ISO 23795-1:2022

Intelligent transport systems - Extracting trip data using nomadic and mobile devices for estimating C02 emissions - Part 1: Fuel consumption determination for fleet management

This document specifies a method for the determination of fuel consumption and resulting CO2 emissions to enable fleet managers to reduce fuel costs and greenhouse gas (GHG) emissions in a sustainable manner. The fuel consumption determination is achieved by extracting trip data and speed profiles from the global navigation satellite system (GNSS) receiver of a nomadic device (ND), by sending it via mobile communication to a database server and by calculating the deviation of the mechanical energy contributions of: a) aerodynamics, b) rolling friction, c) acceleration/braking, d) slope resistance, and e) standstill, relative to a given reference driving cycle in [%]. As the mechanical energy consumption of the reference cycle is known by measurement with a set of static vehicle configuration parameters, the methodology enables drivers, fleet managers or logistics service providers to calculate and analyse fuel consumption and CO2 emissions per trip by simply collecting trip data with a GNSS receiver included in an ND inside a moving vehicle. In addition to the on-trip and post-trip monitoring of energy consumption (fuel, CO2), the solution also provides information about eco-friendly driving behaviour and road conditions for better ex-ante and ex-post trip planning. Therefore, the solution also allows floating cars to evaluate the impact of specific traffic management actions taken by public authorities with the objective of achieving GHG reductions within a given road network. The ND is not aware of the characteristics of the vehicle. The connection between dynamic data collected by the ND and the static vehicle configuration parameters is out of scope of this document. This connection is implementation-dependent for a software or application using the described methodology which includes static vehicle parameters and dynamic speed profiles per second from the ND. Considerations of privacy and data protection of the data collected by a ND are not within the scope of this document, which only describes the methodology based on such data. However, software and application developers using the methodology need to carefully consider those issues. Nowadays, most countries and companies are required to be compliant with strict and transparent local regulations on privacy and to have the corresponding approval boards and certification regulations in force before bringing new products to the market.

Systèmes de transport intelligents — Extraction des données de voyage via des dispositifs nomades et mobiles pour l'estimation des émissions de CO2 — Partie 1: Détermination de la consommation de carburant pour la gestion de la flotte

General Information

• Status: Published
• Publication Date: 30-May-2022
• ICS: 13.020.40 - Pollution, pollution control and conservation
• ICS: 13.040.50 - Transport exhaust emissions
• ICS: 35.240.60 - IT applications in transport
• ICS: 43.040.15 - Car informatics. On board computer systems
• Technical Committee: ISO/TC 204 - Intelligent transport systems
• Drafting Committee: ISO/TC 204/WG 17 - Nomadic Devices in ITS Systems
• Current Stage: 6060 - International Standard published
• Start Date: 31-May-2022
• Due Date: 14-Jan-2023
• Completion Date: 31-May-2022

Overview

ISO 23795-1:2022 - "Intelligent transport systems - Extracting trip data using nomadic and mobile devices for estimating CO2 emissions - Part 1: Fuel consumption determination for fleet management" specifies a method to determine fuel consumption and resulting CO2 emissions by extracting per‑second speed profiles from a GNSS receiver inside a nomadic device (ND). Speed data are sent via mobile communication to a server where the mechanical energy contributions of aerodynamics, rolling friction, acceleration/braking, slope resistance and standstill are compared, in percent, to a defined reference driving cycle. The approach enables fleet managers and logistics providers to estimate fuel use and emissions without vehicle‑mounted sensors.

Key topics and technical requirements

• Data source: GNSS speed profiles collected by a nomadic device; per‑second sampling is assumed.
• Client‑server architecture: ND transmits dynamic trip data to a database/server for analysis and comparison with reference cycles.
• Energy decomposition: Fuel/energy estimation based on Newtonian physics breaking down mechanical energy into:
  • Aerodynamics
  • Rolling friction
  • Acceleration/braking (inertial forces)
  • Slope resistance
  • Standstill (idling)
• Reference comparison: Deviations from a virtual vehicle driving a known reference cycle (e.g., WLTP or other established cycles) are expressed as percentage deviations and converted to fuel units (vLPH, litres per 100 km equivalent).
• Model inputs: Static vehicle configuration parameters (mass, drag coefficient, cross‑sectional area, tyre friction, etc.) are required on the server side - the ND itself is not vehicle‑aware.
• Mathematical basis: Energy and fuel equations (fuel per distance, conversion to litres/100 km) using engine efficiency, fuel energy value and physical constants.
• Out‑of‑scope items: The method does not cover how dynamic ND data are linked to static vehicle parameters (implementation‑dependent) and does not address legal/privacy requirements for collected data.

Applications

• Fleet management: Trip‑level fuel and CO2 monitoring, benchmarking and cost reduction.
• Eco‑driving coaching: Real‑time or post‑trip feedback to drivers to improve driving behaviour.
• Logistics optimization: Route planning and driver performance KPIs based on fuel use estimates.
• Traffic and policy evaluation: Floating car measurements to assess the impact of traffic management for GHG reduction.
• Public‑private R&D deployment: Integrates with mobility projects and telematics platforms for large‑scale monitoring.

Who should use this standard

• Fleet operators and logistics service providers
• Telematics and mobility‑software developers
• Public transport authorities and urban planners
• Eco‑drive trainers and sustainability managers

Practical notes & limitations

• Implementation must supply accurate static vehicle parameters on the server for valid estimates.
• Developers must handle privacy, consent and local data‑protection compliance (not covered by ISO 23795-1:2022).
• Best for applications where non‑intrusive, GNSS‑based monitoring is preferred over vehicle‑integrated sensors.

Related standards

• WLTP (Worldwide harmonized Light vehicles Test Procedure) - reference driving cycles
• ISO 13111‑1 and ISO 13185‑3 (related ITS terminology and privacy concepts)
• ISO/TC 204 publications on intelligent transport systems

Keywords: ISO 23795-1:2022, intelligent transport systems, GNSS, nomadic device, fuel consumption determination, CO2 emissions, fleet management, speed profiles, eco‑driving.

Standards Content (Sample)

INTERNATIONAL ISO
STANDARD 23795-1
First edition
2022-05
Intelligent transport systems —
Extracting trip data using nomadic
and mobile devices for estimating
CO emissions —
Part 1:
Fuel consumption determination for
fleet management
Systèmes de transport intelligents — Extraction des données de
voyage via des dispositifs nomades et mobiles pour l'estimation des
émissions de CO —
Partie 1: Détermination de la consommation de carburant pour la
gestion de la flotte
Reference number
© ISO 2022
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
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or ISO’s member body in the country of the requester.
ISO copyright office
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Phone: [phone]
Email: [e-mail]
Website: www.iso.org
Published in Switzerland
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 2
4 Abbreviated terms . 2
5 Method for fuel consumption determination for fleet management .3
5.1 Introduction . 3
5.2 Conventions of applied Newtonian physics . 3
5.3 Explanation . 5
5.4 Relevance of energy equation for nomadic devices . 5
5.5 Example of the presented methodology . 6
Annex A (informative) Concept for implementation.10
Annex B (informative) Application of the method in a use case study — Examples and
results .20
Bibliography .31
iii
Foreword
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different types of ISO documents should be noted. This document was drafted in accordance with the
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www.iso.org/iso/foreword.html.
This document was prepared by Technical Committee ISO/TC 204, Intelligent transport systems.
A list of all parts in the ISO 23795 series can be found on the ISO website.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html.
iv
Introduction
This document has been established to define the monitoring of energy consumption based on
measured speed profiles from a vehicle in motion compared to a virtual vehicle driving with defined
speed reference cycles.
The service uses in-vehicle nomadic and mobile devices and a client server architecture where the
dynamic speed profile per second is evaluated with fixed vehicle configuration parameters inside the
server. With the near real-time communication between the nomadic device (ND) and the server, the
results of the calculation can also be made visible to the driver during the trip for eco-drive purposes.
The application allows NDs to become a measurement tool for quantifying the energy contributions
and inertia forces of a moving vehicle in units of [%] relative to the virtual vehicle moving along the
reference cycles.
This document can be used by fleet operators, logistic service providers, public transport operators
and eco-drive trainers to develop applications which allow the measurement (in units of [%]) of the
energy consumption in litres of gasoline or diesel equivalent (in joules or kWh), relative to the energy
consumption of a given standard vehicle.
The methodology also optimizes carbon emission calculations using standard energy consumption
without being calibrated to the real trip behaviour of a moving vehicle. This solution has been
successfully implemented in the public-private partnership research and development (R&D) projects
listed in Table 1:
Table 1 — List of public-private partnership R&D projects
Name Full name Duration
LCMM Low Carbon Mobility Management co-funded by the: Federal 2010 - 2014
Ministry for Economic Cooperation and Development
https://energypedia.info/wiki/Emission_Data_Monitoring_Technology
AEOLIX Architecture for European Logistics Information eXchange 09/2016 – 08/2019
https://aeolix.eu/
CO-GISTICS Deploying Cooperative Logistics 01/2014 – 05/2016
https://cogistics.eu/
ESA European-wide mobility, safety and efficiency management for 12/2013 – 01/2017
logistics enterprises
https://business.esa.int/projects/eu-wide-mobility-safety-efficiency-management-logistics
v
INTERNATIONAL STANDARD ISO 23795-1:2022(E)
Intelligent transport systems — Extracting trip data
using nomadic and mobile devices for estimating C0
emissions —
Part 1:
Fuel consumption determination for fleet management
1 Scope
This document specifies a method for the determination of fuel consumption and resulting CO
emissions to enable fleet managers to reduce fuel costs and greenhouse gas (GHG) emissions in a
sustainable manner. The fuel consumption determination is achieved by extracting trip data and
speed profiles from the global navigation satellite system (GNSS) receiver of a nomadic device (ND),
by sending it via mobile communication to a database server and by calculating the deviation of the
mechanical energy contributions of:
a) aerodynamics,
b) rolling friction,
c) acceleration/braking,
d) slope resistance and
e) standstill,
relative to a given reference driving cycle in [%]. As the mechanical energy consumption of the reference
cycle is known by measurement with a set of static vehicle configuration parameters, the methodology
enables drivers, fleet managers or logistics service providers to calculate and analyse fuel consumption
and CO emissions per trip by simply collecting trip data with a GNSS receiver included in an ND inside
a moving vehicle. In addition to the on-trip and post-trip monitoring of energy consumption (fuel, CO ),
the solution also provides information about eco-friendly driving behaviour and road conditions for
better ex-ante and ex-post trip planning. Therefore, the solution also allows floating cars to evaluate the
impact of specific traffic management actions taken by public authorities with the objective of achieving
GHG reductions within a given road network.
The ND is not aware of the characteristics of the vehicle. The connection between dynamic data
collected by the ND and the static vehicle configuration parameters is out of scope of this document.
This connection is implementation-dependent for a software or application using the described
methodology which includes static vehicle parameters and dynamic speed profiles per second from the
ND.
Considerations of privacy and data protection of the data collected by a ND are not within the scope
of this document, which only describes the methodology based on such data. However, software and
application developers using the methodology need to carefully consider those issues. Nowadays, most
countries and companies are required to be compliant with strict and transparent local regulations on
privacy and to have the corresponding approval boards and certification regulations in force before
bringing new products to the market.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1
nomadic device
device that provides communications connectivity via equipment such as cellular telephones, mobile
wireless broadband (WIMAX, HC-SDMA, etc.), WiFi, etc. and includes short range links, such as
Bluetooth, Zigbee
Note 1 to entry: Nomadic devices do not necessarily implement ITS-specified security, e.g., hardware security
module.
[SOURCE: ISO 13111-1:2017, 3.1.14 — modified. Definition shortened and Note 1 to entry added.]
3.2
privacy
choice made by the vehicle owner to grant information access for a special tool or user, or if the data
should be used in the vehicle/off-board systems or not
Note 1 to entry: The privacy/authorization information is kept as master information off-board and synchronized
to the on-board V-ITS-S.
[SOURCE: ISO 13185-3:2018, 3.4]
4 Abbreviated terms
Abbreviated term Definition
API acceleration performance index
EPI energy performance index
EUDC extra-urban driving cycle
GHG greenhouse gas
GNSS global navigation satellite system
GPS global positioning system
HC-SDMA high capacity - spatial division multiple access
HDT heavy duty truck
HDV heavy duty vehicle
IoT internet of things
ITS intelligent transport system
ITS-S ITS-station
KPI key performance indicator
LCMM low carbon mobility management
LCV light commercial vehicle
LPH litre per 100 km
ND nomadic device
R&D research and development
STS standstill
UDC urban driving cycle
Abbreviated term Definition
V-ITS-S vehicular and personal ITS station
vLPH virtual litre per 100 km based on the [%] deviation of
real and reference speed profiles
WiFi wireless ethernet (technology based on IEEE 802.11
standards)
WiMAX worldwide interoperability for microwave access
WLTP worldwide harmonized light vehicles procedure
5 Method for fuel consumption determination for fleet management
5.1 Introduction
To implement the method for fuel consumption determination for fleet management as specified in the
present document, the descriptions in the subsequent subclauses shall be respected. The informative
Annex A presents a concept for the implementation of the method.
5.2 Conventions of applied Newtonian physics
This document is based on the conventions of applied Newtonian physics in the context of the energy
and fuel consumption equation as described in detail in Reference [6].
Formula (1) shows the distance consumption of fuel, Φ, in grams per metre (g/m) for vehicles either
in motion with speed, v, at v > 0 or standing still with v = 0, which can easily be converted to litres
per 100 km considering that 1 litre of diesel weighs between 820 and 845 grams and 1 litre of petrol
around 750 g:
ΦΦ=+Φ (1)
vid
where
Φ is fuel consumption in motion with speed v ≥ ε and ε ≈ 1 in metres per second (m/s);
v
Φ is idling fuel consumption with speed ranges defined by ε > v ≥ 0.
id
The calculation of Formula (1) includes Formulae (2) - (7) for inertial forces F , F , F , F , F (see 5.3),
A B C D E
defined as follows:
T
()FF++FF++FvΔt
AB CD E
∫
Φ =ηb (2)
ve
′
T
vtΔ
∫
Δv
Fm=∗ ,0Δv> (3)
A
Δt
Δv
Fm=∗β ,0Δv< (4)
B
Δt
ρ
FA=∗ '∗cv (5)
CW
Fm= gμ (6)
D
Fm=∗g sin()α (7)
E
where symbols are as follows:
η is the numerical value of the engine efficiency, expressed in percent (%);
b is the numerical value of fuel value, expressed in grams per kilowatt hour (g/kWh);
e
T is the numerical value of the driving time, expressed in seconds (s);
T’ is the numerical value of the driving time to reach the reference distance, usually 100 kilometres,
expressed in seconds (s);
v is the numerical value of the speed, expressed in metres per second (m/s);
Δt is the numerical of the time interval per second applied for the integral in Formula (1), expressed
in seconds (s);
Δv is the numerical values of changing speed of the vehicle from one second to the next defining
positive and negative acceleration according to Newtonian Physics, expressed in metres per
second (m/s);
β is the numerical value of the propulsion, expressed with no units;
where the vehicle constants are as follows:
m is the numerical value of the total weight of the vehicle, expressed in kilogram (kg);
A’ is the numerical value of the cross-sectional area, expressed in metres squared (m );
c is the numerical value of the drag coefficient, expressed without units;
w
where the constants describing road conditions are as follows:
α is the numerical value of the slope angle, expressed in degree (°);
μ is the numerical value of the friction coefficient, expressed without units;
where the physical constants are as follows:
ρ is the numerical value of air density, expressed in kilograms per metre to the power of three
(kg/m );
g is the numerical value of acceleration of gravity, expressed in metres per second squared (m/s ).
The proposed method is based on the innovation that GNSS receivers detect speed profiles per second
and on the assumption that all constants used in Formula (1) have the same numerical values for the
vehicle in motion and the virtual vehicle. This addresses also idle creep, where any overestimation of the
true zero velocity fuel consumption is inconsequential to the practical zero velocity fuel consumption,
meaning that any constant error introduced into the variable equation by idle creep can be reasonably
mitigated by the comparison to the virtual vehicle model. Due to small fluctuations of GNSS speed
signals, zero-values of speed were defined in Formula (1) in the numerical range of 0 to 1 of speed
values expressed in metres per second (m/s).
On the other hand, the proposed method includes the following pre-conditions when analysing trips of
vehicles in motion by detecting speed profiles with the GNSS receiver of an ND:
— All vehicle configuration constants used in Formula (1), particularly weight, tyre pressure, cross-
sectional area and drag coefficient, are time-independent in driving cycle and real-trip data;
— all constants describing road surface conditions are time-independent and, therefore, shall be
identical with regards to cycle and real-trip data;
— all constants describing physical constants, especially air density, are time-independent and
therefore shall be identical with regards to cycle and real-trip data;
— fuel consumption when idling, introduced in Formula (1) as Ф , is defined in units of litres per
id
seconds (l/s). Calculation is triggered by the GNSS receiver whenever speed values are smaller than
a given threshold, usually 1 m/s. By comparing the standstill time of the real trip to the one of the
worldwide harmonized light vehicles procedure (WLTP) cycle, a percentage deviation results which
can be used for expressing the influence of standstill. Usually, established driving cycles such as
WLTP neglect the influence of slope resistance and downhill forces, inertial forces which impact
fuel consumption in mountainous road networks. Therefore, slope work in Formula (1) cannot be
measured in percentage relative to a given driving cycle.
The application of these pre-conditions leads to the following conventions for fleet managers using the
described method:
— the above constants shall be identical in driving cycle and real-trip data; otherwise, the fuel
determination and percentage of energy deviation measured per trip by the nomadic device become
invalid;
— any usage of the methodology shall rely on the correct start and stop definition of a trip, i.e. the
method becomes invalid when a detected speed profile per trip includes mileage of different
vehicles, but vehicle configurations of only one vehicle;
— when changing load and vehicle weight in a trip (e.g. in logistics operation), trip data has to be
adjusted with the corresponding start and stop functions or an average load, such as the ones used
for sustainability reports, and GHG emissions shall be applied.
5.3 Explanation
As stated in Formula (1) and according to the laws of Newtonian physics, the energy consumption of
any vehicle travelling in space and time is separated by a part for motion (v > 0) and a second part for
standstill (v = 0). According to Newton, the need for energy then results from inertial forces opposing
the motion of the vehicle including the energy demand for accelerating the vehicle [Formula (3)] or
energy losses caused by braking [Formula (4)] as well as aerodynamic [Formula (5)] and rolling friction
resistance [Formula (6)]. Additionally, there is energy needed to drive uphill as well as there can be
energy gained and/or lost while driving downhill [Formula (7)], which is caused by slope resistance
and slope down forces.
By integrating all mentioned forces along a given trip distance, an energy value in joule results which
has to be transferred into fuel with the unit of litre or electric power with the unit of kWh. The
parameter in the energy equation is given by the fuel value b .
e
Finally, this value has to be multiplied by the engine efficiency, ƞ, depending on the different types
of engines, e.g. combustion, biofuel or electric. Usually, the fuel consumption is referred to the given
distance of 100 km, resulting in the well-known units of litres per hundred kilometre or kilowatt-hours
per hundred kilometres.
5.4 Relevance of energy equation for nomadic devices
As found in several field trials since 2010 (see References [4], [8] and [11]) the parameters in the energy
equation can be separated into a dynamic function of speed and a static function of fixed vehicle
configuration parameters combined with parameters describing the road characteristics as well as
with physical parameters.
Under the assumption that all parameters of the static function are constant system configuration
values, the dynamic speed profile per second becomes the dominant influence factor for analysing
energy demand and fuel consumption which are directly linked to the carbon emissions per trip. On the
other hand, the speed profile per second is detected by any GNSS satellite receiver of a nomadic device
and is therefore available for fuel and emission monitoring. Additionally, the satellite receiver examines
the geographical location, exact time, height and direction in order to evaluate trip data ex-post, e.g. on
a digital map.
To minimize the errors in calculating energy consumption and carbon emissions resulting from the
assumption of static parameters, it was found in several field trials that the best quality results are
achieved when comparing the inertial forces from Formulae (2) - (7) to speed reference cycles, e.g.
driving constant speed, ECE or WLTP.
5.5 Example of the presented methodology
[8]
To give a simple example on how to use real on-trip speed profiles relative to the reference cycles,
consider a vehicle in motion driving 90 km/h constantly without acceleration, slope resistance or
standstill. For a plain road network with slope α = 0 and constant speed with no acceleration events,
this can be summarized as shown in Formula (8):
E FF+ ∗∫vt∗ΔΔ/ x
()
M,i DC,,ii i
= (8)
E
FF+ ∗∫vt∗ΔΔ/'x
()
M,o
DC,,oo o
where
E is the energy demand of the vehicle i in motion, expressed in joules (J);
M,i
E is the energy demand of the virtual reference vehicle o, expressed in joules (J);
M,o
F is the inertial force of rolling friction acting on the vehicle in motion with index i and o according
D
to those of E , expressed in units of Newton (N);
M
F is the inertial force of aerodynamic acting on the vehicle in motion with index i and o according
C
to those of E , expressed in units of Newton (N);
M
Δx is the distance travelled by the vehicle in motion, expressed in units of metres (m);
Δx’ is the distance travelled by the virtual reference vehicle, expressed in units of metres (m).
All other symbols and definitions are used according to those of Formula (2). To show the usefulness
for fleet operators and drivers with regards to analysing fuel and CO an illustration is given for the
very simple case of comparing a virtual vehicle driving constant speed of 90 km/h with a real vehicle
driving constantly at 100 km/h, with both vehicles integrated according the Formula (8) in a given time
frame of 200 seconds; see Figure 1.
Key
X time (s)
Y speed (km/h)
Figure 1 — Constant driving cycle 90 km/h compared to real speed 100 km/h (y-axis) versus
the time window of 200 s (x-axis)
The force for the rolling resistance, simplifying it in Formula (1) into one term for the entire vehicle, is
defined as in Formula (9):
Fm=⋅g μ (9)
D
with mass, m, in (kg), gravity constant, g, equal to 9,81 m/s and μ as a dimensionless rolling coefficient
which is usually expressed in the numerical value 0,015 for private passenger cars. For a private car
with weight 1 305 kg, this gives a rolling friction force of 192 N. For the real speed profile, this does not
change if the mass does not change and is assumed to be constant.
Additionally, the definition for the aerodynamic resistance is given using Formula (10):
ρ v
 
Fc=⋅A' (10)
 
C w
23,6
 
where all definitions follow those given in Formula (5).
2 3
For a normal, middle-sized passenger car with cross-section A=2,48 m , c =0,26 and ρ=1,204 kg/m ,
w
this results in a total force of 243 N while driving at 90 km/h and 300 N when driving at 100 km/h.
Assuming the very simple reference cycle of driving constant speed of 90 km/h for 200 s, the proposed
measurement methodology shall be described by inserting the above vehicle parameter into Formula (8).
For constant speed, Formula (8) reduces to a percentage relation of forces; see Formula (11)
E ()100
()192+300
M,i
= =113 (11)
E 90 192+243
() ()
Mo,
where the final value is expressed in percentage (%).
Formula (11) shows that the given private passenger car driving at 100 km/h needs 13 % more acting
force on the axis than the same car driving at 90 km/h. If the fuel consumption for the reference
cycle is known, in the present case of 90 km/h, then Formulae (8) to (12) shall be considered as an
indirect measurement tool analysing relative fuel consumption and CO emissions. Results are shown
in Figure 2, where it is observable that driving with speed ranging between 80 km/h and 90 km/h
decreases energy demand, while driving with speed ranging between 90 km/h and 160 km/h increases
demand, with double energy when driving at 150 km/h.
Key
X speed, km/h
Y energy, % rel. 90 km/h
Figure 2 — Energy and fuel demand increase in percentage relative to the energy demand
driving at 90 km/h with percentage increase (y-axis) versus driving speed (x-axis)
It is noted that percentage values relative to the reference cycle are indicative and not absolute, as speed
and engine behaviour are mathematically not coupled in a linear way but by complex thermodynamics
of fuel combustion and engine efficiency; see Formula (1). Nevertheless, “easy-to-understand”
indicators help drivers to better understand energy demand linked to their speed selection while
driving, as calculations are based on the percentage of quantified energy demand for real and virtual
speed profiles. This gives a direct feed-back by applied Newtonian physics and is brand- and engine-
independent, thus making the ND a complementary sensor system for the purpose of analysing energy
demand.
The benefit of having the suggested energy demand information in percentage can play a similar role
to that of energy labels, nowadays very common for electric light bulbs and household devices showing
different colour codes linked to their comparative energy demand.
As drivers are used to evaluating fuel consumption by the unit (litres per hundred kilometres), in
Anglo-American literature often stated as the abbreviated unit LPH, this percentage-deviation shall
be translated into the unit vLPH (virtual litres per hundred kilometres) by the following procedure
according to Formula (1):
a) Multiply the sum of inertia forces in N with the distance travelled per second, giving the energy
demand per second in Nm or J;
b) Sum up all energies in the given time window of the reference cycle and divide by the total distance
travelled during the speed profile, giving results in J/m;
c) Multiply J/m with 1 000 × 100 to give J/100 km;
d) Divide the result by 3,6 × 10 to find values in units of (kWh/100 km);
e) Divide the result by fuel value, b , and engine efficiency, η, to achieve virtual l/100 km (vLPH).
e
With regards to the accuracy of the proposed methodology, any measurement of percentage deviation
relative to a given reference cycle cannot be more precise than the precision of the reference cycle itself.
As all vehicle parameters are set to constant, errors occur by nature of the measurement technology in
rolling coefficient and tyre pressure, aerodynamics and even mass, not to mention thermodynamics
of the engine. Speed reference cycles (e.g. the WLTP cycle), take place under laboratory conditions and
not on the road while driving. This means that all changes of parameters such as tyre pressure, mass,
weather conditions (e.g. wind) and others have influence with regards to real fuel consumption.
Nevertheless, as automobile consumers worldwide use values for fuel and CO as one criterion for their
vehicle and engine choice consumption, vehicle registration authorities feel obliged to give indications
of fuel and CO consumption for these consumers based on standardized laboratory speed cycles,
independently of the complexity linked to the energy equation with all their parameters. Evaluating
real speed profiles by NDs relative to reference cycles has the same purpose: find out the percentage of
speed and energy deviations relative to reference profiles and show strategies for fleet operators and
drivers to reduce fuel consumption and CO emissions.
So far in this document, a simplified calculation has been presented, which does not yet consider the
influence of acceleration, standstill or slope within the real on-trip or the virtual reference cycle. This
can be found in Annex B. Nevertheless, the basic principles already show how the proposed methodology
can be used as tool to quantify energy demand in road transport by the applied principles of Newtonian
physics in combination with given reference cycles and ND sensor systems.
Further details of calculating energy behaviour for strategic monitoring of fleets and how to save
their fuel and CO demand by using the proposed methodology are elaborated in Annex B. Additional
and useful literature as well as results from pilot projects and academic studies can be found in the
Bibliography, in the published results of field trials in China in Reference [4].
Annex A
(informative)
Concept for implementation
A.1 System architecture
To obtain information about the energy behaviour per trip, a low-cost solution called Low Carbon
Mobility Management (LCMM) cloud platform architecture has been developed and applied in a
number of pan-European logistics projects. This solution considers the economic constraints felt by
most logistics companies and their difficulties in investing in the introduction of new technologies. A
plug-&-play solution is adopted, where GPS data provided by the HDVs is sent to a database, elaborated,
analysed, mapped and then given back in a simplified version to drivers and dispatchers. A schematic
architectural representation of the platform is presented in Figure A.1 and further details can be found
in Reference [4] in the Bibliography.
The process goes as follows: primary GPS data about vehicles derives from a mobile phone installed in
the truck. Such data, which includes time, latitude, longitude, speed and altitude, are transmitted every
fifteen seconds to the server platform. Since the average duration of a mobile phone with GPS turned
on is four hours, which is a short period in comparison to the average daily tour of a truck, the mobile
phone is equipped with an external battery that extends the duration of the battery to the duration
of the entire trip. With this system, the driver needs to turn the device on or off only once a day and
can use the smart phone as a telephone or mobile internet device during the day without losing any
information.
Before the beginning of each trip, all users need to register their vehicle configuration according to a
predefined database and register some user information including the average payload of the specific
trip. Data is transmitted to the LCMM platform, which includes four main technical components.
1) A nomadic device with GNSS satellite receiver, e.g. GPS.
2) The radio data transmission to connect device and cloud server.
3) An Internet of Things (IoT) or ICT back-end platform to receive trip data and manage the device.
4) A web-interface to give fleet-operator or others access to the trip data.
The collected GPS data is analysed and evaluated within the database server by any programming
language: in the piloted projects, PHP was used. Inside the server, the percentage deviation relative to
reference cycle, nowadays WLTP, is calculated and used as a basis for determining fuel consumption
and CO emissions.
Key
A GPS
B smartphone
C cloud server
D IoT platform
E fleet operator
F client application
1 mobile terminal device with app
2 telecom network connectivity
3 cloud server/device and data management
4 visualization tools and dashboard
[8] [11]
SOURCE: CAVALLARO, F., MAINO, F., & MORELLI, V. (2013) , WILLENBROCK R., TISCHLER J. ;reproduced with
the permission of the authors
Figure A.1 — LCMM cloud platform architecture
A.2 LCMM — Front-end
On left side of the system architecture in Figure A.1, a mobile phone is shown which is installed inside
the vehicle, using standard in-vehicle cradles, common also for smart phone based in-vehicle navigation.
After starting the LCMM APP software application on the mobile side, the exemplary design of the main
menu is illustrated in Figure A.2.
[8] [11]
SOURCE: CAVALLARO, F., MAINO, F., & MORELLI, V. (2013) , WILLENBROCK R., TISCHLER J. , reproduced with
the permission of the authors
Figure A.2 — LCMM exemplary Android APP design
Once the application has been started, trip recording is running, and satellite data are sent to the
server in the sample frequency of 1 Hz in compressed data format. The data are stored and extracted
in the mySQL database for online monitoring and reporting in the back-end side, after giving access to
authorized users by an IT administrator.
In Figure A.3, an example of three screens visible for the driver inside the vehicle is presented. The
energy panel (Figure A.3, left) shows increased energy demand of 58 % relative to the reference cycles.
The colour helps drivers to be aware of their eco-drive behaviour in the time window given by the
reference cycle. Compared to this, (Figure A.3), middle, shows a relative increase of 36 %, which has
colour code yellow-orange with regards to the reference cycle in terms of fuel consumption and CO
emissions. Finally, Figure A.3, right, shows a trip with improved driving performance and energy
demand reduction of -10 % relative to reference cycle values.
[8] [11]
SOURCE: CAVALLARO, F., MAINO, F., & MORELLI, V. (2013) , WILLENBROCK R., TISCHLER J. , reproduced with
the permission of the authors
Figure A.3 — LCMM cloud platform in use,
examples of Red-Green-Yellow encoded feedback for drivers on-trip with trip-related
evaluation of travel time, distance and energy performance
On-trip visualization for drivers is limited by safety concerns and usually involves nothing more than
a short glance at the colour coding to acquire some feedback on driving eco-mode or not. The positive
learning effect for drivers is rather linked to remembering the principles of eco-mode driving by using
inertia forces and smart braking or choosing the right speed on motorways according to traffic flow
and trip planning. The ex-post evaluation then clarifies details of positive or negative impact on energy
demand linked to certain driving manoeuvres.
Whenever trip recording needs to be stopped, drivers press a stop button to terminate trip recording.
Before shutting down the LCMM APP, drivers are informed about some general evaluation results of
the recorded trip. These include CO emissions, ranking inside the fleet of users, trip costs or savings,
as well as indications for improvements in percentage, for example. Figure A.4 shows the LCMM front-
[7]
end APP design in an exemplary manner, as used in the 2013 Jiangsu field trial after trip recording
has finished. Note that the presented numbers in Figure A.4 do not match with real trip data, but only
present dummy test data.
[8] [11]
SOURCE: CAVALLARO, F., MAINO, F., & MORELLI, V. (2013) , WILLENBROCK R., TISCHLER J. , reproduced with
the permission of the authors
Figure A.4 — LCMM exemplary Android APP design;
informative panel after trip recording has been stopped
In the 2013 Jiangsu field trial, based on the consumption registered by the mobile phone, the fuel cost
function and the performance of the driver was determined, both in aggregate and disaggregate forms.
They were visualized thanks to specific tools, such as summary sheets about driving performance
of the single vehicles, their speed curves and an emission profile map. Compared to on-board unit
platform solutions, many new features were introduced based on the physics of driving calculations,
usually not easily accessible by the use of in-vehicle on-board units for freight transport operators, such
as time lost, grade work, normalized braking and acceleration index. During the field trial execution,
ND and Android APP software gave high flexibility for adding or shutting down service features and
functions of the LCMM APP, which were requested by the fleet operator for information exchange with
their drivers on the front-end.
In Clause A.3, the LCMM back-end solution is described. This back-end solution includes the modules
indicated in Figure A.1 (System Architecture). It was not only used to test the effectiveness of eco-drive
training in the Chinese province of Jiangsu, but also to execute several R&D field trials in Europe (see
Table 1 in the Introduction).
The aim of all field trials with a different number of commercial users was to understand whether the
real-time component provides reliable information, which would help to launch ND solutions that can
be considered consistent with the traditional macro-modelling approaches adopted to evaluate the
CO efficiency of vehicular fleets. As previously mentioned, this would not only contribute to an ex-post
evaluation of the performances but could also grant a prompt modification of the driving style to reduce
fuel consumptions and CO emissions. In this way, it is possible to understand the real potentialities
granted by field trials, giving at the same time a practical solution to improve their effectiveness.
A.3 LCMM — Back-end
Before discussing the online evaluation and reporting function of LCMM, Figure A.5 gives an overview
of the vehicle model management where the different fixed parameters necessary for the energy
demand calculation according to Formulae (1) to (8). Different models with their vehicle characteristics
in cross section, rolling friction, empty weight, etc. were included and could be changed at any time, e.g.
in case engine efficiency turned out to be somehow different in real driving mode operation compared
to cycle values. After a specific vehicle is registered in the LCMM database it can be selected by drivers
and used for their trip evaluation.

[8] [11]
SOURCE: CAVALLARO, F., MAINO, F., & MORELLI, V. (2013) , WILLENBROCK R., TISCHLER J. , reproduced with
the permission of the authors
Figure A.5 — LCMM back-end vehicle configuration module
The LCMM back-end offers several possibilities for online-monitoring and reporting functions.
Feedback on driving performances is provided back to freight transport operators through the website
platform (for post-trip evaluations and tour planning of the fleet manager), or through the front-end
Android application (for real-time evaluations) mentioned in A.2. The website platform provides
detailed information about the time of the trip listed as well as the duration of trip, distance travelled,
average speed, energy behaviour in percentage relative to reference cycle from during the field trial the
European Driving Cycle (Urban UDC and Extra-Urban EUDC).
An example of the appearance of the website evaluation of recorded trips for fleet operators can be
found in Figure A.6 showing an LCMM recorded trip with a private passenger car in Sweden. At the
top of Figure A.6, the trip ID, vehicle model, date and time are listed together with some basic energy
evaluations, i.e. the normalized energy performance index (EPI) in units of cl/t×km, which was found to
be effective when comparing vehicle and load influence during the fleet operation in logistics.
Figure A.6 also illustrates the energy colour coding used within the different pilot projects designed
to monitor fuel and CO demand along a given trip of a vehicle in motion. Green was chosen to indicate
the same demand as that of the reference cycle or better, yellow for demand increase between 100 %
and 150 %, whereas red represents fuel and CO increase above 150 %. The introduction of traffic light
colour coding allows logistics fleet operators and drivers to better understand causes and effects of
certain driving manoeuvres including effects of traffic congestion, road segments suffering of reduced
capacity due to construction work, etc. By exporting trip data and energy analysis to ex-post data files
in “.csv”, “.pdf” or “.kml” format, drivers and logistics service providers can monitor details for pre-trip
tour planning considering energy demand and individual driving behaviour.
NOTE ".kml" is the data format which can be used for Google Earth digital map presentations.

[8] [11]
SOURCE: CAVALLARO, F., MAINO, F., & MORELLI, V. (2013) , WILLENBROCK R., TISCHLER J. , reproduced with
the permission of the authors
Figure A.6 — LCMM cloud platform in use,
recorded private passenger trip in Sweden and digital map presentation
Figure A.7 shows the same trip from Sweden in the speed (km/h) over time (s) cartesian presentation,
including stop time and urban/extra-urban segments of the recorded trip, whereas Figure A.8 gives an
indication of the same trip including height information.

[8] [11]
SOURCE: CAVALLARO, F., MAINO, F., & MORELLI, V. (2013) , WILLENBROCK R., TISCHLER J. , reproduced with
the permission of the authors
Figure A.7 — LCMM cloud platform in use,
recorded private passenger trip speed profile (y-axis) over trip time recorded (x-axis)
[8] [11]
SOURCE: CAVALLARO, F., MAINO, F., & MORELLI, V. (2013)
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