EE-VERT Project title: Energy Efficient VEhicles for Road ... · 1.1 Background Due to the fact...

EE-VERT

© 2009 The EE-VERT consortium 1

Project acronym: EE-VERT

Project title: Energy Efficient VEhicles for Road Transport – EE-VERT

Grant Agreement Number: 218598

Programme: Seventh Framework programme, Theme 1.1, Greening

Contract type: Collaborative project

Start date of project: 1 January 2009

Duration: 36 months

Deliverable D1.2.1:

MISSION PROFILE REPORT

Authors: Organisation Name CRF Carlo d’Ambrosio

CRF Michele Genovese

LEAR Antoni Ferré

VTEC John Simonsson FH-J Hubert Berger

FH-J Manuela Midi

Bosch Marcus Abele Bosch Gerd Schmieder

Reviewers: Organisation Name VTEC Eilert Johansson

MIRA Derek Charters

Dissemination level: Public

Deliverable type: Report

Work Task Number: 1.2 Version: 1.0

Due date: 31/03/09

Actual submission date: 15/04/09

Consortium Members

Organisation Abbreviation Country

MIRA Limited MIRA GB

Volvo Technology AB VTEC SE

Centro Ricerche Fiat Società Consortile per Azioni CRF IT

Robert Bosch GmbH Bosch DE

LEAR Corporation Holding Spain SLU Lear ES

Engineering Center Steyr GmbH & Co KG /

MAGNA Powertrain

ECS AT

FH-JOANNEUM Gesellschaft mbH FH-J AT

Universitatea ―Politehnica‖ din Timisoara UPT RO

SC Beespeed Automatizari SRL Beespeed RO

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Document history

Planned revisions:

Version Description Planned date Actual date

1.0 First issue of deliverable 31/03/2009 15/04/2009

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SUMMARY

1. INTRODUCTION ........................................................................................... 5

1.1 Background ...............................................................................................................................5

1.2 Purpose ......................................................................................................................................5

1.3 Scope .........................................................................................................................................5

2. ANALYSIS OF EXISTING MISSION PROFILES IN TERMS OF

AUXILIARIES ENGAGEMENT .................................................................. 6

2.1 Overview ...................................................................................................................................6

2.2 SC03 cycle (USA) ......................................................................................................................6

2.3 ARTEMIS cycle (Europe) .........................................................................................................9

2.3.1 Air conditioning excess fuel consumption ............................................................................9

2.3.2 Other auxiliaries excess fuel consumption ............................................................................9

2.3.3 Excess CO2 emission ......................................................................................................... 10

2.4 Conclusions ............................................................................................................................. 11

3. REALISTIC EXTENDED MISSION DEFINITION ................................. 12

3.1 Passenger car data mission ..................................................................................................... 12

3.1.1 Principle of analysis ........................................................................................................... 12

3.1.2 Overview of ARTEMIS approach ...................................................................................... 12

3.1.3 Emissions on ARTEMIS and NEDC .................................................................................. 17

3.1.4 Data acquisition on test vehicle .......................................................................................... 21

3.1.5 Reference mission .............................................................................................................. 25

3.1.6 Conclusions ....................................................................................................................... 27

3.2 Bus vehicle data mission ......................................................................................................... 27

3.2.1 Introduction ....................................................................................................................... 27

3.2.2 Driving cycles .................................................................................................................... 27

3.2.3 Additional boundary conditions.......................................................................................... 30

3.2.4 Bus parameters .................................................................................................................. 33

3.2.5 Conclusion ......................................................................................................................... 34

3.3 Link between the components operational mode and the mission profile ............................. 34

3.3.1 Detailed and simplified procedure for the analysis .............................................................. 34

3.3.2 Generator ........................................................................................................................... 35

3.3.3 Motor oil pump .................................................................................................................. 38

3.3.4 Water pump ....................................................................................................................... 39

3.3.5 Fuel pump .......................................................................................................................... 41

3.4 Extrapolation methodology from condensed/standard cycles ............................................... 41

3.4.1 Simulating mission profile providing only vehicle speed (NEDC) ...................................... 41

3.4.2 Implementing EE-VERT mission profile ............................................................................ 45

4. ROLLBENCH FUEL CONSUMPTION PROCEDURE GUIDELINES .. 47

5. REQUIREMENTS ON MISSION PROFILES REGARDING THEIR

INTEGRITY FOR DERIVING STIMULI-SET FOR PREDICTIVE

CONTROL SIMULATION .......................................................................... 48

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5.1 Suggestions for driving cycle concerning Air-condition ........................................................ 49

5.1.1 Deriving a mission profile using measured equivalent heat sources and thermal

resistance ........................................................................................................................... 50

6. CONCLUSION ............................................................................................. 52

REFERENCES ..................................................................................................... 53

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1. INTRODUCTION

1.1 Background

Due to the fact that EE-VERT emphasises energy management aspects, the usage of the vehicle in a

realistic driving scenario and over a longer period than a single mission will be studied (e.g. the use over one week). In this way it will be possible to evaluate potential energy usage and optimisation not

only related to a medium working condition as standard homologation cycles.

Actual missions used for fuel consumption assessment are poorly related to real vehicle use, especially

on aspects related to auxiliaries. State of the art shows a lack of auxiliaries engagement description or rough simplifications. This documents aims to propose a realistic mission profile and a detailed

description of related auxiliaries usage.

1.2 Purpose

The purpose of the subtask is the definition of a vehicle mission profile and of a common procedure to

assess the fuel consumption associated with the auxiliary systems. Mission profile is comprehensive of detailed auxiliaries engagement.

Activity is focused on passenger vehicles and buses. Difference in vehicle use is greatly marked

between passenger cars and buses, so different reference missions are defined for each of them.

Due to the difficulty to reproduce a complex vehicle real life cycle and obtaining repeatable results in a vehicle test bench, the ―weekly mission‖ results will be extrapolated starting from standard or

―condensed mission‖ which should be feasible in a test-bench, through a defined methodology.

1.3 Scope

The scope of the task is to define a procedure which could constitute a basis for a new EU standard:

to assess the vehicle fuel consumption in a more realistic manner; to qualify the auxiliary systems in terms of energy efficiency.

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2. ANALYSIS OF EXISTING MISSION PROFILES IN TERMS OF

AUXILIARIES ENGAGEMENT

2.1 Overview

The increasing impact of auxiliary loads on vehicle fuel economy is a critical parameter for the

designers of these items. Research projects such as ―The Effect of Air Conditioning on Regulated Emissions from In-Use

Vehicles‖, sponsored by the Coordinating Research Council (CRC), the California Air Resources

Board (ARB) and the Texas Commission demonstrated that auxiliary loads such as air conditioning

(A/C) had a substantial impact on emissions and fuel consumption [1]. This program showed that operation of the vehicle A/C system over a range of environmental conditions resulted in consistent

increases in vehicle emissions of nitrogen oxides (NOx) and carbon monoxide (CO). NOx increased by

0.06 to 0.4 g/km, depending on the severity of the test cycle and ambient conditions. CO increased by 0.3 to 7 g/km. Hydrocarbon emissions were not affected by air conditioning use except in a few,

limited cases.

In Europe, tests done at CRF on cars equipped with A/C systems and tests realized on an A/C system

test bench show that the fuel over-consumption due to A/C operation is in between 1 and 2.45 L/100km along the European NEDC cycle, representing respectively 21% to 53% of over-consumption [2].

Since standard tests cycles, as NEDC in Europe or FTP in United States failed to take into account this

fact, research projects and standardization initiatives have been developed both in Europe and Unites States to incorporate this effect on emission models and tests [3].

2.2 SC03 cycle (USA)

In United States, current driving cycle for emission testing is the so-called FTP-75 (Federal Test

Procedure). FTP-75 is a transient-type test cycle for cars and light duty trucks. Effective model year

2000, it has been complemented by two Supplemental Federal Test Procedures (SFTP) designed to

address shortcomings with the FTP-75 in the representation of (1) aggressive, high speed driving (US06), and (2) the use of air conditioning (SC03) [4].

The SC03 Supplemental Federal Test Procedure (SFTP) represents the engine load and emissions

associated with the use of air conditioning units in vehicles certified over the FTP-75 test cycle. The cycle represents a 5.8 km (3.6 mile) route with an average speed of 34.8 km/h (21.6 miles/h),

maximum speed 88.2 km/h (54.8 miles/h), and duration of 596 s as shown in Figure 1.

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Figure 1. SFTP SC03 Cycle

The SFTP applies to vehicles with a gross vehicle weight under 2608 kg (5750 lb).

The SC03 Test is conducted with following conditions:

(1) Ambient air temperature is controlled, within the test cell, during all phases of the air

conditioning test sequence to 35 ± 2 °C (95 ± 2 °F) on average and 35 ± 2 °C (95 ± 5 ºF) as an

instantaneous measurement.

(2) Ambient humidity is controlled, within the test cell, during all phases of the air conditioning test

sequence to an average of 100 ± 5 grains of water/pound of dry air.

(3) Radiant energy intensity set point is 850 ±45 W/m2

(4) Air flow volumes must be proportional to vehicle speed. With the above optimum discharge size,

the fan volume would vary from 0 L/s (0 cfm)1 at 0 km/h (0 mph) to approximately 44,835 L/s (95,000

cfm) at (96,5 km/h) 60 mph.

The air conditioning system controls of the vehicle are set as follows:

(1) A/C mode setting at Maximum.

(2) Airflow setting at Recirculate, if so equipped.

(3) Fan setting at highest setting.

(4) A/C Temperature setting at full cool. For automatic systems set at 22,2 ºC (72 °F).

(5) Air conditioning controls should be placed in the "on" position prior to vehicle starting so that the air conditioning system is active whenever the engine is running

Tests done at Clean Air Vehicle Technology Center measured the effect of the air-conditioning system

on fuel economy and tailpipe emissions for a variety of vehicles. The average impacts of seven

vehicles (’95 Voyager, ’97 Taurus, ’95 Civic, ’95 F-150, ’97 Camry, ’96 Camaro, and ’95 Skylark) are shown in Table 1 for the A/C system on compared with the results with the air-conditioning system off

[5].

1 cfm is the acronym of cubic feet / minute

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Table 1. SC03 Test Results [5]

U.S. Department of Energy's National Renewable Energy Laboratory (NREL) has developed a model for extra power consumption and emissions due to A/C conditioning and introduced it on the

simulation tool ADVISOR. The litres (gallons) of fuel used for air conditioning were then determined

by using the fuel consumed to drive the vehicle the number of miles travelled with the A/C on and a

hypothetical amount of fuel that would have been consumed if those same miles were travelled without the A/C [5].

NREL used this model to estimate fuel consumption due to A/C for a conventional vehicle and for a

high-fuel-economy vehicle. The conventional vehicle was modelled as a 1406 kg (3100 lb), 3.0 L, spark-ignition engine, with an 800 W base auxiliary load resulting in a combined city/highway fuel

economy of 8,8 L/100km (26.8 mpg). The high-fuel-economy vehicle was modelled as a 907 kg (2000

lb), 1.3 L, direct-injection, compression ignition engine, parallel hybrid with a base auxiliary load of 400 W. Figure 2 shows the impact of auxiliary load on the fuel economy found over the SCO3 cycle.

The fuel economy of a nominally 2,9 L/100 km (80 mpg) high-fuel economy vehicle could drop to about

4,7 L/100 km (50 mpg) if the auxiliary loads increase from 400 W to 2000 W.

Figure 2. Auxiliary load impacts on SC03 cycle fuel economy [5]

Results from NREL on SC03 cycle show that current air-conditioning systems can reduce the fuel

economy of high fuel-economy vehicles by about 50% and reduce the fuel economy of today’s mid-sized vehicles by more than 20% while increasing NOx by nearly 80% and CO by 70%. In other

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words, for high fuel economy vehicles, current air conditioning systems have a completely

unacceptable impact on fuel economy. Therefore, the impact on auxiliary on fuel consumption should

not be neglected.

2.3 ARTEMIS cycle (Europe)

Project ARTEMIS included a specific worktask [8] to develop models for estimating the impact of

auxiliaries on fuel consumption and emissions (CO2, CO, HC, NOx, and particles). Two main types are identified: A/C loads and other auxiliaries. To this aim, various data from European laboratories were

analyzed and parameters linked to technology and to climatic conditions were investigated.

Firstly, they propose the type of unit to express the excess fuel consumption due to air conditioning and other auxiliaries: in volume per distance unit or in volume per time unit. For physical reason (no strong

relation between cooling demand and vehicle speed), it seems that excess fuel consumption due to air

conditioning have to be expressed in volume per time ([L/h] for instance). Excess fuel consumption due to auxiliaries can be express in [L/h] as for A/C.

2.3.1 Air conditioning excess fuel consumption

Excess fuel consumption for a vehicle due to A/C is estimated using a simplified model. The model

has to take into account climatic conditions. Climatic conditions depend on temperature, humidity, solar radiation (direct and diffuse) and position of sun in the sky. Solar radiations can be quite difficult

to obtain, so they preferred the use of the hour in the day than the use of solar radiation.

Statistical regressions were done on numerical results obtained with the physical model along 90 locations, in order to determine the most appropriate form of the model and the value of the

parameters.

The hourly excess fuel consumption (hfc) for a vehicle due to A/C is given by [8]:

where:

TBex,t,wf : external temperature provided by hourly, daily or monthly weather data (wf:

weather format) [°C]. Hourly weather format contains 8760 values; daily weather format

contains 365 values and monthly weather data contains 12 values.

Tint : set temperature in the cabin; default value is 23°C.

h: hour (between 1 and 24).

a1,wf , a2,wf , a3,wf , a4,wf , a5,wf : coefficients depending on the location.

2.3.2 Other auxiliaries excess fuel consumption

The following analysis is mainly based on the work done by EMPA ([9],[10]) on the effect of

auxiliaries on emissions. Table 2 lists auxiliaries and gives electrical power consumption.

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Table 2. Auxiliaries list according to EMPA

Excess fuel consumption due to auxiliaries can be express in [L/h] as for A/C. According to [10], it is

estimated that an average excess fuel consumption of 0.075 L/h is produced by an electrical load of 160 W corresponding to dip headlight. It is assumed that excess fuel consumption is proportional to

electrical load. Therefore, the excess fuel consumption due to given auxiliaries hfcaux is given by:

(%)160

075.0timePhfc auxaux

Paux is the power of the auxiliaries [W] and time is the percentage of use of the auxiliary.

2.3.3 Excess CO2 emission

The excess CO2 emission is deduced from the excess fuel consumption by carbon balance, assuming

that the whole carbon of the fuel is transformed into CO2.

hfcceCO CO22

where:

cCO2: is the transformation factor from fuel to CO2 depending on vehicle.

The transformation factor is deduced from carbon balance equation ([9]) and density of fuel. To calculate this factor, they neglected the mass of non-CO2 pollutants in comparison with the mass of

CO2 :

rH/C is the hydrogen carbon ratio depending of the type of fuel: 1.8 for gasoline engines and 2 for

diesel engines.

fuel is the density of fuel [kg/L]: 0.766 kg/L for gasoline and 0.8414 kg/L for diesel engines.

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2.4 Conclusions

Tests done in US and Europe have demonstrated repeatedly that auxiliaries (such as current air

conditioning systems) reduce the fuel economy of conventional on high-fuel efficiency vehicles in an important amount.

In this section we present state-of-the-art methodologies (based on US standard and European research

projects ARTEMIS) to determine the amount of extra emissions and fuel consumption due to equipped auxiliaries. These methodologies will be used in EE-VERT mission profiles in order to obtain realistic

emission and fuel consumption estimations for simulation and validated using vehicle testing.

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3. REALISTIC EXTENDED MISSION DEFINITION

3.1 Passenger car data mission

3.1.1 Principle of analysis

It’s common knowledge that NEDC (New European Driving Cycles) does not represent realistic emission factors because it’s too different from real world driving conditions. However NEDC will be

also considered in the EE-VERT project for new components impact assessment to give a fuel saving

figure on a well known reference cycle. To define a realistic mission profiles the ARTEMIS driving cycles were first analyzed to check and

exploit their coverage of real-world driving and vehicle operating conditions.

Second, an experimental activity was done on a test vehicle: data were recorded by CRF in different real driving scenarios. Acquired data were compared to ARTEMIS cycles according to related

scenarios and high correlation was found.

Finally weekly cycle was defined combining acquired data.

3.1.2 Overview of ARTEMIS approach

ARTEMIS project was based on:

recording driving conditions using a large sample of instrumented vehicles;

analysing data;

developing cycles from the recorded data.

Many set of data were acquired from different teams in Europe:

Driving cycles in Naples: 3 drivers during 20 days. It’s a good approach of urban driving

and congested driving.

Handbook real-world driving data and cycles: 210 hours from Switzerland and 240 hours

from Germany.

Drive-MODEM (MODelling of EMissions and fuel consumption in urban areas) and

MODEM-HYZEM (European Development of Hybrid Vehicle Technology approaching

efficient Zero Emission Mobility) driving data and cycles: 77 private vehicles in 4

countries and driven by their owners during 2200 hours and 88000 km.

MODEM-Hyzem data set is the most extensive description of driving conditions and behaviour in

Europe, but there is no information about location of the vehicle and traffic conditions. Italian and Swiss data, on the on the other hand, are related to specific situations, but not so extensive.

Artemis driving cycles derive from the processing of the previous data. The principle is a breakdown of recorded speed profiles into segments of variable lengths. Each segment is described through its

instant speed and acceleration.

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Figure 3. Segmentation based on acceleration and speed.

Figure 4. Segmentation based on stop duration and speed.

Analyzing data, it is possible to show that Swiss data, compared to the MODEM-Hyzem, are quite

well distributed only in the case of acceleration (Figure 3). If stop duration is taken in account, Swiss

data shows short stops compared to MODEM-Hyzem (Figure 4).

MODEM-Hyzem data were classified into 12 classes of driving conditions (Figure 5). The 12

identified driving classes situations are shown in Table 3.

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Figure 5. Identified classes related to driving conditions.

Table 3. ARTEMIS classes description

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Congested urban driving (10-16 km/h)

- congested urban with high stop duration (61%)

- urban dense

- urban low steady speeds (17 km/h)

Free-flow urban driving

- free flowing (26 km/h)

- unsteady (32 km/h)

Secondary roads (44-64 km/h)

- unsteady speed

- rural roads

- steady speed

Main roads

- unsteady speed - steady speed

Motorway driving (115-124 km/h)

- unsteady speed - steady speed

UR

BA

N

RU

RA

L

MO

TO

RW

AY

Table 4. ARTEMIS classes description

The previous classes typology enables to define the statistical distribution of classes (in % of the total number of observations) within different trip categories as regards to encountered driving conditions.

Three trip categories were identified: motorway, road and urban trips. These trip categories can be

characterised by their speed and driving conditions. Their respective shares (in mileage, in trip number) were measured in all acquired data (Table 5).

Table 5. Trip description through a typology in 3 trip classes

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Each ARTEMIS trip cycle was developed through the idea of ―multi-component‖ driving cycles: based

on composition of different classes of driving condition (Table 6). Each type of driving conditions can occur within each trip categories, but with different probabilities.

Table 6. Trip described in function of their driving conditions

The chronology of the driving conditions within the trips can be particularly important:

it takes into account of the driving conditions at start and in particular at cold start,

emissions are strongly dependent on the chronology of the operation of the engine and catalyst.

The trip typology and composition in the different recorded driving patterns enable to analyse the

chronology of these driving conditions within the trips. It is possible to establish the probabilities for each of the driving patterns to be followed by another driving type (Table 7).

Table 7. Sequence of the typical driving condition within the Artemis cycles

Finally the aim is to reproduce the 3 trip cycles (urban, rural and motorway) considering their main

characteristics (average speeds, stop frequency and duration), their structures according to the various

driving conditions encountered and the chronology of these conditions.

Resulting ARTEMIS trip cycles are shown in Figure 6.

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Figure 6. ARTEMIS driving cycles.

It is important to remark that:

No recommendation from ARTEMIS about engine start condition (cold/warm engine).

No recommendation from ARTEMIS about engine ignition at cycle start. Engine can be already

switched on or off at trip start.

3.1.3 Emissions on ARTEMIS and NEDC

Regulated emissions from four current production European vehicles (Table 8) were measured over the

Common Artemis Driving Cycles (CADC). CADC results are presented for each of the four vehicles tested together with results measured on the regulated New European Driving Cycle (NEDC) test for

comparison.

Regulated emissions (CO, HC, NOx) were measured by conventional analytical techniques and the

draft UNECE Particulate Measurement Protocol (PMP) [2] was used for both Particulate Mass and

Particle Number measurement.

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Table 8. Vehicles used for emission tests

During pre and post-conditioning parts of the Artemis cycles, no measurements were performed. All

results presented and discussed only treated the valid part of the cycle.

NEDC cycles were performed to the requirements of EU Directives and were all cold start.

3.1.3.1 CO emissions

CO emissions from all four vehicles were below the relevant limits for Euro 4 on the NEDC test.

Results for the three diesel vehicles on the hot-start Artemis cycles were all significantly lower than on the NEDC, in most cases below the limit of detection.

For the gasoline vehicle the results on the Artemis Urban cycle were comparable to the NEDC and those on the Artemis Extra-Urban cycle were some 20% lower. However, CO emissions for the

Artemis Highway cycle were some 10 times those on the other cycles.

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Figure 7. CO emissions

3.1.3.2 HC emissions

The HC results all present lower emissions on the hot-start Artemis cycles than on the NEDC, for all

vehicles.

Figure 8. HC emissions

3.1.3.3 NOx emissions

For NOx the results are different.

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The gasoline vehicle gave good results on all cycles, achieving 33 mg/km on the NEDC, 11.4 mg/km

for the Artemis Urban and 3 mg/km for the Extra-Urban and Highway Cycles.

For all three diesel vehicles, however, the results on the Artemis cycles were significantly higher than on the NEDC test. As an example, the results for diesel A on the legislative NEDC test were relatively

close to the legislative limit (227 mg/km ± 9 mg/km, compared to a legislative limit of 250 mg/km).

Average NOx emissions on the Artemis Urban cycle were some four times higher at 950 mg/km. Artemis Extra-Urban and Highway NOx emissions were 624 and 706 mg/km respectively: also a lot

above the NEDC figures. These bad results can be due to the not optimised gear-shift points on the

Artemis cycles for diesel vehicles.

Figure 9. NOx emissions

3.1.3.4 PM emissions

Emissions from the Diesel with a DPF were at least an order of magnitude lower than for the other

diesels and were comparable to those of the gasoline vehicle.

For all the diesel vehicles the results on the Artemis Urban cycle were higher than on the NEDC test.

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Figure 10. PM emissions

3.1.3.5 Fuel consumption

Besides regulated emissions, also fuel consumption (and related CO2 emission) was measured, as shown in the Table 9. Fuel consumption was a critical factor, being substantially higher on the Artemis

Urban cycle.

Table 9. Fuel consumption

This analysis shows that impact on emissions and fuel consumption is quite different between NEDC

and ARTEMIS cycles. Considering ARTEMIS extremely close to realistic vehicle use, as shown in

3.1.2, effective emissions and fuel consumption reduction must go far ahead simple evaluation on NEDC.

Regarding current project EE-VERT (which focuses just on fuel consumption, legislative emissions

requirements are out of scope), this analysis clearly shows that effective fuel saving must be approached starting from a well assessed realistic vehicle mission with realistic boundary conditions.

3.1.4 Data acquisition on test vehicle

Three real drive cycles were performed and acquired in different road and traffic conditions using a

test vehicle. The purpose was to give evidence that ARTEMIS cycles are a trusted database to build

the vehicle weekly mission. Test vehicle is an Alfa Romeo 159 1.9JTD. Vehicle model is not relevant at the moment for this kind

of analysis; just vehicle use is under analysis.

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The three cycles represent possible real uses of a car during a week.

Monday to Friday: work and free time (Figure 11);

Saturday: free time, shopping, night life (Figure 12); Sunday: out of town trip (Figure 13).

Monday -> Friday

0

20

40

60

80

100

120

140

0.00

0.02

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9.39

9.41

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9.51

9.54

9.56

9.58

10.0

010

.02

10.0

410

.06

10.0

810

.10

10.1

210

.14

10.1

610

.19

10.2

110

.23

10.2

510

.27

10.2

910

.31

22.0

3

hours

Vehi

cle

spee

d [k

m/h

]

Vehicle speed [km/h]

Home to work Work to home Free time

9 hours stop night stop

Figure 11. Monday to Friday cycle.

Saturday

0

10

20

30

40

50

60

70

80

0.00

0.00

0.01

0.02

0.02

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4.09

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4.11

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6.27

17.2

8

hours

Vehi

cle

spee

d [k

m/h

]


Free time Shopping Restaurant / cinema

night stop 4 hours stop 2 hours stop

Figure 12. Saturday cycle

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Sunday

0

20

40

60

80

100

120

140

0.00

0.01

0.03

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8.38

8.39

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8.54

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8.58

8.59

9.01

9.03

9.04

9.06

9.08

9.09

9.11

9.13

hours

Vehi

cle sp

eed

[km

/h]


Out of town trip Out of town trip

night stop 8 hours stop

Figure 13. Sunday cycle

It is possible to identify, within these 3 real acquired cycles, almost all the driving classes in the

Artemis project. Some parts of the acquired cycles were compared to ARTEMIS classes (Table 3,

Table 4) and high relationship was found (Figure 14, Figure 15, Figure 16, Figure 17).

Main roads

unsteady

speed

steady

speed

Motorway

unsteady

speed

steady

speed

Rural secondary roads

unsteady

speed

Figure 14. Home to work vs ARTEMIS conditions

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Free flow urban

Congested low

speed Flowing stable Urban dense Free flow urban

Figure 15. Shopping/sport/free time vs ARTEMIS conditions

Congested stops Free flow urban Free flow urban Urban dense

Figure 16. Free time/shopping/restaurant

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Congested

speed

Rural secondary

roads

unsteady speed

Motorway

unsteady speed steady speed

Flowing stable

Figure 17. Out of town trip

3.1.5 Reference mission

Due to high relationship between test vehicle acquired data and ARTEMIS classes, test vehicle

acquired data can be used as the reference weekly mission. Detailed mission description can be found in annex file ―Driving_cycles_passengercars_EEVERT.xls‖

The mission is described in terms of vehicle dynamic and boundary (environmental, comfort, driver

request) conditions. Not just average values are reported, but a complete time history for each relevant

parameter:

vehicle speed

engine speed road slope

Two weather conditions are considered: hot summer and winter. The related relevant parameters are:

outside air temperature

outside Relative Humidity

solar radiation

Environmental data were recorded in Turin over yearly experimental activities. Through data

elaboration a time history was defined to be used in the project. To make simulation related to different location, it is enough to provide related time histories.

Also incident vertical solar radiation was included. For A/C systems solar radiation is a relevant

information to evaluate cooling/warming request. Incident vertical radiation is assumed as a simplified average value during vehicle mission; so no other radiation components are considered: horizontal or

angle dependent.

Solar radiation may be used during the WP2 and WP3 of the project also to evaluate the impact of

solar roof on vehicle energetic balance. Incidence is a very important parameter to maximise efficiency of solar panels; in automotive application the most suitable position for mounting solar panel is vehicle

roof. So vertical solar radiation is good for evaluation purpose. However it must be taken in account

that optimal solar radiation depends on inclination (Figure 18, Figure 19). Since panels must reject 80% to 90% of the solar energy incident upon them, and usually this transfer

is to the ambient air, both the air temperature and the wind speed and direction have great effects on

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their efficiency. In application like solar roof, vehicle speed can be used to calculate equivalent wind

speed.

Reference to solar radiation may be found in [17].

Comfort boundary conditions defined in the reference mission are:

internal vehicle temperature: 22°C summer / 24°C winter low beam lights

high beam lights

fog lights

wiper rear window heating

Figure 18. Optimal panel inclination (Turin)

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Figure 19. Radiation at optimal angle (Turin)

3.1.6 Conclusions

A weekly reference mission for passenger cars was identified through exploitation of ARTEMIS projects results and their validation using test vehicle acquired data over a typical week usage.

All relevant vehicle dynamics and boundary conditions were defined through time history data.

3.2 Bus vehicle data mission

3.2.1 Introduction

Simulations are planned which will demonstrate the gain on fuel economy by the improvements

utilised/developed in the project. The simulations will compare a modified state-of-the art bus with an unmodified bus of the same model. No bus prototype will however be built.

Driving cycles and additional boundary conditions are needed as input to the simulations. The driving

cycles give the speed versus time during a certain time period. The additional boundary conditions are mainly focused on covering additional input parameters needed in the simulations, e.g. outside

temperature, humidity etc.

3.2.2 Driving cycles

There exist many different driving cycles for buses that could be used in this project as input to the

simulations that will be performed, see [11]. Driving cycles have been created in Europe, the US and many other countries by authorities, organisations, companies and universities. These cycles have,

more or less, been derived from driving data measured on actual bus routes. The driving cycles can be

used for many purposes (e.g. emission and fuel consumption testing) in simulations, dynamometer or real driving tests. In this project we are however only interested in studying fuel consumption in

simulations. The reason for the existence of standardised driving cycles is the need to be able to

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compare the results for different parameters, e.g. fuel consumption, between different vehicle models

and also from different manufacturers.

In both the SORT standard [13] and in the SAE J2711 standard [12] three different driving cycles are

recommended. Each of the three driving cycles represents different traffic conditions / situations. The

main parameters of the driving cycles are compared between the two standards in Table 10. It can be seen that the similarities between the low speed cycles are higher than between the high speed cycles.

Parameter SORT

SAE J2711

Cycle name

―Classification‖ in standard

Average speed

Maximum speed

Number of stops

Idling time fraction

SORT 1 cycle

Urban

12.6 km/h

40 km/h

5.8 stops/km

39.7 %

Manhattan cycle

Low speed transit bus operation

11.0 km/h

40.7 km/h

6.2 stops/km

36.1 %

Cycle name


Average speed Maximum speed

Number of stops


SORT 2 cycle

Mixed

18.6 km/h 50 km/h

3.3 stops/km

33.4 %

Orange County Transit cycle

Intermediate speed bus operation

19.8 km/h

65.4 km/h 2.9 stops/km

21.3 %

Cycle name


Average speed

Maximum speed

Number of stops


SORT 3 cycle

Suburban

26.3 km/h

60 km/h

2.1 stops/km

20.1 %

1UDDS

High speed operation

30.3 km/h

93.3 km/h

1.5 stops/km

33.3 %

Note 1: Urban Dynamometer Driving Schedule

Table 10. SORT and SAE comparison

3.2.2.1 SAE J2711

The driving cycles in SAE J2711 have been developed by different parties, i.e. not by SAE (Society of

Automotive Engineers) themselves. SAE has however picked out three driving cycles as representative

for different driving situations / conditions, see §3.2.2 above, and standardised them (data can be found in annex file ―Driving_cycles_buses_EE-VERT.xls‖). The driving cycles chosen in the standard aims

at dynamometer testing and look more realistic and less simplified than the SORT cycles. The SAE

J2711 cycles are therefore recommended for use as input in the simulations in this project. Driving

cycles with lower speed and more stops, e.g. the Manhattan cycle, are likely to give higher gain in fuel economy. It is therefore likely that improvements developed/utilised in the project will be

implemented first on bus models designed for bus routes similar to lower speed driving cycles. It is

therefore also recommended to perform simulations first of all with driving cycles belonging to lower speed cycles.

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Manhattan cycle

0

5

10

15

20

25

30

35

40

45

0 200 400 600 800 1000

Time (s)

Sp

eed

(km

/h)


0

10

20

30

40

50

60

70

0 200 400 600 800 1000 1200 1400 1600 1800

Time (s)

Sp

eed

(km

/h)

UDDS (Urban Dynamometer Driving Cycle)

0

10

20

30

40

50

60

70

80

90

100

0 200 400 600 800 1000

Time (s)

Sp

eed

(km

/h)

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3.2.2.2 SORT (Standardised On Road Test Cycles)

The SORT cycles were developed by the organisation UITP (The International Association of Public Transport). The purpose of these cycles is to be able to easily compare fuel consumption of different

buses from different manufacturers based on real driving tests. To be able to reproduce the driving

cycles in real driving tests the cycles are very simple and consist of only three trapezes each, see the graph below. Due to the driving cycles simplicity they are mainly used by Volvo buses to give fuel

consumption figures to bus companies which specifically request these figures. Other driving cycles

are used by Volvo buses when for instance dimensioning the drivetrain.

SORT cycles

0

10

20

30

40

50

60

70

0 50 100 150 200

Time (s)

Sp

eed

(km

/h)

SORT1

SORT2

SORT3

Data can be found in the annex file ―Driving_cycles_buses_EE-VERT.xls‖.

3.2.3 Additional boundary conditions

The additional boundary conditions aim at providing input parameters to simulation models for

different components such as the generator, the climate system etc. Some of these models will be

developed in this project. It is difficult beforehand to list all parameters that are necessary to be able to accurately model these components. Updates of this paragraph in the document are therefore likely

further ahead in the project.

In the SORT standard and also in the SAE J2711 standard auxiliaries such as A/C are turned OFF. In

the SAE J2711 standard however they propose some boundary conditions for the A/C which could be

used in a future standard.

Three cases are proposed to be used in the simulations in this project. They are named ―standards‖,

―summer‖ and ―winter‖. Presently all input parameters are static except the speed in §3.2.2. The case

―summer‖ for instance assumes an ambient temperature of 35ºC. This is not realistic when combined with a full days driving of approximately 18 hours. This might have to be modified further ahead in the

project in order for the simulations to better predict the real world behaviour. The parameters ambient

temperature and solar load would be especially interesting to specify as time varying and probably also relative humidity.

Case 1: “Standards”

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Here basically all auxiliaries are turned OFF. Corresponds to conditions in the SORT and SAE J2711

standards.

Case 2: “Summer”

Conditions for a hot summer day. Many parameters under ―Climate system conditions‖ in the table

below have been copied from SAE J2711.

Case 3: “Winter”

Conditions for a gloomy winter day.

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Parameter Case 1: “Standards” Case 2: “Summer” Case 3: “Winter”

Road conditions

Slope 0% 0% 0%

Curvature 0% 0% 0%

Driving cycle extension to ~18h/day ~18h/day ~18h/day

Climate system conditions

Climate control setting OFF Auto Auto

1Ambient temperature N/A 35ºC 10ºC

1Ambient humidity N/A 50% 70%

Interior target temperature N/A 23ºC 21ºC

Interior initial temperature N/A 35ºC 10ºC

1,2Solar load N/A 850W/m

2 0W/m

2

Per passenger heat load N/A 67W 67W

3Per passenger humidity load N/A 55W 55W

Defined passenger load Half seated plus driver

Door openings Open one door, both halves, at every stop

Fresh air intakes Auto (open if auto is not available)

Lighting conditions

Front lights OFF Dipped headlights Dipped headlights

Back lights OFF ON ON

Position lights on sides OFF ON ON

Turn indicators, fog lights OFF OFF OFF

Registration plate lights,

destination plate lights

ON ON ON

Interior lighting (roof, steps) OFF ON ON

Miscellaneous conditions

Seat heaters, Windshield wipers, Windshield heating,

Power windows and ―similar‖

auxiliaries

OFF / not used OFF / not used OFF / not used

Average power to ―add-ons‖

such as GPS-navigators,

monitors with bus stop

information, radio etc not

covered elsewhere

0 W 250 W 250 W

Note 1: Parameter that should be time varying to become more realistic in the modelling

Note 2: Defined as resulting incident vertical radiation

Note 3: Parameter proposed in SAE J2711 and not quite understood if it is specified in a relevant way. The

parameter should be revisited when actual modelling work of climate system is under way.

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3.2.4 Bus parameters

The effects on fuel economy from the improvements developed / utilised in the project will be

investigated on a Volvo 7700 city bus model built on chassis model B9L. Parameters given in this

subchapter will only be valid for this particular model.

Some parameters for this bus model are listed in the table below:

Parameter Value

Bus length 12m

Body width 2.55m

No of doors 2 (assumption)

Passenger capacity 90 (assumption)

Engine Rear-mounted, 6-cylinder, 9-litre diesel D9B260 191kW (260hp)

Gross weight 18900 kg

Gearbox 6-speed automatic

Below the engine speed is plotted versus time. The engine speed is the output from Matlab+Simulink

simulations with the different driving cycles discussed in §3.2.2 as input to the simulations.

Note: Due to implementation details in the simulation environment at Volvo, which is distance based,

the resulting speed versus time values are not exactly the same as in the driving cycle that was used as

input to the simulations. The differences are however considered small enough.

~Manhattan cycle - Engine speed for chassis model B9L

400

600

800

1000

1200

1400

1600

1800

2000

0,00 200,00 400,00 600,00 800,00 1000,00

Time (s)

En

gin

e s

peed

(rp

m)

SORT1 - Engine speed for chassis model B9L

400

600

800

1000

1200

1400

1600

1800

2000

0,00 20,00 40,00 60,00 80,00 100,00 120,00 140,00

Time (s)

En

gin

e s

peed

(rp

m)

~Orange County Transit cycle - Engine speed for chassis model B9L

400

600

800

1000

1200

1400

1600

1800

2000

0,00 200,00 400,00 600,00 800,00 1000,00 1200,00 1400,00 1600,00 1800,00

Time (s)

En

gin

e s

peed

(rp

m)


400

600

800

1000

1200

1400

1600

1800

2000

0,00 20,00 40,00 60,00 80,00 100,00 120,00 140,00 160,00 180,00

Time (s)

En

gin

e s

peed

(rp

m)

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~UDDS (Urban Dynamometer Driving Cycle) - Engine speed for chassis model B9L

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

0,00 200,00 400,00 600,00 800,00 1000,00

Time (s)

En

gin

e s

peed

(rp

m)


400

600

800

1000

1200

1400

1600

1800

2000

0,00 50,00 100,00 150,00 200,00

Time (s)

En

gin

e s

peed

(rp

m)

Data can be found in annex file ―Driving_cycles_buses_EE-VERT.xls‖.

The information about engine speed above should be useful when designing the generator. In the

generator design it should also be useful to know the average electrical power that is generated by the generator during normal driving / idling. This figure is normally between 2 kW and 3 kW (data from

actual measurements).

3.2.5 Conclusion

The driving cycles recommended in the SAE J2711 standard and presented in §3.2.2 shall, as a baseline, be used for simulations. In order of preference in the simulations the following cycles shall

be used:

Manhattan cycle


UDDS

The additional boundary conditions to be used are listed in §3.2.3. Three different cases are listed. In

order of preference in the simulations the following cases shall be used:

Case 2: ―Summer‖

Case 1: ―Standards‖

Case 3: ―Winter‖

Volvo bus model 7700 built on chassis B9L has been chosen as reference bus. Some parameters for this bus model are listed in §3.2.4, e.g. the engine speed has been obtained from Matlab+Simulink

simulations for the different driving cycles.

3.3 Link between the components operational mode and the mission profile

The purpose of this subtask is to investigate the interaction between the defined vehicle mission profile and the components operational characteristics. Due to the multitude of influencing factors a simplified

procedure to assess the fuel consumption associated with the components is proposed.

3.3.1 Detailed and simplified procedure for the analysis

Figure 20 gives the detailed analysis structure if the additional fuel consumption of a generator is to be

investigated. In a similar manner it is to be done for other auxiliaries like the water or oil pump.

EE-VERT


Vehicle velocity [km/h]

on mission profile Generator speed [rpm]

0

50

100

150

200

0 3000 6000 9000 12000 15000 18000

>70%

>40%

>65%

>60%

<40%

>55%

>50%>45%

Generator efficiency

characteristic

Generator speed [rpm]

IG [A]

Gear shift strategy

Transmission ratio

Idle speedPG_el

1/UG

UG

Ge

ne

rato

rtr

an

sm

issio

nra

tio

Engine speed

nG

PG_mech

PDriving

Pmech Auxiliaries

nMot

MMot PMot

Belt

effic

iency

MMot k*pme

IGTemperature

Diesel=840g/l

Specific fuel consumption map

0

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

9.000

0.000.08

0.160.25

0.339.41

9.499.58

10.0610.14

10.2310.31

Time [hours]

Monday -> Friday

0,00

20,00

40,00

60,00

80,00

100,00

120,00

140,00

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.25

0.27

0.29

0.31

0.33

0.35

0.37

9.39

9.41

9.43

9.45

9.47

9.49

9.51

9.54

9.56

9.58

10.00

10.02

10.04

10.06

10.08

10.10

10.12

10.14

10.16

10.19

10.21

10.23

10.25

10.27

10.29

10.31

22.03

hours

Veh

icle sp

eed

[km

/h

]


Home to work Work to home Free time

9 hours stop night stop

Time [hours]

Fuel

consumption

Speed [rpm] (a)

Mea

n e

ffec

tiv

e p

ress

ure

(~

To

rqu

e)

Fuel consumption [g/kWh]

Engine speed [rpm]

Me

an

eff

ecti

ve

pre

ssu

re (

~ T

orq

ue

)

120

90

60

30

0

Figure 20. Detailed analysis of the additional fuel consumption of a generator

The driving cycle determinates together with the gear shift strategy and the generator transmission

ratio the speed of the generator. The speed of the generator, the actual voltage level, the actual current

demand in the power net and nevertheless the temperature determine the operation point in the generator’s efficiency characteristic diagram. Under consideration of the belt efficiency the additional

mechanical power demand of the generator on the ICE results. To calculate the additional fuel

consumption that is caused by the generator one important parameter is the actual operation point of the ICE. The actual operation point of the ICE is influenced by the engine speed and the overall power

demand which is composed by the power demand for the driving and also by the power demand for all

other auxiliaries.

From the above considerations it can be seen that due to the multitude of influencing factors and interdependencies a detailed system simulation is necessary to investigate the link between the

components operational mode and the mission profile. But the better the operational modes in a

specific driving cycle are understood and the better the driving cycle represents a huge amount of vehicles the more it makes sense to optimise a component’s operational mode to the specific driving

conditions. Currently the standard components are not optimised is a system approach. They are

designed to guarantee their function over a wide range of speed. Therefore in most operation points the

components run in a not efficient operation area. A detailed system analysis and simulation with the goal to analyse and optimise the components

operational modes is task within work package 2 and 3. Many of the components models will be

developed during the project. Due to the multitude of influencing factors a simplified procedure to assess the fuel consumption associated with the components is proposed at this stage of the project. In

the following sections the generator, the oil pump, the water pump and the fuel pump is considered.

For a first qualitative estimation of the link between the components operational mode and the mission profile only the main influencing parameters like the speed range are considered.

3.3.2 Generator

A generator converts mechanical energy to electrical energy, generally using electromagnetic

induction. The generator is mechanical driven by the ICE through a belt at a fixed ratio. Typically a

state-of-the-art generator has an efficiency of about 60%. But due to the low electrical power demand

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and the efficiency characteristics the average efficiency of a standard generator is only about 40 or

45%. The efficiency on the system level is lower due to the efficiency of the belt and the ICE.

On this measurements on NEDC on different C-segment vehicles have been done by CRF. The measurements show that the typical generator efficiency is just about 35% on NEDC.

0

5

10

15

20

25

30

35

40

45

2.100 2.700 3.300 3.900 4.500 5.100 5.700 6.300 6.900 7.500 8.100

Generator speed range [rpm]

Re

lati

ve

fre

qu

en

cy

[%

]

Monday -> Friday Saturday Sunday

Figure 21. Histogram of generator speed distribution on the EE-VERT mission

profile

Main influencing parameters on the generator operation characteristics are the generator speed, the

current level and the temperature. Figure 21 shows the statistical analysis of the generator speed on the

EE-VERT mission profile. The distribution indicates that between Monday and Friday and on Sunday the generator runs mainly in a speed range between 3.900 and 6.900 rpm. On Saturday the generator

runs mainly in lower speed ranges due to many stop phases and lower vehicle speed. Regarding the

temperature characteristic of the generator this information is not available at this stage of the project.

Hence, for a first estimation it is assumed that the temperature profile is similar to other currently existing cycles.

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0

5

10

15

20

25

30

35

40

45

Rel

ativ

e fr

equ

ency

[%

]


Figure 22. Histogram of generator speed distribution on the EE-VERT mission

profile

Figure 22 combines the operation characteristic of the generator which is used in the reference vehicle

Alfa Romeo and the speed distribution from figure 3.3.2. It is visible that the generator operates mainly in an area where the efficiency is only about 45% and thus 15% lower than the maximum possible

efficiency of 60%. Furthermore, the figure shows that the generator is designed to work over a wide

range of speed up to 15.000 rpm or even more. But in most cases the generator runs in lower speed ranges on the mission profile. This influences the volume/weight to power ratio. Out of the

considerations one is able to propose some improvements for the EE-VERT generator.

To increase the efficiency another machine type with an optimisation of the efficiency

characteristic on the speed range of the mission profile is proposed. An increasing of the

average efficiency by 20 or 25 % is feasible in this way.

To reduce the speed range of the generator a two-speed gear or a three-speed gear is proposed

between engine and generator. This would also improve the volume/weight to power ratio.

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3.3.3 Motor oil pump

Lubricating oil creates a separating film between surfaces of adjacent moving parts to minimize direct

contact between them, decreasing friction, wear and production of excessive heat, thus protecting the engine. The oil pump powered by the vehicle engine, pumps the oil throughout the engine to the

bearings, including the oil filter. The oil pump is usually a gear type driven by the camshaft or

crankshaft, or a rotor type. Oil pressure varies quite a bit during operation, with lower temperature and

higher RPM's increasing pressure to a maximum of about 4.5 bar.

An oil pump is designed to guarantee its function over a wide range of speed. Hence, in most operation

points the component runs in a not efficient operation mode. State-of-the-art oil pumps work with constant displacement. They are driven in direct dependence to the engine speed. An oil pump is

dimensioned for the worst-case hot idling. In all other operation areas the oil pressure has to be

regulated and limited by a bypass. That causes losses and a reduction of the efficiency. Typically a state-of-the-art oil pump has an efficiency of about 60 - 80% on component level. But the efficiency on

the system level is lower due to the above described characteristics.

Dis

pla

cem

en

t

Dis

pla

cem

en

t

Engine speed Engine speed

Losses

Losses

Oil pump w

ithconstant displacement

Deficit

Engine oil demand at 25°C

Engine oil demand at 125°C

Oil pump with

constant

displacement

Figure 23. Engine oil demand and actual oil pump characteristic

In Figure 23 it can be seen that the currently oil pump operation mode produces high losses especially

during cold engine operation and at higher engine speeds. The actual engine oil demand depends on the engine speed, the load, component tolerances and especially on the engine operation temperature.

To realise a high efficient operation the oil pump should adapt the displacement on the actual oil

demand.

0

5

10

15

20

25

30

35

40

45

700 900 1.100 1.300 1.500 1.700 1.900 2.100 2.300 2.500 2.700

Engine speed range [rpm]

Re

lati

ve

fre

qu

en

cy

[%

]


Figure 24. Histogram of engine speed distribution on the EE-VERT mission

profile

EE-VERT


To estimate the influence of the oil pump operational mode on the fuel consumption during the

mission it is necessary to know the influencing parameters especially the temperature characteristic of the engine and the engine speed range during the mission. Figure 24 shows the histogram of the engine

speed distribution on the EE-VERT mission profile. Between Monday and Friday and on Sunday the

engine runs mainly in a speed range between 1.500 and 2.300 rpm. On Saturday the engine runs mainly in lower speed ranges due to many stop phases and lower vehicle speed.

Regarding the temperature characteristic of the engine this information is not available at this stage of

the project. It is a measurement task for WP2 and WP3. But for a first estimation it is assumed the engine temperature characteristic is similar to other currently existing cycles.

Figure 25 compares two concepts for the operation mode of the oil pump. The red curve shows the operational mode of a currently oil pump while the green curve shows a proposed operational mode for

a pressure stage regulated oil pump which could be used for the EE-VERT vehicle. A pressure stage

regulated oil pump reduces the losses especially in the middle and high range of speed. It is a proposed concept to be investigated and developed during the project.

0

500

1.000

1.500

2.000

2.500

0 1.000 2.000 3.000 4.000 5.000 6.000

Engine speed [rpm]

Me

ch

an

ica

l p

ow

er

de

ma

nd

[W

]

Bypass unregulated Pressure stage regulated

Pressure

switching

Figure 25. Comparison of two concepts for the oil pump operational mode

It can be seen that due to the engine speed distribution on the EE-VERT mission profile the proposed

operational mode for the oil pump can reduce the mechanical power demand in the main operation area

of the engine to the half. At an average engine speed of 2.100 rpm for example the mechanical power

demand is reduced from 460 W to approximately 230 W. An additional positive effect could come from an improved thermal management.

3.3.4 Water pump

Water pumps are typically used on vehicles today to provide heat transfer means for an engine during

operation. The water pump circulates cooling through the engine. Water pumps are typically belt

driven by the engine at a fixed ratio. Hence, the water pump rotates at a speed proportional to a

EE-VERT


rotation speed of the engine. Since 2004 also electrical water pumps are used in some BMW vehicles.

Typically a state-of-the-art water pump has an efficiency of about 35%. The efficiency on the system

level is even lower due to the characteristics of the cooling circuit and the belt’s and ICE’s efficiency.

A water pump is designed to guarantee its function over a wide range of speed. Hence, in most

operation points the component runs in a not efficient operation mode. State-of-the-art water pumps work with engine speed proportional volume flow. To estimate the influence of the operational mode

on the fuel consumption during the mission it is necessary to know the influencing parameters

especially the temperature characteristic of the cooling circuit and the internal combustion engine and

the engine speed range during the mission. Regarding the temperature characteristics of the cooling circuit and the engine they are not available at this stage of the project. It is a measurement task for

WP2 and WP3. But the influence of the engine speed range during the mission can be considered.

0

200

400

600

800

1.000

1.200

1.400

1.600

1.800

2.000

0 1.000 2.000 3.000 4.000 5.000 6.000

Engine speed [rpm]

Me

ch

an

ica

l p

ow

er

de

ma

nd

[W

]

0

20

40

60

80

100

120

140

160

180

200

Vo

lum

e f

low

ra

te [

l/m

in]

Mechanically driven Electrically driven Volume flow rate

0

5

10

15

20

25

30

35

40

45

Rela

tive

frequ

ency

[%]


Engine speed distributionR

ela

tive

fre

qu

en

cy

[%]

700 2.700Engine speed

Figure 26. Comparison of two concepts for the water pump operation

Figure 26 compares two possible solutions of the water pump. The blue curve shows the operational

characteristic of a mechanically driven water pump while the green curve shows the operational

characteristic of an electrically driven water pump which is used in some modern vehicles for example. The benefit of an electrically driven water pump is that it is adjustable on the actual cooling demand of

the internal combustion engine.

In Figure 26 it can be seen that due to the engine speed distribution on the EE-VERT mission profile it

doesn’t matter if the water pump is driven electrically or mechanically if one consider only the power

demand. The power demand of the two pump types in the lower ranges of engine speed is nearly the same. Hence, a potential to reduce fuel consumption by only replacing the state-of-the-art

mechanically driven water pump with an electrically driven one is hardly given.

Nevertheless, in higher speed ranges exists a potential to reduce the fuel consumption. An additional benefit can come from an optimised thermal management as well. A motor which reaches faster his

optimal thermal operation point produces lower CO2 emissions. If the thermal management of the

engine is improvable with an electrically driven water pump is to be investigated during the project.

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3.3.5 Fuel pump

An fuel pump pumps the fuel from the fuel tank to the engine and delivers it under pressure to the fuel

injection system. Currently the fuel pump is driven electrically. It is usually located inside of the fuel tank. A benefit to placing the pump inside the tank is that it is less likely to start a fire. Liquid fuel will

not explode and therefore submerging the pump in the tank is one of the safest places to put it.

Typically a state-of-the-art fuel pump has only a low efficiency between 20 and 40 %.

Currently the mission profile has hardly an influence on the electric fuel pump operation. In most cars

the fuel pump delivers a constant flow of gasoline or diesel to the engine. The fuel flow is typically

between 60 and 140 l/h for a passenger car. Fuel that is not used is returned to the tank via a bypass. The fuel pump is in operation as long as the electronic ignition system is in operation as well. They

produce constant fuel pressures up to 4.5 or even 6.5 bar. The current demand is between 3 and 12

amps. Hence, the electrical power demand is between 42 W and 168 W. A potential to reduce the fuel consumption with another operation mode of the fuel pump is given because the fuel pump is in

operation during the whole time of vehicle operation and this on a pressure level that is not needed

under lower engine load conditions. It is to be investigated in WP2 if an optimised operational strategy

for the fuel pump could bring an benefit for example through a controlled pulse-width modulated operation that is based on the real fuel pressure demand of the engine.

3.4 Extrapolation methodology from condensed/standard cycles

Presently, when standard cycles are examined, the simulation has to derive engine speed and engine

torque from the given vehicle speed and results have to be matched with real time data. In addition to that each auxiliary component has to be modelled independently in order to investigate different

operation strategies within the electrical system.

3.4.1 Simulating mission profile providing only vehicle speed (NEDC)

Currently used mission profiles (NEDC) presently define only the vehicle speed over time. In order to derive the required fuel consumption in the simulation it is therefore necessary to:

include all external losses (friction losses, air resistance, acceleration losses, probably turning

losses as they increase friction….) and transmission factors;

consider gear shift operations and idle speed;

in order to derive appropriate engine speed and required torque (compare Figure 27). With this data the actual fuel consumption can be read from the engine efficiency diagram.

Further the simulation models of these components must assure real vehicle behaviour. Accordingly

component models have to be matched with real vehicle parameters and therefore the simulation results will correspond only to this concrete vehicle.

EE-VERT


External

Data

Pool

MATLAB

Simulink

System

Vision

Engine Efficiency Diagram

Ma

in T

orq

ue

[N

m]

Angular Speed

[rpm] Specific

Consumptio

n

[g/kWh]

External

Data

Pool

+

Altitude [m]Vehicle Losses and

Capability

G

velocity [m/s]

Angular Speed

(ration of belt)

[rpm]

Electric

Domain

- +

Battery

Consumer I

Consumer II

Consumer III

Fair

Fg cos(β)

β

froll

m, A, cw

Fele = Fg sin(β)

Fg

Ffri

Figure 27. Schematic overview of the simulation of fuel consumption

Driver Engine

Components

Electric

Environment

Manual

Gearbox

Brake

Tire

External Looses

SVX

Matlab Connection

SVX

Matlab

Connection

Mass

V_ref

System Definitions:

Mission Profile

Rotational Nature

Translational Nature

Electrical Nature

SVX I/O

Figure 28. Simulation of fuel consumption on NEDC using a driver model, done

with VHDL-AMS in System Vision

EE-VERT


The implemented simulation given in Figure 28 computes the fuel consumption on NEDC (concrete

cycle is determined by the input vehicle velocity V_ref). Because of missing definition of engine speed and engine torque these values are derived within the simulation, taking into account various external

losses and introducing a gear shift strategy.

Although this simulation is not perfectly matched to real world values it shall be given in this report to give an impression of the applied methodology.

The only possibility is to derive engine speed and torque from the vehicle speed which is defined by a

mission profile. Therefore engine speed and torque are simulated/computed values and do not

correspond for 100% to the real values.

SVX

real

svx_simulink_real_cons_2

SVX

real

svx_simulink_real_cons_1

SVX

chan

svx_simulink_chan

SVX

acc sock

svx_simulink_acc_sock

SVX

platform

svx_simulink

rad/sec to 1/min

1

s

g/s -> g

g/kWh * kW --> g/hg/h --> g/s

W to kW1

W to kW

Scope1

Product2

Lookup

Table (2-D)1

1e3

Constant5

60/(2*pi)

Constant4

0.75*1000

Constant2

60*60

Constant1

|u|

Abs

TORQUE [Nm]

TORQUE [Nm]

CONSUMPTION [g/kWh]

CONSUMPTION [g/kWh]

ANGULAR_SPEED

SPEED [1/min]

SPEED [1/min]

POWER [kW]

FUEL [l]

Figure 29. Determination of fuel consumption in MATLAB according to the

engine efficiency diagram

The NEDC has been simulated, once without any electrical load, a second time with a continuous load of 1 kW. An exemplar result is given in the following figures for the first 80 seconds of the NEDC.

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Figure 30. First 80 seconds of NEDC

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3.4.2 Implementing EE-VERT mission profile

Implementing the EE-VERT mission profile (Monday to Friday, home to work 40min) allows a

simplification of the simulation given in Figure 27. In addition to the vehicle speed, the engine speed is

available as input, therefore the simulation has to calculate the power provided by the engine PICE.

All data will be import by MATLAB and over SVX transferred to System Vision. The calculation of

external losses is done by solving dynamic equations for air losses and friction etc. All belt driven

components are idealized and consume constant power.

The generator model implements the specific efficiency diagram, but only considering efficiency

dependent from generator speed. Parameters as temperature effects, voltage, load and excitation are not yet considered.

Figure 31. MATLAB is used to import and pre-process data from excel sheets and

calculate fuel consumption

Engine has to deliver torque for propulsion as well as torque to drive all belt driven auxiliaries:

ICE

ICEICE

ICEbeltaux

prop

auxpropICE

PT

TP

vFP

PPP

where:

P: power [W]

T: torque[Nm]

F: force [N]

ω: speed [rad/s]

v

: vehicle speed [m/s]

Equation 1: ICE power flow

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Figure 32. System Vision simulation implementing Equation 1

In Figure 32 an impression of the applied methodology is given.

The SystemVision and MATLAB Co-simulation has to be improved, because at the moment the data

exchange between these tools happens too often (ms), which requires a lot of computation time.

A MATLAB/SystemVision co-simulation was used, because data pre-processing and the integration of

an engine efficiency diagram is easier done in MATLAB than in SystemVision. To avoid problems

occurring due to actually unnecessary data transfers, the whole simulation of the power flow could in

fact also be done in SystemVision only. The tool would allow the simulation of the hardware as well as the concurrent development of the algorithms that control it. For that, SystemVision accepts multiple

language formats, this is C-programming language, Spice models, and VHDL_AMS. VHDL_AMS is a

standardized hardware description language to model and simulate digital, analog and mixed-signal systems consisting of electrical and non-electrical parts.

Nevertheless, as mentioned before, it is possible to perform a co-simulation: using MATLAB which is

signal-flow-oriented implementing the control part, together with SystemVision wich allows concurrent simulation of multiple technology components.

As it is also possible to simulate the power/energy flow only in MATLAB/Simulink this could be done as an alternative, but VHDL_AMS seem to be best suited for this application.

Belt driven

auxiliary units

External car

characteristics

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4. ROLLBENCH FUEL CONSUMPTION PROCEDURE GUIDELINES

This procedure shows the most important steps about fuel consumption tests on a roll bench: it’s useful

both for technicians safety during the test and for the obtainment of a good result of the test itself. To limit the spread of fuel measures when working in a rollbench, care must be paid to many aspects

during test. Typical at least 3 repetition of the mission under study are required. The same test

conditions must be reproduced during each mission. The procedure is complete, but generic: so it’s important to adapt some steps to the vehicle on which

tests are being done.

Vehicle setup on rolls

Tires pressure check: vehicle nominal pressure

Battery state of charge check. Recharge battery current during cycle (engine on) should be less then 5 A.

Open valves of high and low pressure circuits of air conditioning system and verify

AC-Coolant circuit charge Verify the correct coast down setting of the roll bench

Before test starting

Verify the absence of anomalous electrical loads when the vehicle is turned off

Verify the coherence between vehicle internal temperature and required test

temperature. Vehicle mean internal temperature (measured through the onboard acquisition system) must be constant and similar to roll bench box temperature.

If the test is an homologated one, all vehicle electrical loads must be turned off.

Bonnet closed.

a) Air conditioning system turned off

To wind down all vehicle windows.

b) Air conditioning system turned on

An engine warm up is necessary with all vehicle windows wind down.

Air conditioning control must be set: ―Full auto‖, ―Automatic air distribution‖, ―Monozone‖, ―Automatic airflow‖, 22°C. Then wind up all vehicle windows.

Connect onboard acquisition system to an external supply in order to avoid that the

external acquisition system turns off during cranking phase and drain power from

vehicle electrical powernet.

c) Cold test

Verify that data acquisition starts before test beginning. This is important because it is

possible, due to legislative constraint, to do only one cold test in 24 hours: so, acquisition errors must be avoided.

3. At the end of the test

Download data from external acquisition system

Vehicle must be set into a key off state

Verify the absence of anomalous electrical loads Disconnect positive battery pole

EE-VERT


5. REQUIREMENTS ON MISSION PROFILES REGARDING THEIR

INTEGRITY FOR DERIVING STIMULI-SET FOR PREDICTIVE

CONTROL SIMULATION

In order to derive suitable mission profiles to measure the additional fuel consumption for auxiliary

components it is necessary to consider the following points.

When the cycle is defined for cold engine start conditions (to be more realistic) information

about the engine performance for this condition must be provided for the simulation, in order

to obtain meaningful results. Further simulations can show that starting with an engine at operation temperature will lead to reduced fuel consumption.

Easier for simulations is to define a cycle starting with an engine at operation temperature as additional temperature modelling will be avoided. This approach is also not too far away from

reality, as power from the electric household system could be used for pre-heating. A

disadvantage although is, that not all the loads will be considered in this case (e.g. no energy is

required for pre-heating circuits)

It is definitely not helpful to estimate an average excess fuel consumption in [l/h] for each Watt

consumed by an auxiliary component, without considering the efficiency of neither the engine nor the component (as it was done in ARTEMIS).

It is required to define the estimated power consumption of each electrical load over the

time of the cycle and/or suitable boundary conditions. This should be based on real life observations (Winter/Summer cycle for air conditioning system, independent definitions for

climate compressor and fan) and is best expressed as percentage of maximal power

consumption. The defined power consumption of the auxiliaries does not require a very detailed on/off switching characteristic, because freedom must be left to use various operating

strategies. Some loads may require only boundary conditions (e.g. cabin/outside

temperature/humidity and solar radiation for air condition) and others may require more or less

defined power consumption over time (light, seat heating, …).

For simulation purpose it is required that in addition to the vehicle speed at least the gear shift

ratio and the transmission ratio is provided/defined in order to derive engine speed. An

elevation profile expressed through a defined power for propulsion allows the prediction of energy that can be recovered during engine overrun. Vehicle speed and propulsion power can

then be used to simulate engine speed and engine torque.

Instead of vehicle speed and propulsion power it would be perfect for simulation purpose to

directly provide engine speed and engine torque. That means that all mechanical losses and

efficiencies could be disregarded and only the electrical system is simulated. This would lead

to most accurate results.

It is out of question that simulation reaches 100% accuracy in overall fuel consumption, as there are too

many external factors to be modelled. But what is possible and necessary (and what hast o be assured

during the definition of a new driving cycle) is, that for additional on board energy production the

additional simulated fuel consumption can be matched with an additional measured fuel

consumption. That means that the definition of the cycle must impose a run without auxiliary loads,

and one with the use of auxiliary loads with a given average power demand/boundary conditions.

EE-VERT


5.1 Suggestions for driving cycle concerning Air-condition

At outside temperatures above 20°C, the air must be cooled to achieve the desired interior

temperatures. The most relevant power consumers of the air condition system are the climate

compressor (consuming approximately 2 kW – 5kW ) and the blower (~120 W). The compressor as the ―heart‖ of the coolant circuit is a belt driven pump that is fastened to the engine

via an electromagnetic clutch (in all conventional vehicles). It is responsible for compressing and

transferring refrigerant gas and has an efficiency expresses through the COP(Coefficient of

performance) of about 1,5 – 2.

State of the art is according to ARTEMIS research, that for high loads and high speed the mean speed

has little impact on excess fuel consumption. Research with electrically driven A/C compressor can achieve an improvement of the systems COP

from presently 1,5 – 2 to >3 in a properly optimized system design with dynamically controlled

operation. The technique is based upon formulating optimization objective functions from linear combinations of critical design performance parameters hat characterize independent design goals.

The simplified ARTEMIS model of the air conditioning system (see 2.3.1) can be regarded as

proportional to the average energy consumption to keep a specified temperature inside the passenger cabin.

Id country city longitude latitude Köppen

class

average

temperature

1 AUT GRAZ 15.43 47 Dfb 9.5

a1 a2 a3 a4 a5 a1

-0.863 0.0402 -0.0376 0.0334 -0.00164 -0.863

Table 11. Parameters for hfc according to ARTEMIS

hourly hfc accordingt to ARTEMIS ~ Power demand

-0,15

-0,1

-0,05

0

0,05

0,1

1 6 11 16 21 h

Figure 33. Example of cooling capacity of air condition system (based on

EE-VERT


ARTEMIS model)2

With such information as given in Figure 33, which relies on observations of the reality, a new mission

profile has to be developed, including other relevant information given in chapter 5. It is not helpful to estimate average excess fuel consumption in [l/h] for each Watt without considering

the efficiency neither of the engine nor of the auxiliary component (as it was done in ARTEMIS).

5.1.1 Deriving a mission profile using measured equivalent heat sources and thermal resistance

The intention of FH-J is to develop a mission profile (only a temperature profile, not a speed profile) using radiation data from Photovoltaic Geographical Information System

3, which is a main contributor

to the implementation of solar energy with head quarters in Ispra, Italy.

Ambient temperature as well as solar radiation energy leads to temperature rise/fall in the passenger

compartment. This defines the cooling/heating capacity which has to be provided by the air conditioning system. The approximate relation is given by Newton’s Law of Cooling.

Newton’s law of cooling:

TAhdt

dQ

where:

Q: thermal energy [J],

h: heat transfer coefficient,

A: surface area,

ΔT: Temperature difference

Thermal transfer can be easily modelled by using equivalent electric circuits (Figure 34).

UTemp

URad

Rth

IQ

Measurement

Data from PVGIS

Measurement

Figure 34. Thermal circuit describing heat transfer between ambient temperature

and vehicle compartment.

The analogy results from the similarity of the differential equations for thermal and electrical processes

(both have the same time constraints):

UTemp: Temperature difference [ΔT]

URad: equivalent source describing solar radiation

2 hfc>0 always, directly taken from ARTEMIS. Negative values indicate that no cooling is necessary

3 http://re.jrc.ec.europa.eu/pvgis/about_pvgis/about_pvgis.htm

EE-VERT


Rth: Thermal resistance of the car [K/W]

IQ: thermal energy [J]

Rth corresponds to the vehicle’s heat resistance. The larger the heat resistance the better the isolation.

The intention is to measure this resistance directly in the passenger car. This could happen by measuring the cooling-down (at night or in underground car park for example, as there is no solar

radiation and a constant temperature) of the cabin temperature after a pre-heating (for example in the

sun), for different vehicle speeds.

The source URad is the electrical equivalent source of heat describing the solar radiation which causes temperature rise/fall in the cabin. By measuring the temperature difference and the solar elevation and

irradiance [W/m2] (equipment available at FH Joanneum for measuring direct and diffuse radiation) the

equivalent heat source can be found.

In order to get reproducible measurement conditions for the air conditioning system it will be

necessary to reproduce the temperature environment on the roll bench. At the same time they shall be not only reproducible, but also very close to reality.

If there is no possibility to reproduce radiation (by using powerful spotlights) the measurement of URad

is done to find an alternative, which could be a radiator within the passenger compartment.

If there is no possibility to preset the environmental temperature on the roll bench either the temperature sensor of the car could be manipulated, representing UTemp.

Other than presetting environmental conditions, the required cooling capacity for the AC system has to

be derived according to Figure 34 and added to the vehicle speed in the mission profile as an input parameter (example given in Figure 35).

Sunday

0

20

40

60

80

100

120

140

0.00

0.01

0.03

0.05

0.06

0.08

0.10

0.11

0.13

0.15

0.16

0.18

0.20

0.21

0.23

0.25

0.26

0.28

0.30

0.31

0.33

0.35

0.36

8.38

8.39

8.41

8.43

8.44

8.46

8.48

8.49

8.51

8.53

8.54

8.56

8.58

8.59

9.01

9.03

9.04

9.06

9.08

9.09

9.11

9.13

hours

Veh

icle

spe

ed [k

m/h

]


Figure 35. Example for mission profile: vehicle speed with overlaid AC cooling

capacity (which is exemplarily taken from ARTEMIS)

EE-VERT


6. CONCLUSION

Current existing mission profiles and their coverage of vehicle realistic used has been analyzed,

especially in term of auxiliaries engagement. Lack of boundary conditions was found.

The usage of the vehicle in a realistic driving scenario and over a longer period than a single mission has been defined together with a realistic mission profile and a detailed description of related

auxiliaries usage. ARTEMIS project results were exploited for validation of the vehicle mission.

Difference in vehicle use is greatly marked between passenger cars and buses, so different reference

missions were defined for each of them. The link between the most important components and the EE-VERT mission has been investigated.

The possible fuel economy improvements due to electrification or different component solutions were

analyzed. Finally simulation/validation methodology for fuel saving evaluation using defined mission profiles

has been specified.

EE-VERT


REFERENCES

Bibliography

[1] J.S. Welstand et al, ―Evaluation of the Effects of Air Conditioning Operation and Associated

Environmental Conditions on Vehicle Emissions and Fuel Economy,‖ SAE Paper No. 2003-

01-2247, June 23-25, 2003

[2] J. Benouali, D. Clodic, S. Mola, G. Lo Presti, M. Magini, C. Malvicino, Fuel consumption of

Mobile air conditioning. Method of testing and results, The Earth Technology Forum,

Washington, April 2003

[3] S. Samuel, L. Austin, and D. Morrey, ―Automotive test drive cycles for emission measurement

and real-world emission levels: a review‖, Proceedings of the Institution of Mechanical

Engineers. Part D, Journal of automobile engineering, 2002, vol. 216, no7, pp. 555-56

[4] Electronic Code of Federal Regulations, Title 40: Protection of Environment, Part 86—Control

of Emissions from new and in-use Highway Vehicles and Engines, available at: Electronic

Code of Federal Regulations

[5] R. Farrington and J. Rugh, ―Impact of Vehicle Air-Conditioning on Fuel Economy, Tailpipe

Emissions, and Electric Vehicle Range‖, National Renewable Energy Laboratory, NREL/CP-

540-28960, September 2000

[6] V.H. Johnson, ―Fuel Used for Vehicle Air Conditioning: A State-by-State Thermal Comfort-

Based Approach‖, SAE 2002-01-1957, 2002

[7] R.B. Farrington, J.P. Rugh, and G.D. Barber, ―Effect of Solar-Reflective Glazing on Fuel

Economy, Tailpipe Emissions, and Thermal Comfort‖, SAE 2000-01-2694, 2002

[8] S. Roujol, Influence of passenger car auxiliaries on pollutant emissions, Artemis 324 report

(Report n° LTE 0502), February 2005

[9] R. Joumard and J.M. André, Campagne de mesure des émissions unitaires de polluants non

réglementés des véhicules particuliers, Rapport INRETS/LTE n°0408, July 2004

[10] P. Soltic and M. Weilenmann, Influence of electric load on the exhaust gas emissions of

passenger cars, Transport and Air Pollution, Graz, Austria, June 19-21, 2002

[11] Uhttp://www.dieselnet.com/standards/cycles/UT Descriptions of different driving cycles

(possibility to download driving cycle data).

[12] SAE J2711 – Recommended practice for measuring fuel economy and emissions of hybrid-

electric and conventional heavy-duty vehicles; Issued 2002-09; TUwww.sae.orgU T

[13] SORT – Standardised On-Road Test Cycles; D/2004/0105/16; TUwww.uitp.comU T

[14] Real-world driving cycles for measuring car pollutant emissions – Part A: The ARTEMIS

European driving cycles INRETS, Michel André, June 2004

http://ecfr.gpoaccess.gov/cgi/t/text/text-idx?c=ecfr;sid=6bf83ef5c5ab30e9a66d14ea074fa9d9;rgn=div5;view=text;node=40%3A18.0.1.1.2;idno=40;cc=ecfr



http://www.dieselnet.com/standards/cycles/

http://www.sae.org/

http://www.uitp.com/

EE-VERT


[15] ―Regulated‖ and ―Non-regulated‖ emission from Modern European Passengers Cars SAE

International, 2006 World Congress Detroit

[16] TUhttp://www.powerfromthesun.net/chapter5/Chapter5Word.htmUT

[17] TUhttp://re.jrc.ec.europa.eu/pvgis/apps/radmonth.php?lang=it&map=europeU T

Annex files:

[1] ―Driving_cycles_buses_EE-VERT.xls‖ on ProjectPlace in the folder

\\EE-VERT\WP1\WT1.2 Mission Profiles\Deliverable\

[2] ―Driving_cycles_passengercars_EE-VERT.xls‖ on ProjectPlace in the folder

\\EE-VERT\WP1\WT1.2 Mission Profiles\Deliverable\

http://www.powerfromthesun.net/chapter5/Chapter5Word.htm

http://re.jrc.ec.europa.eu/pvgis/apps/radmonth.php?lang=it&map=europe

EE-VERT Project title: Energy Efficient VEhicles for Road ... · 1.1 Background Due to the fact...

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