

33,900

34,600

37,600

22,500

35,500

Diesel
plug-in

27,500

29,400

30,100

40,200

N/A

N/A

Battery
17,500
21,300
13,500
19,500
electric
Battery
15,000*
20,500*
13,500
19,500
electric
NL
Diesel
20,000
24,400
25,100
N/A
N/A
van
*) Based on the part of the Dutch EV fleet for which enough odometer data was available to
calculate an annual mileage (77,945 vehicles).

3

Vehicles with age 0, 1, 2 or 3 years. To calculate an average, the CBS data for vehicles age 0 is
scaled up to a full year by dividing by 0.55. This does not correct for irregularities in sales over
the year due to e.g. changes in tax regime.

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2

Real-world fuel consumption of passenger cars

2.1

Introduction

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This chapter shows trends in the real-world fuel consumption/CO2 emissions of
passenger cars, based on the Travelcard tank pass database. In the first part of the
analysis the data is grouped by fuelling date, meaning that the results are averages
over the entire fleet of vehicles present in the database for that year/month.
The second part of this chapter shows trends that can be observed when vehicles
are grouped by registration year of the vehicle.
The fuel consumption and the tailpipe CO2 emissions are directly related. Hereafter,
only graphs for CO2 emissions are displayed. The numbers can however be
converted into fuel (l/100 km) using the factors mentioned in paragraph 1.3.
2.2

Real-world consumption by fuelling date
The trend of the average real-world and type approval CO2 emissions per kilometre
for all petrol and diesel passenger cars in the database is shown in Figure 2-1 and
Figure 2-2. Figure 2-1 shows the recent trend for vehicles that were type approved
under the WLTP regulation. Figure 2-2 shows the trend for vehicles for which an
NEDC type approval value is available4.
Comparing the graphs it is clear that the WLTP type approval values are on
average quite a bit higher than the NEDC type approval values, and therefore
closer to the average real-world emissions. At the same time, the average
real-world emissions of WLTP approved cars have decreased more than the type
approval values (Figure 2-1), reducing the gap a bit more. Figure 2-3 shows that
the gap is reduced to around 15% for petrol and around 10% for diesel.
The reduction in real-world emissions in recent years is likely to be at least partially
related to reduced congestion due to Covid-19, and/or an effect of the daytime
highway speed limit of 100 km/h that was enforced from March 16, 2020.
A noteworthy point to make about Figure 2-2 is that the average WLTP CO2
emissions of the considered vehicle group are almost equal for petrol and diesel
vehicles. For NEDC, this has never been the case throughout the period 20042021, although the difference between the two has decreased in recent years.

4

NEDC or NEDC2.0 (calculated from WLTP). Note that although the WLTP average is also plotted
in the graph for reference, it cannot be directly compared to the NEDC average, because WLTP
covers only a subset of the vehicles.

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Figure 2-1: Average real-world and WLTP type approval tailpipe CO2 emissions of WLTP
approved conventional and non-plug-in hybrid petrol and diesel vehicles in the
database.

Figure 2-2: Average real-world and NEDC & WLTP type approval tailpipe CO2 emissions of all
conventional and non-plug-in hybrid petrol and diesel vehicles in the database. Note
that the WLTP lines are plotted just for reference; these represent a subset of (newer)
vehicles only.

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Figure 2-3: Type approval to real-world CO2 gap for WLTP approved conventional and
non-plug-in hybrid petrol and diesel vehicles

For plug-in hybrids the trends in real-world and type approval CO2 emissions are
shown in Figure 2-4 for WLTP type approved vehicles, and in Figure 2-5 for all
(passenger) vehicles in the database.
In contrast with non-plug-ins, the average type approval values of plug-ins have not
changed much during the change from the NEDC to the WLTP regime. This is
despite the changes in the test procedure and calculations. Furthermore, vehicles
may have been updated with a larger battery before the WLTP type approval
occurred.
The average real-world emissions of both petrol and diesel plug-ins hover around
150 g/km: not much lower than the average for non-plug-ins in Figure 2-1.
The gap with the average type approval rating is therefore large, as can be seen in
Figure 2-6.

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Figure 2-4: Average real-world and WLTP type approval tailpipe CO2 emissions of WLTP
approved plug-in hybrid petrol and diesel vehicles in the database.

Figure 2-5: Average real-world and NEDC type approval tailpipe CO2 emissions of all plug-in
hybrid petrol and diesel vehicles in the database.

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Figure 2-6: Type approval to real-world CO2 gap for WLTP approved plug-in hybrid petrol and
diesel vehicles

2.3

Real-world consumption by vehicle registration year
The developments on the level of the complete fleet as described above are largely
determined by the developments in new vehicles. To analyse these developments,
the fuel consumption data was regrouped by the registration year of the vehicles.
For each vehicle the fuel consumption was averaged over the entire duration it
was part of the Travelcard fleet, and the value was attributed to its year of first
registration.
The analysis focuses on the most recent trends, i.e. the developments since the
changes in type approval procedure were effectuated in 2017. This means that
hereafter the analysis is done only on WLTP approved vehicles. The analysis of
NEDC-based vehicles can be found in the previous report5.
New passenger cars in the year 2020 had average real-world CO2 emissions of
156 g/km (petrol, including (plug-in) hybrids) and 164 g/km (diesel, including
(plug-in) hybrids. These numbers were approximately the other way around in
last year’s report, reporting on 2019: 163 and 158 g/km for petrol and diesel
respectively. At least two factors play a role here: the calculated result for the fuel
consumption of new cars is not stable yet in the data, and the consumption of diesel
vehicles in the Travelcard fleet has increased fast over the last few years (from
153 g/km in 2018, through 158 g/km in 2019, to 164 g/km for cars registered in
2020). In the next chapter it is demonstrated that mass is a large factor in that
increase.

5

Real-world fuel consumption of passenger cars and light commercial vehicles, TNO report 2020
R11664, 30 October 2020.

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In Figure 2-7 the evolution of the gap between real-world and type approval
CO2 emissions is shown across the registration years. This is done for WLTP
approved vehicles, which started to come onto the market in significant numbers
in 2018. As concluded in the previous paragraph, the gap is smaller than was the
case with the old, NEDC-based type approval numbers. The trend, however, seems
to be upwards: the real-world emissions show a slight upward trend, while the type
approval values actually decreased slightly, leading to a gap of almost 25 g/km for
petrol and just under 20 g/km for diesel in 2020. Figure 2-8 shows the numbers
excluding plug-in hybrid vehicles. From a comparison of the graphs it can be
derived that the low type approval value for petrol vehicles in 2020 compared to
the previous years, and thereby the increased gap, is mostly caused by inflow of
plug-ins. The fact that the real-world emissions did not decrease at the same time
can be explained from the high real-world emission of these plug-ins (see Figure
2-6). For diesels, the small number of plug-ins have no real influence on the
numbers.

Figure 2-7: Average real-world CO2 emissions versus the average WLTP type approval values of
new petrol and diesel cars, including plug-in hybrids, differentiated by the year of
registration.

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Figure 2-8 Average real-world CO2 emissions versus the average WLTP type approval values of
new petrol and diesel cars, excluding plug-in hybrids, differentiated by the year of
registration.

New petrol plug-in hybrids in 2020 have an average real-world tailpipe CO2
emission of 147 g/km; that is 104 g/km higher than the average WLTP type
approval value of 43 g CO2/km. Thereby the relative gap is around 240%.

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3

Trends

3.1

Introduction

25 / 56

Using the same dataset, an analysis was made of some trends that are relevant for
understanding the observed development in real-world emissions. One of the main
factors influencing the differences in average fuel consumption among different
registration years, is vehicle mass. A higher mass leads to larger rolling resistance
and, although to a lesser extent for hybrids, higher braking energy losses. From
modelling work6 it was observed that mass generally also correlates well with the
frontal area of a vehicle, which influences the fuel consumption on the highway to
a large extent.
Other factors such as improvements in drivetrains, aerodynamics and tyres also
influence the trends in chapters 2, 5, and 6 as well, but this chapter focuses on
mass aspects only.
3.2

Mass trends
The development of vehicle mass is visualised in Figure 3-1 and Figure 3-2 for
petrol and diesel vehicles in the database, including (non-plug-in) hybrids.
The real-world fuel consumption trend is shown as well for reference. The two
y-axes of the graphs do not start at zero, but have equal ratios between top and
bottom. A difference in angle of the two lines indicates a change in fuel
consumption per kg of vehicle mass. The next graphs will further detail the fuel
efficiency trends.
After a decrease in mass since 2010 the mass trend of petrol cars is upwards
again after 2016. The relative increase in fuel consumption is much smaller.

6

Work carried out in the LIFE+ MILE21 project (LIFE17 GIC/GR/000128); the full report
D C 3.1 is available at https://www.mile21.eu/project/activities.

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Figure 3-1: Trend of average vehicle mass and real-world fuel consumption per registration year
for petrol passenger cars (incl. hybrids, excl. plug-in hybrids).

For diesel passenger cars (Figure 3-2) mass and fuel consumption increases have
gone hand in hand since 2015. The average mass of diesel cars in the database
has increased by 15% since 2015.

Figure 3-2: Trend of average vehicle mass and real-world fuel consumption per registration year
for diesel passenger cars (incl. hybrids, excl. plug-in hybrids).

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Diesel vans have become more efficient, especially in the last few years, see Figure
3-3. The variations, however, are relatively smaller than for passenger cars (note
that the scale on the y-axis is much smaller than that of the graph for diesel
passenger cars).

Figure 3-3: Trend of average vehicle mass and real-world fuel consumption per registration year
for diesel vans (excl. plug-in hybrids).

3.3

Fuel consumption per ton
If the mass evolution and the trend in real-world fuel consumption are combined, it
is possible to visualise the improvements in vehicle efficiency across the registration
years. The trends in the other chapters of this report are influenced by both the
mass trend (previous paragraph) and the efficiency trend.
The mass-independent efficiency gain is shown by dividing the fuel consumption by
the mass of the vehicle; this results in a fuel consumption in litres per 100 km per
ton of empty vehicle mass. Figure 3-4, Figure 3-5, and Figure 3-6 show the average
real-world fuel consumption per ton of vehicle mass (empty), for petrol passenger
cars, diesel passenger cars, and diesel vans, of different empty mass categories. In
the graphs, every group of vehicles of a certain mass range is plotted separately, to
show the differences over time. Hybrids and plug-in hybrids are included in the
graphs.
The declining lines in the graphs show that generally newer vehicles consume less
fuel than older vehicles with the same empty mass. This is least profound for diesel
passenger cars, probably because the most important efficiency gains were made
in the period before 2010. For petrol the improvement is largest for the lightest cars.
For vans, the newest vehicles are indeed the most efficient in all mass categories.
Note that the <=1100 kg-line first inclines steeply, and then disappears; this is due
to the fact that vans of this mass category disappeared from the market.

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In all three graphs, the lightest vehicles have the highest specific fuel consumption,
which means that the fuel consumption is less-than-proportional to mass. However,
this levels out; the lines for the heavier categories are closer together. For petrol up
to 2017 the vehicles with the lowest mass specific fuel consumption are not even
the ones in the heaviest category, but the ones of 1300-1500 kg. This is because of
the high share of hybrids.
Some trends in the graphs can be explained from the influence of changes in tax
regulations in the Netherlands on the sold fleet composition. For instance, further
analysis pointed out that in the years 2012-2015 also non-hybrids were relatively
efficient, which holds for both petrol and diesel. To illustrate the impact of different
driveline shares, Figure 3-7 is introduced.

Figure 3-4: Mass specific fuel consumption of petrol passenger cars, including plug-ins, per mass
category

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Figure 3-5: Mass specific fuel consumption of diesel passenger cars, including plug-ins, per mass
category

Figure 3-6: Mass specific fuel consumption of diesel vans, per mass category

Figure 3-7 shows the number of passenger cars present in the Travelcard
database, split per registration year, per fuel and per drivetrain type. For the dieselline it is important to recognize that the inflow diminished in 2020, and that the drop
in 2016 coincides with dropping the 14%-ruling for cars with low NEDC CO2 value
(end of 2015).

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Judging from the previous graphs, the diesels sold in the years before that were not
only lower on NEDC CO2, but more economical in the real world as well. For petrol
it is slightly more complex. Plug-in sales peaked in 2015, just before the 0% tax
liability was stopped, but non-plug-in petrol sales (to Travelcard customers) already
dropped considerably in 2013. Apparently the remaining non-plug-ins sold were
relatively efficient as well.

Figure 3-7: Number of passenger cars of every registration year in the Travelcard database

3.4

Power-to-mass ratio
Based on the Travelcard database, a fuel consumption prediction model was made,
which uses just fuel type, empty mass, year of first registration and engine power
as inputs7. During the development of the model it became apparent that some
vehicles have a much higher fuel consumption and CO2 emission than expected.
Besides mass and mass per ton of empty mass, the relative engine power is also of
importance to understand real world emissions.
For most vehicle models, the average fuel consumption can be predicted quite well
using just fuel type, empty mass and registration year (as a proxy for build year) of
the vehicle. For some individual vehicle models, the average real-world fuel
consumption was found to be quite different than the average of vehicles with
similar parameter values. One of the factors observed to have a large influence for
some models is the power-to-mass ratio. In theory, an oversized engine has a
relatively low efficiency in normal operation, because losses are partially
independent of the delivered power. If the engine operates at a low load, the share
of these losses is large. For underpowered vehicles, the opposite should be the
case, which could make them run more efficiently than average.
7

Work carried out in the LIFE+ MILE21 project (LIFE17 GIC/GR/000128); the full report
D C 3.1 is available at https://www.mile21.eu/project/activities.

TNO report | TNO 2022 R10409 | 4 March 2022

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In the real world there are more factors in play however, not in the last place related
to differences in driver input for different cases, so the actual effect is difficult to
predict.
In Figure 3-8, the deviation of real-world CO2 emissions from model prediction is
plotted as a function of the power-to-mass ratio. The graph includes all passenger
cars and vans in the Travelcard database. Each dot represents a group of vehicles
with similar mass and engine power. The semi-transparent lines indicate the
number of vehicles (frequency distribution) across the power-to-mass range.
These show that most vehicles are in the range of 50-90 kW/tonne.
The light orange line also reveals that there is still a significant group of diesel
vehicles with a power-to-mass ratio below 50 kW/tonne. These are small diesel
passenger cars, and a category of vans with relatively low power engines. This
category of vans is slowly disappearing, but was the standard around 20 years ago,
in terms of power/mass ratio. The orange dashed line is the suggested model
adaptation to correct for low-powered diesels; it reaches up to 50 g CO2/km for
diesels with 30 kW/tonne.
The scattered cloud of blue dots on the right-hand side of the graph represents
mostly sportscars, and some high-power SUVs. The number of vehicles of this
category in the dataset is rather small though. The dashed blue line indicates the
model adaptation for high-power petrol cars.

Figure 3-8: Deviation of real-world CO2 emissions from the prediction model calculation, as a
function of the power-to-mass ratio. Every dot is a bin of vehicles with similar mass
and power.

In the same project a second model was designed, that can fine-tune
(or personalise) the fuel consumption prediction of a vehicle based on information
about the use pattern and -conditions. It is described in deliverable D C3.2 and can
be downloaded here as well: https://www.mile21.eu/project/activities.

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4

Real-world electricity consumption and hydrogen
consumption of battery-electric and fuel cell
passenger cars

4.1

Introduction

32 / 56

As described in paragraph 1.3, real-world electricity consumption of electric vehicles
has been monitored by combining charging sessions data with odometer readings
for each full electric car in the Travelcard fleet. Note that charging sessions include
home charging as well as public fast and regular charging. Vehicles with intermittent
charging patterns were rejected from the dataset. Most vehicles monitored last year
are still in the fleet, and many new electric vehicles entered the fleet since the
analysis for the last report. Therefore the amount of available, usable data has
doubled. In total 9,659 series of charging events of 8,461 unique vehicles were
available for deriving the electricity consumption per vehicle model. Charging data
were available up to 31 May 2021 and are expressed in paid kilowatt hours (kWh).
The 9,569 series were further filtered for outliers (see paragraph 1.3), after which
7,010 series of charging events were considered valid and used for further analysis
(6,287 unique vehicles).
Because the data in this chapter were derived from paid kilowatt hours, the
following energy consumption items are included as well:
-

-

Charging losses. These may vary from model to model and from charger to
charger.
Conditioning of the vehicle interior while connected to the charger. Most
electric vehicles have a built-in timer that allows pre-heating and -cooling of
the interior before the time the user sets to depart.
Conditioning of the battery while connected to the charger. Almost all
electric vehicles cool the battery when necessary during charging. The
higher the charging speed, the larger the (relative) heat production. Some
vehicles also prevent the battery from freezing in the winter.

These numbers are not included in the dashboard readings (they don’t affect the
range), and the last two are not considered in the official WLTC energy
consumption values either.
The last paragraph of this chapter shows the first results of the average hydrogen
consumption of fuel cell electric passenger cars.
4.2

Average electricity consumption per model
Table 4-1 shows the average electricity consumption, as well as the number of
observations (series of charging events) and the standard deviation. Vehicle models
having less than 10 observations were not further analysed. In some occasions
vehicle model variants could be distinguished, e.g. for the different versions of the
Nissan Leaf. When the next report in this series is due, the amount of data as well
as the completeness of RDW registration data is expected to be sufficient to
distinguish variants for most vehicle models.

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Table 4-1: Average electricity consumption per brand and model.

Series of
charging
events

Unique Average
vehicle [kWh/100 Standard
s
km] deviation

Brand

Model

Audi

427

380

Azure Dynamics

E-Tron
Transit Connect
Electric

=<10

=<10

BMW

I3

241

BMW

I3S

59

Citroen

C-Zero

=<10

=<10

DS

DS3 Crossback

=<10

=<10

Fiat

500E

=<10

=<10

Ford

Focus Electric

=<10

=<10

Hyundai

Ioniq

406

Hyundai

Kona

Jaguar

I-Pace

Kia

Niro

Kia

Soul

Lexus

UX300E

Maxus

EV80

=<10

=<10

Mazda

MX-30

=<10

=<10

Mercedes-Benz

B 250 E

22

Mercedes-Benz

EQC400 4Matic

27.3

6.1

204

17.8

4.4

49

18.4

4.3

364

16.1

3.7

736

658

17.3

4.0

190

167

26.3

7.2

641

614

18.0

3.9

22

17

21.1

6.7

=<10

=<10

17

20.7

6.1

23

22

25.8

6.6

Mercedes-Benz

eVito

=<10

=<10

MG

ZS EV

42

42

18.1

6.8

Mini

Cooper SE

13

12

17.2

4.0

Mitsubishi

i-MiEV

=<10

=<10

Nissan

E-NV200

60

55

25.4

5.1

Nissan

Leaf

90

87

20.6

5.5

Nissan

Leaf 40Kwh

307

274

18.8

4.6

Nissan

Leaf 62Kwh

140

124

21.3

4.9

Opel

Ampera-e

182

165

19.9

4.9

Opel

Corsa-e

24

24

23.7

5.7

Opel

Vivaro-e

=<10

=<10

Peugeot

e-208

32

32

22.4

5.8

Peugeot

e-2008

45

44

22.8

3.5

Peugeot

Ion

=<10

=<10

Peugeot

Partner

=<10

=<10

Polestar

2

82

82

28.4

5.8

Porsche

Taycan

=<10

=<10

Renault

Fluence Z.E.

=<10

=<10

Renault

Kangoo Express Z.E

22

21

21.2

5.2

Renault

Twingo

=<10

=<10

Renault

Zoe

436

396

20.2

4.8

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Series of
charging
events

Unique Average
vehicle [kWh/100 Standard
s
km] deviation

Brand

Model

Seat

MII

=<10

=<10

Skoda

Citigo

=<10

=<10

Smart

Fortwo ED

=<10

=<10

Smart

EQ Forfour

28

26

Smart

EQ Fortwo

=<10

=<10

19.5

5.4

Smart

Forfour ED

=<10

=<10

Tesla

Model 3

1100

1014

19.2

4.8

Tesla

Model S

381

337

20.6

5.1

Tesla

Model X

70

70

24.8

5.7

Volkswagen

Crafter

=<10

=<10

Volkswagen

Golf

769

608

17.4

4.0

Volkswagen

ID.3

299

288

23.5

6.2

Volkswagen

ID.4

33

34

24.6

5.0

Volkswagen

Up

42

28

17.9

4.5

Volvo

XC40

46

42

29.9

6.3

The standard deviations are included to illustrate that the variation among drivers of
the same vehicle model is large. For electric vehicles the energy consumption per
kilometre is relatively independent of speed for lower speeds, but increases with the
square of the speed above 100 km/h, due to higher air drag at higher velocities.
Therefore, the share of highway driving and the actual speed driven has a large
influence on the average energy consumption.
In urban areas, brake energy recovery helps reducing the energy consumption.
However, losses do occur, and frequent and heavy braking on any road can still
increase the energy consumption. The use of airconditioning and heater influences
the energy consumption as well, especially over short trips and at low speeds (more
time per kilometre). Another factor that plays a role is the charging efficiency: for
slow charging it varies from vehicle model to vehicle model. For fast charging, faster
means higher losses.
For some models the electricity consumption has decreased significantly compared
to the previous analysis. The amount of data has doubled, which means that the
pool of drivers (use cases) for each model has changed quite a lot. Furthermore,
it can be expected that insofar electric vehicles were driven at high speeds on the
highway, the 100 km/h speed limit introduced in March 2020 should have had a
large influence.
Figure 4-1 shows the vehicle models sorted by average real-world consumption.
The top of the chart consists of SUVs and vans: tall vehicles with relatively
unfavourable shapes in terms of aerodynamics. For large van models not enough
data is available yet, but dependent on the speed driven, even higher values can
be expected for these.

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Figure 4-1: Average real-world electricity consumption per vehicle model, measured at the charger
(includes charging losses).

The average electricity consumption of the 28 passenger car models in the graph is
20.2 kWh/100 km (weighted across the models). If accounted for the number of
vehicles of each model on the road, the average for the present Dutch fleet (per
August 2021) is 20.1 kWh/100 km. 85% of the electric vehicles in the Dutch fleet is
one of the 28 models in the graph.
4.3

Comparison with WLTP declared values
The presented real-world electricity consumption values are generally higher than
the values declared by the manufacturer under the WLTP regime. A per-model
comparison is made in Figure 4-2.

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Figure 4-2: Comparison of real-world electricity consumption and WLTP declared values for full
electric vehicles.

On average, the real-world value equals 1.19 times the WLTP declared value.
This result is in line with the deviation between the WLTP and real-world energy
use of conventional cars, but probably caused by different underlying issues.
WLTP does include charging losses (AC charging).
In the graph, the vertical distance from a point to the red line indicates the
difference between real-world and type approval. The difference between real-world
and type approval energy consumption varies to a large extent from model to
model. The MG ZS EV and the Nissan E-NV200 stay closest to the declared value,
while the real-world energy consumption of the Smart EQ Forfour is lower than its
WLTP value. The Polestar 2 has the largest deviation in an upward direction.
4.4

Prediction model
On the basis of the data in Table 4-1 and some additional properties of each vehicle
model, a regression model was made that can be used to predict the average
electricity consumption that can be expected from vehicle models that are not in the
table. The best prediction could be made on the basis of mass, aerodynamic drag
area and battery size (as an indicator for technological progress).

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The following formula was drawn up:
𝑅𝑊𝐸𝐶

0.00679 ∙ 𝑚

11.09 ∙ 𝐶𝑑𝐴

0.0063 ∙ 𝑐

1.29

With
RWEC = electricity consumption in kWh/100 km
m = mass in running order in kg
CdA = aerodynamic drag area in m2 (Cd*A)
cbattery = gross battery capacity in kWh (values as published by manufacturers)

The CdA-values used to develop the model were taken from manufacturer
brochures and websites, or calculated from a Cd value provided by the
manufacturer and a calculated frontal area. The information is not always official,
and of an unknown accuracy.
For the Tesla Model 3, m is 1830 kg, CdA is 0.52 m2 and c is 75 kWh. Filling these
numbers in the formula results in a predicted average electricity consumption of
20.0 kWh/100 km. Note that also these calculated values are including charging
losses and energy consumption for conditioning the vehicle and battery. The
calculated value can differ from the one in Table 4-1, as a result of relatively
efficient or inefficient technology used in a certain model, but also as a result of
different behaviour of drivers of a certain model compared to the average. For
instance, a larger range gives more freedom to drive faster on the highway. The
average deviation of the model is 8%.
It is interesting to see how well the model predicts the energy consumption of the
three outliers in Figure 4-2. For the Volvo, the model prediction is 11% lower than
the real-world value, and for the Polestar even 16%. For the Smart the model
prediction is close to the real-world value in Figure 4-2; possibly the declared
WLTP value of this vehicle is too high.
The largest deviation of the model from the real-world average was for the
Opel Corsa: the model prediction is 20% lower than the actual real-world value;
for the technically equal (but slightly smaller) Peugeot e-208 it is still 16%.
The relative effect of the battery capacity as an indication of technological
advancement is small. A larger battery increases the real-world electricity
consumption (by a small amount). It is not clear what causes this (the higher mass
of a larger battery is already accounted for in the mass factor), although a larger
battery obviously gives more freedom to drive faster on highway trips.
4.5

Consistency of results
This is the third analysis of electricity consumption of electric vehicles done on the
basis of charge pass data. The amount of available data has increased significantly.
To see if that caused a trend in the results, the average consumption of several
vehicle models was compared for the three reporting years, see Figure 4-3.

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Figure 4-3: Evolution of results for models on the market for a longer period of time. The overall
amount of data in 2021 is 2.8 times higher than in 2020, and 15 times higher than in
2019.

There is no clear upward or downward trend that applies to all models. However,
the most significant changes were downward, for the Hyundai Ioniq and the Renault
Zoe.
4.6

Charging losses
Within the Green Vehicle Index programme8, detailed measurements were done
on a number of electric vehicles. Amongst other things, the total losses were
determined between the (AC) grid and the power output from the battery.
This includes the losses of the inverter and the losses in the battery during charging
and discharging. The results are shown in Table 4-2. Note that the charging speed
was not the same for the three vehicles, so the results are not entirely comparable;
e.g. inverters work most efficient at (close to) their maximum power. Regardless, in
all cases the losses were over 10%.
Table 4-2: Losses of charging plus battery charge/discharge cycle for seven electric vehicle
models.

Vehicle model

Charging + battery cycle
losses
20.8%

Average charging power
(kW)
N/A

Ford Mustang Mach-E

18.3%

N/A

Lexus UX 300e

10.3%

N/A

Nissan Leaf e+
Volkswagen ID.3

16.4%
10.8%

N/A
10.5

Hyundai Kona Electric

13.1%

3.6

Renault ZOE

19.4%

3.7

Fiat 500

With an annual mileage of 15,000 km, a 10% loss equals approximately 9.5 GJ per
year.
8

https://www.gvi-project.eu

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Annual mileage and energy consumption
Vehicles that cover large distances per year are likely to have a higher share of
highway driving, and therefore a higher electricity consumption per km. On the other
hand, when driving short trips or at low speeds, heating and airconditioning use can
increase the energy consumption per kilometre a lot as well. An analysis was made
for the most common vehicles in the database to see if there is a correlation
between annual distance and energy consumption. Figure 4-4 shows the relation
between the average electricity consumption and the annual mileage, for the five
vehicles for which the most data is available in the charge pass database. Vehicles
were grouped per 5000 km, e.g. all Hyundai Kona's with 15,000-20,000 km per year
form one group. A marker is only placed in the graph if such a group has at least 5
observations (vehicles).
The graph reveals that, on average, the electricity consumption decreases with
increasing annual mileages. This means that despite higher average speeds
consumption is slightly lower. The downward trend is more or less constant.

Figure 4-4: Average electricity consumption as a function of annual mileage

The upward effects of high velocity, braking losses in dynamic driving despite brake
energy recovery, and interior heating and cooling (relative) for short trips are
balanced in a certain way to lead to this trend. However, other factors can play a
role as well. Fast DC charging may have smaller total losses in charger and battery
than AC charging. Drivers that cover high annual mileages may optimize their
driving style for range, amongst other things because longer trips occur more
frequently.
4.8

Hydrogen consumption of fuel cell electric vehicles
The tank pass data contains 552 hydrogen fuelling records of 23 fuel cell electric
vehicles (FCEVs). After filtering out unreasonable consumption numbers (smaller
than 0.5 kg/100 km and larger than 5 kg/100 km), the average consumption for all
the vehicles in the database is 1.24 kg/100 km.

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Because the amount of data is limited, no vehicle model specific value can
be provided. In the dataset, the following vehicle models were present:
Hyundai Nexo, Hyundai IX35, Toyota Mirai.
The energy content of hydrogen is 120 MJ/kg. This means that the tanked hydrogen
is equivalent to 1.24 * 120 / 3.6 = 41 kWh/100 km; roughly twice as much as for
battery electric vehicles. The difference is mostly related to the losses in the fuel cell
stack.

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5

Real-world fuel consumption of plug-in hybrid
passenger cars

5.1

Theory
As in the previous reports in this series on ‘real world fuel consumption’, an analysis
has been made of the share of electric driving for plug-in hybrids.
Plug-in hybrids operate in different modes, which are normally chosen by the
vehicle’s software, although a preference can be selected by the user via a button.
The different modes are explained in a slightly simplified way in Table 5-1.
Table 5-1: Modes of operation of plug-in hybrid vehicles (simplified).

Mode

Brief
explanation

Battery

Description

EV mode

Full electric

Charge
depleting

Hybrid
mode

Petrol/diesel, part
of the time with
support of
electric motor

Charge
depleting
(blended)

Vehicle is propelled by the electric
motor only, and behaves like a fullelectric vehicle. All consumed
energy is derived from the battery.
The blended CD strategy gradually
depletes the battery, ideally
choosing the best combination of
electricity and fuel to make optimal
use of the engine when it is needed.
The engine assists the electric
motor when power demand
exceeds the limits of the electric
motor or when the control strategy
deems the use of the engine more
efficient.

Charge
sustaining

After reaching the end of its charge
depleting range, a plug-in hybrid
vehicle will switch to charge
sustaining mode. The mode can
also be selected by the user. In this
mode the vehicle uses regenerative
braking and power from the engine
to keep the battery state of charge
constant. The switch to charge
sustaining operation is triggered by
the battery reaching a specific state
of charge (usually ~20%).This
behaviour is similar to that of a
hybrid vehicle without a plug.

All modes lead to reduced tailpipe CO2 emissions compared to conventional
combustion engine vehicles. The use of energy from the battery, which is charged
from the grid, avoids the use of fuel, thereby avoiding tailpipe CO2 emission.
This happens in EV mode, but also in charge depleting hybrid mode.

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Furthermore, in all hybrid modes, the combustion engine can operate in a more
efficient way than normal because of the interaction with the electric
motor/generator. Lastly, brake energy is recovered in both EV and hybrid mode.
However, from the fuelling data available, it appears that these mechanisms do not
lead to markedly lower fuel consumption and CO2 emissions for most of the current
plug-ins monitored.
If the vehicle is not (often) charged from the grid, the benefits are smaller, and may
not outweigh the negative effects of the additional mass that comes with the plug-in
hybrid system. By analysing the variation in fuel consumption (l/100 km) per tank
event, an estimation can be made of the fuel consumption with an empty battery
(charge sustaining mode), and that information can in turn be used to estimate the
share of full-electric driving (EV mode), and the electricity consumption in that
mode. Since fuel consumption is roughly proportional to mass for an average
vehicle, the 200 kilogram extra for battery and electric motor, i.e., 5% to 15% extra
energy consumption, should be compensated party by efficient engine operation in
hybrid mode, brake energy recuperation, and electric charging. Therefore, benefits
depend on the technology and usage. 9
5.2

Results
In Figure 5-1 the frequency distribution is plotted of the fuel consumption across all
fuelling events of a particular plug-in hybrid model. The shortest distances driven
per litre are related to driving on the combustion engine.

Figure 5-1: The frequency distribution of the distance driven per fuelling event (km/l) of all petrol
plug-in hybrid models with data of more than 5000 fuelling events.

Many plug-in hybrid vehicle models have a typical engine-only fuel consumption of
between 8 and 16 kilometres per litre (12 to 6 litres per 100 km). The exception is
the Toyota Prius Plug-in Hybrid with 20 kilometres per litre.

9

TNO report 2016 R10419, Supporting analysis on real-world light-duty vehicle CO2-emissions.

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On the other hand, the tail of the distribution, i.e. the largest distances per litre, is
related to periods with substantial amounts of electric driving. The longer the tail,
and the more skewed the distribution, the more electric driving can be assumed.
The position of the peak is a good indication of the fuel consumption on the
combustion engine while the total average fuel consumption is derived from the
combined data. From these two results the share of electric driving is deduced.
For the different models the electric share ranges from 12% to 34% of the driven
kilometres, as shown in Table 5-2. Note that the list of models shown is slightly
different from the ones displayed in Figure 5-1.
Table 5-2: The fuel consumption and share of distance driven electrically, deduced from the
distribution of fuel consumption.

Combustion engine
only

Make and model

km/litre

l/100 km

All driving
Share of km
driven
km/litre
electrically

Mitsubishi Outlander

12.3

8.1

15.9

22.3%

Mercedes-Benz C 350 E

11.7

8.5

15.3

23.0%

Volkswagen Golf

13.6

7.4

18.1

24.9%

Audi A3 Sportback E-tron

15.2

6.6

18.5

17.9%

Volvo V60

13.1

7.6

15.8

17.0%

Volvo XC40

11.7

8.5

17.5

33.1%

Opel Ampera

15.9

6.3

23.9

33.6%

Ford C-Max Energi

14.6

6.8

19.2

23.8%

Volkswagen Passat

14.1

7.1

17.8

20.4%

Chevrolet Volt

16.3

6.1

22.1

26.0%

Toyota Prius Plug-in Hybrid

20.1

5.0

22.9

11.9%

Volvo XC90 T8 Twin Engine

9.7

10.3

13.1

25.9%

Volvo V60 (diesel)

14.9

6.7

19.0

21.4%

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44 / 56

Real-world fuel consumption of vans
Despite the fact that electric van sales are rising, light commercial vehicles are in
majority still fitted with a diesel engine. The average diesel consumption was
calculated for 54,061 light commercial vehicles. Note that for the years 2014, 2015
and 2016 fuelling data is available for only a limited fleet of vans (~7,700 vehicles).
Therefore the results per fuelling date for these years are less stable than the
results for the more recent years, and may not have the same market coverage.
The most common annual mileage among vans is 20,000 km/year, and the average
annual mileage is 24,400 km/year, as can be read in paragraph 1.4.
The average empty mass of vans is increasing since 2018 and has continued
increasing in 2020 and 2021, as visible in Figure 6-1.

Figure 6-1: Evolution of average empty mass of vans in the Travelcard database.

A dip in average mass occurs in September 2020. Further analysis points out that
this seems to be related to a problem in the supply chain of one of the major
manufacturers of larger vans.
Vans have been type approved according to the WLTP since September 2018.
The first WLTP-vans that came to the market were small vans with low CO2
emissions, see Figure 6-2. The vans registered in 2020 and 2021 have, on average,
similar real-world CO2 emission levels to the fleet average of the years before. This
can be seen in the graph that is based on NEDC, Figure 6-3. Note that the NEDC
graph contains all vehicles: for newer, WLTP approved vehicles, an NEDC CO2
number had to be provided by calculation as well.

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The graphs show that also for vans the change to WLTP has led to higher type
approval values. The average calculated NEDC value for the latest vehicles was
around 170 g/km, while the corresponding WLTP values are around 200 g CO2/km.

Figure 6-2: Average real-world and type approval CO2 emissions of light commercial vehicles in
the Travelcard fleet that were WLTP type approved. The number of vehicles was
limited at the start of the lines.

Figure 6-3 Average real-world and NEDC-based type approval CO2 emission of all light
commercial vehicles in the Travelcard fleet. The number of vehicles was limited at the
start of the lines.

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The kink in 2017 in Figure 6-3 coincides with the first CO2 emission target for light
commercial vehicles (EU regulation EC/510/2011), of 175 grams per kilometre in
2017, although this 175 g/km-target had already been met in 2014 on a European
level. There is no visible development towards the 2020-target of 147 g/km in the
graphs.
For further analysis, the fuel consumption data for vans is regrouped by the
registration year of the vehicles. For each vehicle the fuel consumption was
averaged over the entire duration it was part of the Travelcard fleet, and the value
was attributed to its year of first registration.
As displayed in Figure 6-4, the average real-world CO2 emissions and the average
WLTP type approval CO2 emissions have both increased from 2018 to 2020.
The gap has increased slightly to 15 g/km.

Figure 6-4: Average real-world CO2 emissions versus the average WLTP type approval values of
new diesel vans, differentiated by the year of registration.

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47 / 56

Effects of weather on fuel consumption
This chapter describes a first exploration of the effects of weather on fuel
consumption on a fleet level, using empirical data.
The effect of ambient temperature on the fuel consumption is well known.
From physical principles it is expected that the effect is most pronounced in the
air drag of the vehicles, related to the air density decreasing with increasing
temperature. This predominantly affects the fuel consumption at higher velocities,
i.e. on motorways, where air drag is responsible for more than half of the fuel
consumption. It can be expected that the effect on air drag is about 3.5% per
10 degrees temperature difference, and about half of that difference as resulting
impact on the overall fuel consumption.10 The effect observed in the data is larger,
as shown in Figure 7-1, which is probably due to additional temperature
dependencies of rolling resistance and traffic. Typically, in colder weather with
precipitation the car is also used more for shorter trips, leading to more congestion,
than in warmer weather where walking or cycling is more common as an option.
With the type of data that is available, it is difficult to establish a causal relationship
between temperature and fuel consumption. The multi-regression analyses show
the correlation, but the changes in fuel consumption may also be caused indirectly,
i.e. by the different use of vehicles and changes in driving behaviour due to the
season or due to ambient conditions. In order to provide some additional evidence
to the observed effects, decomposed in the multi-regression analyses, also the
relations between the individual parameters (themselves cross-correlated) and the
fuel consumption are given hereafter.

Figure 7-1: The deviation of the fuel consumption from the average for different average day
temperatures, for petrol and diesel vehicles in the Travelcard database.

10

Correction algorithms for WLTP chassis dynamometer and coast-down testing, TNO report,
2015, R10955 and Supporting analysis on real-world light-duty vehicle CO2-emissions, TNO
report 2016 R10419.

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Besides temperature, other aspects of the weather that may affect the fuel
consumption are analysed with KNMI data for the period 2004 to 2021, using mainly
day average values, and the weather of three days before the fuelling, as a
midpoint of the period of use given the typically weekly fuelling of these vehicles.
In the meteorological data the parameters that are correlated with the fuel
consumption data are: daily mean temperature, daily mean wind speed, minimum
day temperature, maximum day temperature, solar irradiation, and daily
precipitation.
From multi-regression analyses of meteorological data with the variation in fuel
consumption over time, the effects can be estimated from the data. The use of
maximum and minimum temperatures is found to have limited additional value over
the mean, and precipitation shows little correlation with fuel consumption.
The dominant effect is ambient temperature, with a 0.28% decrease in fuel
consumption with 1 degree higher temperature for petrol vehicles, as shown in
Figure 7-1. For diesels the effect is -0.17% per degree ambient temperature
increase.
In magnitude the second effect is wind. With a 1 m/s higher average wind speed
the increase in fuel consumption is 0.30% for petrol vehicles and 0.25% for diesels.
The average wind speeds are 3.3 metres per second, with a variation of 1.3 metres
per second. If wind speeds are much higher the effect is larger. From Figure 7-2 it
can be inferred that above 4 m/s average wind speed the effects are increasing
significantly. The difference between petrol and diesel vehicles is smaller for wind
than for temperature, indicating either a difference in vehicle use and size, or a
limited (average) difference in engine efficiency between petrol and diesel engines
when driving at higher velocities, where wind effects play a major role.

Figure 7-2: The deviation of the fuel consumption from the average for different average wind
speeds. The relation clearly shows the quadratic dependency with wind speed that is
expected from physical principles.

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The third effect in magnitude is the solar irradiation, in J/cm2 (1017 +/- 783).
For an increase in solar irradiation of 100 J/cm2 the fuel consumption of petrol cars
decreases by 0.085%. For diesels the effect is 0.039% per 100 J/cm2 solar
irradiation increase. The effect of solar irradiation shows a similar, yet slightly
different trend than the effect of ambient temperature. See Figure 7-3.
Since the fuelling cannot be related directly to the time and place of driving the
vehicle and the associated local and temporal weather variations, these results are
based on the daily averages at the national level, and therefore the correlations
between the conditions and the fuel consumption are limited. If more specific
information on weather conditions per vehicle were available, the effects would
probably be somewhat larger, possibly up to two times.

Figure 7-3: The deviation of the fuel consumption from the average for different daily levels of
solar irradiation. The solar irradiation is strongly correlated with ambient temperature.
However, it can be noted that at the highest solar irradiation the improvement in fuel
efficiency is limited.

The analysis shows that ambient conditions cause substantial variations in fuel
efficiency. This explains a large part of the seasonal variations, as e.g. shown in
Figure 2-1 and Figure 2-2. In addition it is expected that changes in vehicle use
and traffic over the year also affect the fuel consumption. Overall the fuel
consumption is found to vary between -4% and 4% around the yearly average.
Corrected for the known average weather effects, the remaining variation is
between -2.5% and 2.5%.See Figure 7-4. In the latter some seasonal effects may
be expected to remain in that variation, probably because the extremes are higher
than the averages used in the correlation, but a large share of the remaining
variation is likely to depend on factors not directly related to weather conditions,
like vehicle usage, fuel composition, tyres.

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Figure 7-4: After the correction for impacts of daily mean temperature, average wind speed, and
irradiation, some seasonal effects in the fuel consumption of petrol cars remain,
especially for the years after 2016..

It seems that modern petrol vehicles, registered after 2016/17, have larger
remaining seasonal dependencies (after correction for variations in average
temperature, wind speed and solar irradiation), than earlier generations.
See Figure 7-4. It can also be that in the earlier period 2004 to 2012 other effects
like heavy congestion limit these correlations.
Concluding, just under half of the seasonal variation in fuel consumption on a fleet
level can be described (predicted) using information about temperature, wind and
solar irradiation. A part of this effect is secondary, due to changes in vehicle use
and driving style as a result of changes in weather conditions. The remaining
variation can have many causes, including nonlinear effects of weather not explored
yet, and seasonal variation in fuel composition and tyres. A detailed investigation for
specific groups of vehicles and for specific conditions can give more understanding
of the relation between fuel consumption and each of the influencing factors.
This can lead to better predictions of real-world fuel consumption in the future.

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The transitions to E5 and E10 petrol
In the long-term-trends in fuel consumption, seasonal effects dominate, and over
the years changes in these seasonal effects can be observed. The underlying
causes for these changes are not easily isolated, because the seasonal variation in
fuel consumption is also affected by severe winter cold, economic situation, and
congestion levels that change from year to year. To isolate factors of interest such
as the 100 km/h speed limit introduction and the Corona pandemic proved to be
difficult. See Figure 8-1. From the figure it could be inferred that fuel consumption in
2020 and 2021 is about 1% to 2% lower than before in 2017 and 2018. However,
something similar happened in the period 2013 – 2016.

Figure 8-1: The seasonal changes in fuel consumption on a fleet level. For every individual vehicle
in the Travelcard fleet the time dependent deviation from its long-term average fuel
consumption was calculated, and averaged.

By contrast, the effect of E10 on fuel consumption can actually be isolated, by
looking at the long-term trends in the differences in seasonal variation between
petrol and diesel vehicles. Other influences than E10, such as weather, may have
a different amount of effect on diesels than on petrols, but the trend is the same,
because they are exposed to the same on-road conditions. Figure 8-1 shows
indeed that for vehicles of the two fuel types the seasonal trends are roughly the
same, albeit less pronounced in the case of diesel. A correlation analysis points out
that, on average, the seasonal variation for petrol is 162% of the variation for diesel.
Knowing that, the actual fuel consumption of petrol cars can be plotted relative to
a predicted fuel consumption of petrol cars using the expected seasonal variation
of petrol (162% of the variation of diesel). This way a structural change in fuel
consumption can be discovered. This is done in Figure 8-2. It can be noted that
from the summer of 2020 onwards the actual petrol consumption is constantly and
systematically above the average, and hovers around +2% compared to the
average for the entire period.

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It is likely that this is due to the introduction of E10 fuel. In 2005 there seems to be
a similar step upward, possibly related to the introduction of E5 that occurred
around that time; however, since the dataset starts in 2004, the preceding period is
too short to draw a definite conclusion.

Figure 8-2: The changes in petrol fuel consumption relative to the changes in diesel fuel
consumption. Both in autumn 2005 and summer 2020 there seems to be a jump
upwards of about 2%.

Very likely the higher petrol consumption from the summer of 2020 can be linked to
the introduction of E10 fuel in the Netherlands. The variations in fuel consumption
are less than the jump in 2020. See Figure 8-2. If this effect is to be incorporated in
the fuel consumption and CO2 emission analyses in the future, it will require some
further study of fuel properties, like density and carbon content, to establish the new
relation between these aspects.11

11

Petrol fuel and blending ethanol analyses, TNO report 2020 R10138, and Dutch market fuel
consumption for GHG emissions, TNO report 2016 R10700.

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Conclusions and discussion
The real-world CO2 emissions and energy consumption of passenger cars and
light commercial vehicles were studied from different angles. Fuel consumption
monitoring data was primarily obtained from Travelcard B.V., and augmented for
the analysis with data from RDW and the TNO contribution to the LIFE-MILE21
project.
Petrol and diesel passenger cars
The type approval CO2 emission values of newly registered vehicles in the
Travelcard database in 2020 are lower than those registered in 2019, while the
real-world emissions have remained stable for petrol cars, and have slightly
increased for diesel cars. The increase for diesels is likely to be related to the fast
increase of empty weight of the diesels sold, which coincides with a strong
decrease in sales. At the same time, the WLTP type approval values have
decreased slightly.
WLTP values remain closer to real-world emissions than the old NEDC values
were. The result of the trends is that the real-world-to-WLTP gap has increased,
and approaches 20 g CO2 per km for both diesel cars and petrol cars (both
excluding plug-in hybrids); thereby the gap is 13%. If plug-in hybrids are included,
the gap for petrol vehicles is close to 25 g/km or 17%.
For the vehicles in the Travelcard database the average real-world CO2 emission of
new passenger cars in 2020 was 156 g/km for petrol including (plug-in) hybrids, and
164 g/km for diesel including (plug-in) hybrids. New petrol plug-in hybrids in 2020
had an average real-world CO2 emission of 147 g/km: a gap of 240% to the
average WLTP type approval value of 43 g/km.
An analysis was made of the fuel consumption per tonne of empty vehicle weight,
to cancel out the changes in average mass over time. This was done for new
registrations. For petrol cars, this fuel efficiency (litre per 100 km per tonne) has
improved considerably for the lightest cars, over the last 10 years. For heavier
petrol cars, including plug-in hybrids, as well as for diesel passenger cars, the
improvement is limited.
Electric passenger cars and hydrogen fuel cell passenger cars
For electric passenger car models the average electricity consumption was
calculated from charge pass records and odometer registrations. The numbers
include charging losses. The most efficient models consume 16 kWh per 100 km,
the least efficient up to 30 kWh/100 km. The values have decreased quite a bit for
some models. Possible reasons include the fact that the amount of data has
doubled compared to the previous report, changes in the speed limit on the highway
and potential effects of the lockdown. The Travelcard fleet average has not
changed over time: 20.2 kWh/100 km. Reweighted to the Dutch EV fleet
composition does not change this number much: 20.1 kWh/100 km.
The gap of these real-world numbers with the WLTP declared value is 19% on
average, the real-world number being higher. Some models are very close to, or
even below WLTP-value, while others have an upward deviation of 40% or more.

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A prediction model was made to estimate the real-world electricity consumption of
battery electric cars. It is based on mass, air drag and battery capacity, and is fitted
to the actually observed electricity consumption averages in the abovementioned
dataset. The average deviation of the model is 8%, and the maximum is 20%, so
the model is a better predictor than the WLTP value.
Data on charging losses from other projects point out that these are significant, in
the order of 10-20%.
The relation between annual driven distance and electricity consumption
(kWh/100 km) was investigated. Although driving on the highway is relatively
energy consuming for electric cars, and high mileage usually means a high share of
highway driving, the trend is the other way. The higher the annual distance, the
lower the average energy consumption. At 40,000 km/year the five most common
car models in the dataset have an electricity consumption that is around 12% lower
than for vehicles of the same models which are driven 10,000 km/year. It cannot be
derived from the data why this occurs. Possibly experienced drivers optimize their
driving style, or the lower amount of braking losses offsets (partially) the extra
energy needed to drive higher speeds. The mode and speed of charging might play
a role as well, through differences in losses.
For fuel cell electric vehicles for the first time an average real-world hydrogen
consumption value was calculated based on tank pass data for 23 vehicles.
The hydrogen consumption was found to be 1.24 kg/100 km. In terms of energy
content in hydrogen this is roughly twice as much as the energy in charged
kilowatt-hours for battery electric vehicles.
Plug-in hybrid passenger cars
The real-world fuel consumption data of plug-in hybrids was analysed over time to
derive an indication of the share of electrically driven kilometres. The results have
not changed significantly since the previous analysis: the electric share ranges from
12% to 34% of the driven kilometres.
Light commercial vehicles
The first trend observed for light commercial vehicles is that the empty mass of new
vehicles increases quickly: 6 or 7% in two years’ time. In the same time, the fuel
efficiency (litre diesel per 100 km per tonne of empty vehicle weight) has improved
considerably as well: around 1.25% per year, ever since 2010.
The WLTP regulation reduced the type approval-to-real-world gap for vans as well.
However, the numbers are not stable yet because of limited WLTP-approved
vehicles in the dataset. The real-world average CO2 emissions seem unchanged at
around 220 g/km.
Weather effects
Almost half of the seasonal variation in fuel consumption can be predicted using
variations in temperature, wind and solar irradiation, whereby the three factors are
ordered by magnitude of contribution. A part of the effect is indirect, by changes in
the way people use their vehicles and changes in driving behaviour as a result of
the variation in temperature, wind and sun.

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The remaining variation can have many causes, including nonlinear effects of
weather that were not explored yet, and seasonal variation in fuel composition and
tyres.
Fuel composition
The effect of an increasing admixture of ethanol in petrol was estimated by
analysing the trends in seasonal variation in fuel consumption between petrol and
diesel cars. The petrol vehicles on average showed an increase in fuel consumption
of 2% ever since summer 2020, after compensation for the expected seasonal
variation. This increase can most likely be attributed to the introduction of E10.
To incorporate the effect of E10 in the analyses of real-world CO2 tailpipe
emissions, further study of fuel properties, including density and carbon content and
variations therein, will be required.

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Signature
The Hague, 4 March 2022

TNO

Project manager

Author