D21.3: Powertrain Model Validation

Supporting the driver in conserving energy and reducing emissions

Project reference FP7-ICT-2011-7, Grant agreement no. 288611

IP Coordinator Oliver Carsten, University of Leeds

Project co-funded by the European Commission

7th Framework Programme for Research and Development

Information and Communication Technologies

Low carbon multi-modal mobility and freight transport

D21.3: Powertrain Model Validation (Version 15; 2014-02-25)

Subproject SP2: Real Time Calculation of Energy Use and Emissions

Work package WP21: Development of Powertrain Models

Task Task no. 3: Powertrain Model Validation

Authors

Lydie NOUVELIERE, Qi CHENG, Luis CONDE, Oscar DIAZ, Olivier

ORFILA, Samuel RIOS, Guillaume SAINT-PIERRE, Cyndie ANDRIEU,

Philipp SEEWALD, James TATE, Philipp THEMANN, Adrian ZLOCKI

Dissemination level Public (PU)

Status Final

Due date 31/05/2012

File Name D21_3 Powertrain model validation_final.docx

Abstract

This report describes the experimental validation of the powertrain

models developed in the WP21. The experimental tests consider the

dynamical model of the vehicle and also the energy consumption

model. The structure of this report is divided into four parts

corresponding to the four powertrain configurations described in

WP21. Each part covers the powertrain model, its inputs and outputs,

and provides an experimental plan with the associated results. The

deliverable shows the appropriateness of the simulated powertrain

models to the measured signals obtained on the equipped vehicles.

D21.3: Powertrain Model Validation (version 15, 2014-02-25) i

Control sheet

Version history

Version Date Main author Summary of changes

1 15/01/2012 Lydie Nouveliere (IFSTTAR) Initial template with table of contents

proposal.

2 09/04/2012 IFSTTAR First contribution

3 12/04/2012 IKA First contribution

4 12/04/2012 IFSTTAR Contribution update

5 17/04/2012 IKA Contribution update

6 19/04/2012 IFSTTAR First document synthesis

7 23/04/2012 IFSTTAR Contribution and synthesis

8 25/05/2012 IFSTTAR Document synthesis

9 30/05/2012 All Final Draft version

10 31/05/2012 IFSTTAR Version 1

11 24/07/2012 IFSTTAR Version 2

12 11/03/2013 All Updated after EC review

11 15/03/2013 TNO Minor typos updated

12 19/03/2013 IFSTTAR Updated after EC review

13 23/05/2013 Thomas Ivens (TNO) Change dissemination level

14 17/11/2013 IFSTTAR Updated after EC review

15 20/02/2014 Samantha Jamson Updated after 2nd EC review

Name

Prepared by Lydie Nouveliere

Reviewed by Francisco Vicente (CTAG) and Frederic Christen (IKA)

Authorized by Davy Bijnen

Verified by Oliver Carsten

Circulation

Recipient Date of submission

Project partners 28/02/2014

European Commission 28/02/2014

D21.3: Powertrain Model Validation (version 15, 2014-02-25) ii

Table of contents

1. Introduction ....................................................................................................... 1

2. Main Objectives of WP21 in ecoDriver ................................................................ 2

3. PCo1a Powertrain Validation .............................................................................. 4

3.1 Experimental means ................................................................................................................. 4

3.2 Short description of the PCo1a powertrain model ................................................................... 4

3.3 Experimental plan ..................................................................................................................... 6

3.3.1 Assumptions .................................................................................................................................................... 6 3.3.2 Test scenarios .................................................................................................................................................. 7 3.3.3 Measured signals ............................................................................................................................................. 7

3.4 Experimental test track ............................................................................................................. 8

3.5 Experimental results ................................................................................................................. 9

3.6 Results analysis ....................................................................................................................... 12

3.7 Conclusion on PCo1a model validation................................................................................... 12

4. PCo1b Powertrain Validation ............................................................................ 13

4.1 Short description of the PCo1b powertrain model ................................................................. 13

4.2 Experimental results ............................................................................................................... 13


4.4 Conclusion on the PCo1b model ............................................................................................. 14

5. PCo2 Powertrain Model Validation ................................................................... 15

5.1 Experimental means ............................................................................................................... 15

5.2 Short description of the PCo2 powertrain model ................................................................... 15

5.3 Experimental plan ................................................................................................................... 16

5.3.1 Test scenarios ................................................................................................................................................ 17

5.4 Experimental results and analysis ........................................................................................... 18

5.4.1 Validation on IKA test track ........................................................................................................................... 19 5.4.2 Validation on “Aachen Runde” ...................................................................................................................... 21

5.5 Conclusion on the PCo2 model ............................................................................................... 26

6. PCo3 Power train Model Validation .................................................................. 27


6.1.1 Data logger..................................................................................................................................................... 28 6.1.2 Inertial navigation system .............................................................................................................................. 29



6.3.1 Assumptions .................................................................................................................................................. 34 6.3.2 Test Scenarios ................................................................................................................................................ 34 6.3.3 Measured signals ........................................................................................................................................... 34

6.4 Experimental test track ........................................................................................................... 35



D21.3: Powertrain Model Validation (version 15, 2014-02-25) iii


7. PCo4 Power train Model Validation .................................................................. 43



7.2.1 Driver model .................................................................................................................................................. 44 7.2.2 Hybrid Powertrain System ............................................................................................................................. 44 7.2.3 Drivetrain System & Vehicle Dynamics .......................................................................................................... 45 7.2.4 Supervisory Vehicle Controller....................................................................................................................... 46 7.2.5 Fuel Consumption Estimation ........................................................................................................................ 47 7.2.6 CO2 Emissions Estimation .............................................................................................................................. 48 7.2.7 Battery SOC Estimation .................................................................................................................................. 48





8. Models comparison in SP2 ................................................................................ 56

9. Implications for the ecoDriver project .............................................................. 59

References .............................................................................................................. 60

D21.3: Powertrain Model Validation (version 15, 2014-02-25) iv

Index of figures

Figure 1: Global diagram WP21 / SPs. ...................................................................................................... 2

Figure 2: IFSTTAR test vehicle, Renault Clio 3 estate. ............................................................................... 4

Figure 3: Schematic diagram of the PCo1a powertrain model in the global chain. ................................. 5

Figure 4: Tested route. .............................................................................................................................. 8

Figure 5: Experimental road slope and speed for PCo1a and PCo1b. ...................................................... 9

Figure 6: Selected gear and longitudinal speed of PCo1a. ..................................................................... 10

Figure 7: Engine speed of PCo1a. ............................................................................................................ 10

Figure 8: Engine torque of PCo1a. .......................................................................................................... 10

Figure 9: Instantaneous fuel consumption of PCo1a. ............................................................................. 11

Figure 10: Cumulated fuel consumption of PCo1a. ................................................................................ 11

Figure 11: Estimation error of cumulated fuel consumption. ................................................................ 12

Figure 12: Description of PCo1b modelling. ........................................................................................... 13

Figure 13: Experimental results (in green) compared to modelling (in blue). ........................................ 14

Figure 14: Schematic diagram of the DSG vehicle drive train model. .................................................... 16

Figure 15: IKA test track number 1 including intersection scenario. ...................................................... 17

Figure 16:Aachen runde (source: Google Earth). .................................................................................... 18

Figure 17: Velocity of measured and simulated test drive versus time. ................................................ 19

Figure 18: Absolute fuel consumption; measured and simulated. ......................................................... 20

Figure 19: Relative deviation of simulated fuel consumption compared to measured consumption. .. 21

Figure 20: Comparison of simulated versus measured signals. .............................................................. 23

Figure 21: Excerpt from the “Aachen Runde” to analyse the gear change modelling. .......................... 24

Figure 22: Excerpt from the “Aachen Runde” to analyse the gear change modelling in highly dynamic

situations. ................................................................................................................................................ 25

Figure 23: General overview scheme PCo3 model validation. ............................................................... 27

Figure 24: Data logged via CANalyzer. .................................................................................................... 28

Figure 25: RT3100 inertial sensor. .......................................................................................................... 29

Figure 26: RT3100 set up on Nissan Leaf. ............................................................................................... 30

Figure 27: Nissan Leaf test vehicle .......................................................................................................... 30

Figure 28: Tests done with CAN and RT3100 on the laboratory............................................................. 31

Figure 29: General model description. ................................................................................................... 33

Figure 30: Curve of the Nissan Leaf’s electric motor. ............................................................................. 33

Figure 31: CTAG test track. ..................................................................................................................... 35

Figure 32: Time vs. Vehicle Speed........................................................................................................... 37

Figure 33: Time vs. Slope. ....................................................................................................................... 37

Figure 34: Time vs. Motor Torque. ......................................................................................................... 38

Figure 35: Time vs. Battery Current. ....................................................................................................... 38

Figure 36: Time vs. Battery Voltage. ....................................................................................................... 39

Figure 37: Time vs. Power consumption. ................................................................................................ 39

Figure 38: Time vs. State of Charge. ....................................................................................................... 40

Figure 39: Schematic of the PCo4 parallel HEV forward facing powertrain model ................................ 44

D21.3: Powertrain Model Validation (version 15, 2014-02-25) v

Figure 40: Diesel engine map-based model, block diagram ................................................................... 45

Figure 41:Electric motor/generator map-based model, block diagram ................................................. 45

Figure 42:Supervisory vehicle controller model inputs-outputs ............................................................ 47

Figure 43: Vehicle speed profile and road gradient for three sample drive cycles ................................ 51

Figure 44: Comparison between simulation and experimental vehicle speed....................................... 52

Figure 45: Simulation and experimental results of (a) instantaneous CO2 emissions and (b) model

performance ........................................................................................................................................... 53

Figure 46: Simulation and experimental results of battery SOC ............................................................. 54

Figure 47: Battery SOC during deceleration and acceleration modes .................................................... 55

Index of tables

Table 1: WP21 powertrain configurations. ............................................................................................... 3

Table 2: Associated inputs/outputs minimum resolution and units for PCo1a. ....................................... 5

Table 3: Nomenclature for PCO1a longitudinal model. ............................................................................ 6

Table 4: Speed range adopted given a gear number for the fuel consumption measurements.............. 9

Table 5: CANcase presentation. .............................................................................................................. 28

Table 6: RT3100 specifications. ............................................................................................................... 31

Table 7: Parallel HEV model operating modes........................................................................................ 47

Table 8: Recorded parameters. ............................................................................................................... 49

Table 9: Simulation and experimental results of the average fuel consumption and CO2 emissions for

three sample drive cycles. ...................................................................................................................... 52

Table 10: Global comparison of the five powertrain models of SP2. ..................................................... 57

D21.3: Powertrain Model Validation (version 15, 2014-02-25) vi

Glossary of terms

Term Description

VE^3 Vehicle Energy and Environment Estimation

SC Scenario

SP Sub Project

WP Work Package

SoC State of Charge

PID Proportional-Integral-Derivative controller

HMI Human-Machine Interface

I/O Input/Output

LCV Light Commercial Vehicles

GPS Global Positioning System

1. Introduction

` D21.3: Powertrain Model Validation (version 15, 2014-02-25) 1

1. Introduction

The numerical simulation to tackle a new problem in vehicle dynamics, control or optimization is

generally the first step in the process of focusing. But the experimental application to validate the

simulations results is obviously the counterpart of this first step. The issue of the simulation is always

the validation of the simulation models with experimental tests using real or virtual scenarios which

are especially designed for the investigation of the models purposes.

The experimental tests are based on the powertrain models described in the D21.2 deliverable of

WP21. They are run with varying parameters to achieve the complete set of possible variations.

Each powertrain configuration is then tested on the VMC’s test tracks that are described. The

equipment of each vehicle is given. The report also specifies scenarios and test procedures for testing

the accuracy of the models against the real vehicle behaviour. These scenarios are relevant for vehicle

dynamics as well as for the vehicle energy consumption.

These powertrain models developed in SP2 will then be used in SP1 for the feedback strategies and

the conception of the HMI, in SP3 for the trials execution and particularly for the system integration

and piloting, in SP4 for the evaluation of the system.

The report is organized by powertrain configuration, showing experimental results about the models

running with several illustrations in speed, gear numbers, energy consumption and other specific

signals.

2. Main Objectives of WP21 in ecoDriver



The WP21 in SP2 is one of the first steps of development in the ecoDriver project. It is an essential

part that will feed other SPs in the near future of the project. The global diagram of the interactions of

WP21 with other WP’s is recalled in Figure 1.

Figure 1: Global diagram WP21 / SPs.

WP21 had the following main objectives:

To give the powertrain models specifications defining the inputs/outputs of the models, the

models parameters, the different intermediate components to be modelled.

To simulate the four powertrain models.

To validate the four powertrain models with real data by using test vehicles in the different

VMC’s of the project.

This document is the last phase of WP21 presenting the validation results. The four powertrain

configurations are noted in Table 1.



Table 1: WP21 powertrain configurations.

Designation Powertrain Configuration

PCo1 Middle class passenger car with a conventional powertrain and a manual transmission.

A distinction must be made between built-in system (PCo1a) and nomadic device (PCo1b)

for this configuration.

PCo2 Premium passenger car with a conventional powertrain and a DSG transmission

PCo3 Battery electric vehicle

PCo4 Hybrid electric bus for public transportation

In the next sections, each power train model is validated with experimental data.

3. PCo1a Powertrain Validation



The PCo1a regards a conventional light vehicle with a manual gearshift with a gasoline engine (Figure

2). This testing vehicle is described in the Annex A of the D21.1 deliverable.

3.1 Experimental means

Figure 2: IFSTTAR test vehicle, Renault Clio 3 estate.

To achieve the vehicle dynamics state, the sensors of the test vehicle are: vehicle speed and

acceleration sensors, engine speed sensor, accelerator pedal sensor and fuel flow meter. The speed is

recorded using the vehicle own odometer. The fuel consumption is acquired from the CAN bus

channel. Its resolution of measurements is 80 mm3. A RTMAPS (© INTEMPORA, France) platform is

used to guarantee the real-time data logging phase and registration.

3.2 Short description of the PCo1a powertrain model

The inputs and outputs integrated in this model have a minimum resolution as listed in Table 2. The

general schematic diagram of PCo1a in Figure 3 shows the interactions between the different sub-

components with the developed powertrain model and its I/O.



Table 2: Associated inputs/outputs minimum resolution and units for PCo1a.

* This minimum resolution will be defined later in view of the digital map that will be used in the

project. IFSTTAR use a minimum resolution of 1.50 m on the altitude data.

Figure 3: Schematic diagram of the PCo1a powertrain model in the global chain.

The longitudinal powertrain model for the PCo1a configuration is given by the following equation

(Equ. 1)

)(

)))sin(((222

2

dwtfet

xRetf

INNIMr

MgvSCMgCrTNNrv

(Equ. 1)

The instantaneous fuel consumption FC is modelled as a polynomial of the instantaneous engine

speed ωe and the instantaneous engine torque Te

Variable Unit Minimum Resolution

Vehicle position m < 3 m

Vehicle longitudinal speed km.h-1

0.015 km.h-1

Vehicle longitudinal acceleration m.s-2

0.06 m.s-2

Accelerator position % 0.5 %

Engine speed rpm 0.15 rpm

Engine torque Nm 0.5 % of max torque

Instantaneous fuel consumption ml.s-1

80 mm3

Road slope Degree To be defined *



0 if

0 if 2

4321

eee

eeeee

T

TTTFC

(Equ. 2)

where FC is expressed in l/s (litter per second) and β1, β2, β3, β4, α, γ, λ are constant parameters which

depend on the engine. These parameters can be estimated with FC measurement data using the least

squares method.

The parameters and variables in (Equ. 1) are defined in Table 3. The complete model equations are

detailed in deliverable D21.2.

Table 3: Nomenclature for PCO1a longitudinal model.

3.3 Experimental plan

3.3.1 Assumptions

For the PCo1a powertrain model and energy consumption model, the following hypotheses are

supposed:

The vehicle structure is rigid.

The slip at the contact surface between the wheels and the road pavement is neglected.

The clutch is locked.

Designation Description

V Vehicle longitudinal speed

r Wheel radius

Gearbox efficiency

Nf Differential ratio

Nt Gearbox ratio

Te Engine torque

M Vehicle mass

g Gravity

Cr Rolling resistance coefficient

Volumic density of air

S Frontal vehicle area

Cx Drag coefficient

Road slope

Iet Engine inertia

Idw Gearbox + Wheel inertia



The gear changing is instantaneous.

The neutral situation is not considered for the gearbox.

These assumptions are justified by the fact that we need to validate a realistic but simple model:

realistic because this is an obvious dimension needed for the validation; simple model because this

second dimension is imposed by the optimization methods that can be used in WP22, that is to say

that to be sure to obtain a realtime optimization.

3.3.2 Test scenarios

Three scenarios were chosen for the validation of the powertrain models:

SC1 : Reaching a desired speed vd - Starting at a null velocity, the driver has to accelerate to

reach vd then he decelerates until stopping on a null grade road. vd is from 30 to 130 km/h

with a speed step each 10 km/h. Every experiment is repeated 10 times. The 10 replications

are on the same trip.

SC2 : Constant speed scenario - The driver will drive at a given steady speed vc taken on the

interval 10 to 130 km/h, with a different gear ratio N. The duration of every test is about 1

minute. Every test is repeated 3 times. vd is from 10 to 130 km/h with a speed step each 10

km/h.

SC3 : Realistic scenario - On open road, the driver will travel 15 km at urban roads, interurban

roads and highway .

Scenario SC1 is interesting for the test of acceleration and deceleration phases on a plane road to

validate the acceleration/deceleration actions and limits for various reached speeds vd and the

reactivity of the model. Scenario SC2 is used for the test of a constant speed driving situation to have

an idea of the stability of the model with a constant speed, of the consumption response of the model

in this given situation and to validate the interaction with the gear box (gear changes). SC3 offers a

very realistic driving situation on a long trip with several variations of roads types and driving

conditions. These scenarios are regarded as a minimal set to be sure that the proposed model is

adapted to the ecoDriver needs and use.

3.3.3 Measured signals

The main measured signals are the vehicle longitudinal position, vehicle longitudinal speed, vehicle

longitudinal acceleration, accelerator position, engine speed and instantaneous fuel consumption. The

engine torque can be calculated from the powertrain model. Furthermore, the road data and the

cartography are needed, such as the road slope with the units of Table 2. These measured signals

were defined in D21.1 document.



3.4 Experimental test track

In this section, experimental results obtained with the RENAULT Clio III test vehicle from IFSTTAR VMC

are presented under several illustrations. The objective is to compare the simulation results with the

experimental results in order to validate the PCo1a powertrain model.

The experimental plan consisted in travelling on a dedicated route constituted of different road

profiles. This route has been travelled by several drivers (21 volunteers) in different driving conditions:

normal driving: the driver is driving with its own knowledge of how to daily drive without any

instruction from the VMC trainer.

ecodriving: it consisted in having a theoretical training about how to eco-drive before the

tests; the ecodrivers had no HMI or any real-time assistance during the tests but an oral

presentation of the classical eco driving rules. In this document, the measures obtained with

the 21 drivers are not detailed. Only an example of these results is given in the following.

Figure 4 and Figure 5 show the test track map and the road slope profile for these tests.

Figure 4: Tested route.



Figure 5: Experimental road slope and speed for PCo1a and PCo1b.

3.5 Experimental results

As each gear number has a corresponding speed range under which the engine is running well, Table 4

indicates the speed ranges for each gear number. For example, when the gear number is set to 1, it is

difficult to achieve a high vehicle speed. The results are obtained after some experimental tests.

Table 4: Speed range adopted given a gear number for the fuel consumption measurements.

The experimental results are shown in the following Figure 6 to Figure 10, given a gear number.

Gear number Speed range

1 from 10 km/h to 40km/h

2 from 10 km/h to 70 km/h

from 10 km/h

4 from 30 km/h

5 from 30 km/h



Figure 6: Selected gear and longitudinal speed of PCo1a.

Figure 7: Engine speed of PCo1a.

Figure 8: Engine torque of PCo1a.

0 200 400 600 800 1000 12000

1

2

3

4

5

Time (s)

Ge

ar

num

be

r

Gear number

0 200 400 600 800 1000 12000

10

20

30

40

50

60

70

80

90

Time (s)

Sp

eed

(km

/h)

Vehicle speed

0 200 400 600 800 1000 1200500

1000

1500

2000

2500

3000

3500

4000

Time (s)

En

gin

e s

pee

d (

rpm

)

Engine speed



Figure 9: Instantaneous fuel consumption of PCo1a.

Figure 10: Cumulated fuel consumption of PCo1a.

0 200 400 600 800 1000 12000

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Time (s)

Insta

nta

neo

us f

ue

l con

sum

ption

(m

l/s)

CAN bus

Estimation

0 200 400 600 800 1000 12000

0.2

0.4

0.6

0.8

1

1.2

1.4

Time (s)

Cu

mu

late

d f

ue

l co

nsum

ption

(L)

CAN Bus

Estimation



Figure 11: Estimation error of cumulated fuel consumption.

3.6 Results analysis

Figure 11 shows the estimation error of the instantaneous fuel consumption relative to the results

given in Figure 6 to Figure 10. As the vehicle speed is zero during the first 10 s of the experimental

test, the initial consumption is equal to the idle speed consumption. In most of the time the

estimation error of the instantaneous fuel consumption is less than 2 ml/s. The different peaks are

due to the gear changing. The powertrain model reaches a steady state after 1.40 minute after the

engine running or after 1.30 minute after a first longitudinal acceleration following the idle speed

phase. This steady state is reached with a mainly less than 10 % estimation error of the instantaneous

fuel consumption. The validation of the engine torque is not carried out at month 17 of the project,

because of an experimental means unavailability (measuring system not repaired after break). This

validation is not wanted in the project proposal but it will be done when the measuring system will be

available. The results could be added to the extended deliverable part regarding LCV and heavy trucks

models.

3.7 Conclusion on PCo1a model validation

The illustrated results show that the developed vehicle model corresponds to the real vehicle in its

entirety. The model represents the average behaviour of the vehicle longitudinal dynamics and its

energy consumption. The model of instantaneous fuel consumption gives good results most of the

time; the errors are acceptable in the real application. The cumulated fuel consumption has a good

tracking, even if between 100 s and 350 s for instance, there is a small deviation: it is due to high

speed transient right before time 100 s from 0 km/h to around 80 km/h. The powertrain model and

the energy consumption model are precise enough for the real applications in ecoDriver project and

work in real time.

0 200 400 600 800 1000 1200-4

-3

-2

-1

0

1

2

3

Time (s)

Insta

nta

neo

us e

stim

atio

n e

rro

r (m

l/s)

Estimation error

4. PCo1b Powertrain Validation



The experimental means and plan are the same as for the PCo1a powertrain configuration with the

same testing vehicle.

4.1 Short description of the PCo1b powertrain model

The philosophy of this model is to be versatile enough to represent the energy consumption of

different kind of vehicles. The basic principle consists in computing the pure mechanical consumed

energy with the Newton laws of motion according to the road and vehicle main properties (Figure 12).

Then, the specificities of the vehicle powertrain are taken into account through the efficiency term.

Figure 12: Description of PCo1b modelling.


The experimental results are illustrated on the Figure 13, comparing the real signals in green with the

simulated ones in blue. It represents the experimental vehicle speed on the top, the instantaneous

fuel consumption on the middle and the cumulated fuel consumption at the bottom.

Mechanical energyconsumed

Vehicle speedVehicle accelerationRoad grade

Vehicle main parameters

Energy>0 ?

Efficiency when E>0

Efficiency when E<0

Yes

No

Consumedenergy



Figure 13: Experimental results (in green) compared to modelling (in blue).


The model is totally unable to represent the deceleration phases when the vehicle is decelerating but

still consumes fuel. This is due to the modelling choice and is partially compensated by the empirical

efficiency ratio. However, when the route is mainly travelled with long decelerations, this results in an

underestimation of fuel consumption whereas when the route is travelled in a sportive way, this

results in an overestimation.

Furthermore, even considering this issue, the final estimation is rather good with a maximal error in

the cumulated fuel consumption of 10%.

4.4 Conclusion on the PCo1b model

This model is only dedicated to nomadic systems such as smart phones. It enables the possibility to

compute accumulated fuel consumption with a limited error. However, this model cannot be used to

compute the instantaneous fuel consumption.

5. PCo2 Powertrain Model Validation




This document describes scenarios and validation results of the vehicle model for the Passat CC test

vehicle available in Matlab/Simulink at IKA. The general modelling approach and the considered

components of the vehicle are described in D21.2.

5.2 Short description of the PCo2 powertrain model

The input of the vehicle model can be any driving cycle or any other given velocity profile over time.

An input vector containing time stamps [s] and target velocities [km/h] is analysed. Data between two

entries of this vector is interpolated. Apart from target velocity also external influences have to be

provided in a vector over time. These are:

Road inclination [%]

Wind speed [%]

Gear number [] (optional)

The vehicle model contains a gear shift model to simulate the automatic transmission. As the test

vehicle Passat CC offers the possibility to manually choose gears the model can also evaluate a gear

signal over time and consider this in the calculations. If no gear choice input is provided the automatic

transmission model is used to derive the current gear. Highly important vehicle parameters can be set

to fixed values at simulation start and are not required to be delivered over time:

Vehicle mass (including load) [kg]

Density of air [kg/m3]

Aerodynamic drag coefficient times vehicle’s front area [m²]

Rolling resistance coefficient [-]

The target velocity profile provided to the model has to take into account all infrastructure elements

such as curvature or intersections, which has to be generated in advance of the vehicle model

simulation. The driver is simulated by an ordinary PID-controller and follows the velocity of the input

driving cycle. As shown in Figure 14, the driver model compares the target velocity defined in the

input driving cycle with the current simulated vehicle speed and derives a torque value that is

forwarded to the vehicle controller. The controller defines the brake torque, engine torque, coasting

mode and gear for the next time step. The different components of the vehicle (engine, auxiliaries,

friction clutch, gearbox, and differential) and the whole vehicle body (with mass, velocity and the

driving resistances) are connected to each other and influenced by the controller.



Figure 14: Schematic diagram of the DSG vehicle drive train model.

The model is built as a “forward model” which takes into consideration the current state of the vehicle

to simulate the state in the next time step. The principle of this model is to minimize the speed

difference between the cycle or target velocity (input) and the velocity of the vehicle (output) when

comparing the current and predicted states of the vehicle. From the calculation of this speed

difference, the engine torque demand is calculated and is delivered within the limits of the maximum

engine torque. Knowing the gear and differential ratios leads to the calculation of the different

angular velocities and then to the real velocity of the vehicle for the next step. This allows a stable

model and determining the values of torque, speed and losses for every part of the car.

The main output of the model is the fuel consumption for a given velocity profile with defined

environmental conditions. Besides this, additional vehicle internal parameters such as the gear,

engine speed or clutch status can be exported. All output variables are stored as vectors versus time.


Test track number 1 can be used to examine the vehicle’s behaviour in certain situation such as the

approaching of an intersection (see Figure 15). Especially energy efficient deceleration strategies can

be analysed on this track. Test track number 2 contains a part of a motorway and a large dynamic

driving circle. This test track can be used to elaborate on the fuel consumption of constant driving

situations.



Figure 15: IKA test track number 1 including intersection scenario.

5.3.1 Test scenarios

To validate the quality of fuel consumption model test drives on the test track, a simple test has been

performed on the IKA test track number 1 under controlled conditions and without endangering other

road users.

In this scenario simple acceleration and braking tests have been performed. The vehicle accelerates,

holds its speed for a few seconds and brakes until it comes to a complete stop. The results (measured

and simulated speed, measured and simulated absolute fuel consumption over time and relative

deviation of simulated fuel consumption) are presented in the following chapter.

Additionally, specific situations such as the deceleration of the vehicle in front of traffic lights are

considered. As these specific situations are influenced by several disturbance variables such as other

traffic participants the tests will be performed on a route around the city of Aachen. This course is

often used as a reference at IKA projects. With a total length of 26 km it contains rural, highway and

urban traffic scenarios. The route is visualized in Figure 16.

The created simulation model follows a given velocity profile as an input in order to follow the given

trajectory using a PID controller and calculate fuel consumption. The speed profiles used originate

from real world measurements taken by average drivers in order to receive representative driving



cycle results. No special eco-driving characteristics have been respected. The model must outputs

reproducible results for each cycle with respect to the given parameters. Only one run per scenario is

sufficient to obtain the validation results.

Figure 16:Aachen runde (source: Google Earth).

5.4 Experimental results and analysis

The vehicle model has been validated in order to compare simulated fuel consumption to measured

values. Therefore the test vehicle Passat CC has been set up with measurement equipment:

1. Vector CANalyzer: Software and adapter to log signals from the vehicle’s CAN bus

2. Volume flow measurement equipment to log the fuel injected to the engine

3. Novatel RTK-GNSS receiver connected to the vehicle’s CAN bus

Both of the following measurement drives for the experimental validations were driven by the same

driver who was familiar with the vehicle. Within the simulations, the driver was modelled by a PID-

controller and followed the velocity of the input velocity profile.



5.4.1 Validation on IKA test track

The following section presents exemplary results from the test runs performed on the IKA test track.

As already described above, the vehicle accelerates up to approximately 50 km/h within the first 20

seconds. Subsequently, the driver is advised to hold the vehicle’s current speed for another 20

seconds followed by a breaking manoeuvre until the vehicle has come to a complete stop. Figure 17

shows the vehicle’s and simulation model’s velocity over time for this test run.

Figure 17: Velocity of measured and simulated test drive versus time.

As already described in section 5.2, the presented simulation model uses a given speed profile over

time as input values in order to reproduce the vehicle’s behaviour. Figure 17 shows that the vehicle

model is able to follow the measured speed profile with only short deviations in the range of a few

km/h.

Figure 18 shows the absolute fuel consumption over time corresponding to the speed profile that has

been presented in Figure 17. Please note that simulated absolute fuel consumption values cannot be

released due to confidential elements within the vehicle model. The measured value is derived from a

corresponding signal on the vehicle’s CAN bus. Again, it can be seen that the simulated fuel

consumption value corresponds the real measured value with only few deviations.

In order to analyse the exact deviation between measured and simulated fuel consumption, Figure 19

shows the relative deviation of both values. Except from the beginning of the test run where signal



delay and insufficient measurement values cause a deviation of over 35% within the first 5 seconds,

the overall deviation is lower than 5%. It can be seen, that the simulation model is quite accurate

within this boundaries. However, it has to be taken into account that this modelling approach does

not cover highly dynamic driving situations such as rapid acceleration and deceleration manoeuvres.

In this scenario, this can also be observed in Figure 19 after 40 seconds where the vehicle performs a

comparatively strong braking manoeuvre.

Figure 18: Absolute fuel consumption; measured and simulated.



Figure 19: Relative deviation of simulated fuel consumption compared to measured consumption.

5.4.2 Validation on “Aachen Runde”

In the test setup the vehicle is used in warm conditions and driven along the predefined route by a

test driver. Thereby the following signals are of relevance for the validation of the vehicle model and

can be obtained from the CAN bus using the measurement equipment mentioned above:

1. Velocity of the vehicle [km/h]

2. Acceleration [m/s²]

3. Current fuel consumption [ml]

4. Gear [-]

5. GPS height [m]

After the trip the measured data is post processed and the road incline gradient is derived from the

measured GPS elevation signal. As this signal is not highly accurate several filters can be applied in

order to get a more stable signal and to eliminate peaks. In spite of these filters the quality of the

gradient signal is not optimal. The velocity of the vehicle as well as the incline slope signal are

combined and stored in a Matlab file, which can be used as an input to the simulation model.

The model simulates the fuel consumption of the vehicle following the measured velocity profile.

Thereby especially the chosen gear has a huge impact and hence can be analysed in more detail. The

simulation results for the “Aachen Runde” are shown in Figure 20. The first graph visualizes the



velocity profile that has been driven and the velocity considered by the vehicle model. As there are

close to zero deviations the simulated vehicle in the model is capable of following the given profile.

The second graph visualizes the measured gear as well as the gear that is simulated by the model. In

some areas e.g. between 1600 and 1700 seconds the behaviour of the simulated gear shift controller

differs from the real vehicle behaviour. This is analysed in more detail below. The third graph in Figure

20 shows the relative deviation of the simulated fuel consumption as compared to the measured fuel

consumption from the CAN bus and the volume measurement.



Figure 20: Comparison of simulated versus measured signals.

The analysis of the described test drive reveals that the vehicle model results in a relative deviation of

5.726% of fuel consumption compared to the CAN bus Signal over the complete trip. Compared to the

volume measurement a deviation of 1.895% can be found. The behaviour over time is shown in the

third graph in Figure 20 and reveals huge deviations at start of the measurement. The deviation of fuel

consumption is calculated as the difference between the model output and the reference value

divided by the reference consumption. In the first minutes of the test drive the absolute values of

reference and simulated fuel consumption are very small, which results in huge relative deviations.

However, the accumulated fuel consumption given in the last plot of Figure 20 shows that these



values are calculated properly in comparison to corresponding measurements. Furthermore, the

accuracy of the vehicle model’s calculation results is limited by several unknown factors like wind

direction and speed as well as vehicle load and the exact amount of energy consumption demanded

by auxiliary units. In Figure 21, an excerpt from Figure 20, is shown in order to highlight a specific

situation. The vehicle model reproduces the behaviour of the gear shift control quite well and has only

small deviations during highly dynamic situations that occur at about 950 seconds in the plot below.

The change in the deviation of the measured compared to the simulated fuel consumption increases

especially in these highly dynamic situations.

Figure 21: Excerpt from the “Aachen Runde” to analyse the gear change modelling.



Highly dynamic situations also occur between 1550 and 1700 seconds in the test drive and are

highlighted in The same effect as described above can be identified here as the real vehicle

accelerates and decelerates quite strongly between 1610 and 1650 seconds. It can be seen that the

behaviour of the gear box controller deviates from real reference values during these situations. Due

to the fact that the vehicle model is designed to represent the average behaviour of the vehicle and its

components, highly dynamic driving situations are not covered in this approach. However, these

situations are distributed very rarely over the considered set of relevant driving cycles.

Figure 22: Excerpt from the “Aachen Runde” to analyse the gear change modelling in highly dynamic situations.



5.5 Conclusion on the PCo2 model

The presented simulation results show that the described vehicle model corresponds to the real

vehicle quite well. The model has been built to represent the average behaviour of the components of

the vehicle with regards to longitudinal dynamics, gear shifting patterns and energy consumption. In

highly dynamic situations the behaviour of the gear box controller varies, which is not covered by the

model. However, the model is capable to simulate fuel consumption with relatively low deviations

compared to the measurements.

6. PCo3 Power train Model Validation




The main goal of this deliverable is to validate the model explained in the WP21.2 and to compare it

with real test done in a special road defined close to CTAG facilities. To validate this model the state of

charge of the vehicle is logged in each moment via CAN bus and the PCo3 is equipped with an

accelerometer sensor with GPS to measure the accelerations and speed of the vehicle. This sensor,

called RT3100 from OXTS, has an inertial sensor block with three accelerometers and three gyros

(angular rate sensors). Additionally, kinematic GPS receiver updates the position and velocity

estimated by the inertial sensors.

RT3100 system processes the data in real-time. The real time results become output via an RS232

serial port or via CAN bus that can be read from a computer using a CANcase as interface.

The rest of the information the throttle pedal, steering angle, brake pressure, light status, gearshift

(PRND) status, etc. is available in PCo3’s CAN bus.

The general system architecture needed to validate the developed model is described in Figure 23.

Figure 23: General overview scheme PCo3 model validation.

CAN-bus

CANcaseXL

PC

CAN-bus

RT3100

Accelerometer



6.1.1 Data logger

CANcase is a data logger for recording traffic on the bus and CAN interface with USB 2.0 connector

(Table 5). The logger is configured with CANalyzer and the data recorded is transferred to a PC for

post-analysis via USB (Figure 24).

Table 5: CANcase presentation.

Features CANCASE XL log

Microcontroller ATMEL AT91 (ARM7 TDMI, 64MHz)

CAN controller 2 Philips SJA1000

CAN version CAN 2.0B

PC interface USB 2.0 (1.1 compatible)

Operating system Windows 2000, XP

Current consumption 180 mA

Ext. voltage supply 7V…30V

Ext. trigger input 5V (TTL, falling edge)

Reaction time <1ms

The analysis of the data is done using the same Vector tool (CANalyzer).

Figure 24: Data logged via CANalyzer.



6.1.2 Inertial navigation system

The RT3100 sensor (see Figure 25) is equipped with an inertial sensor containing three

accelerometers, three gyros and Kinematic GPS. Figure 26 to Figure 28 show the use of this sensor.

Figure 25: RT3100 inertial sensor.

This approach gives the RT several distinct advantages over systems that use GPS alone:

High update rate (100Hz).

The outputs are available with low latency 3.5ms.

Recognises jumps in the GPS position and ignores them.

Position and velocity measurements are smoothed to reduce the high-frequency noise.

Different measurements that GPS cannot deliver (e.g. acceleration, angular rate, heading,

pitch, roll, etc.)

Table 6 indicates the specifications of the RT3100 sensor.



Figure 26: RT3100 set up on Nissan Leaf.

Figure 27: Nissan Leaf test vehicle



Figure 28: Tests done with CAN and RT3100 on the laboratory.

Table 6: RT3100 specifications.

Specification RT3100

Positioning SPS / DGPS / SBAS

Position accuracy

1.8mCEP SPS

1.2m CEP SBAS

0.4mCEP DGPS

Velocity accuracy 0.1 km/h RMS

Acceleration

Bias

Linearity

Scale Factor

Range

10 mm/s² 1σ

0.01%

0.1% 1σ

100 m/s

Roll/Pitch 0.05° 1σ

Heading 0.1° 1σ

Angular Rate

Bias

Scale factor

Range

0.01°/s 1σ

0.1% 1σ

100°/s

Track (at 50km/h) 0.15° RMS

Slip Angle (at 50km/h) 0.2° RMS



Specification RT3100

Lateral Velocity 0.2%

Update Rate 100 Hz

Calculation Latency 3.9 ms

Power 9-18 V d.c. 15W

Dimensions (mm) 234 x 120 x 80

Weight 2.2 kg

Operating Temperature –10 to 50°C

Vibration 0.1 g²/Hz 5-500 Hz

Shock Survival 100G, 11ms

Internal Storage 500 MB


The PCo3 model describes the behaviour of the Nissan Leaf. All the features and parameters are

defined for this vehicle. The Nissan Leaf’s powertrain can work as an electric motor or as a generator

in case the vehicle slows down. For this reason, the traditional models based in the fuel consumption

of an internal combustion engine do not suit, because the motor can also generate the energy to

recharge the batteries and these features become very important in order to maximize the power

efficiency.

The PCo3 model contains three sub-models that define the behaviour of the powertrain, the

resistance, including the electrical consumers, and the vehicle model (Figure 29). The aim of the

powertrain model is to describe the performance of the batteries, the inverter and the electric motor

as well as the performances involved in the motion transmission. The resistance model defines the

aerodynamic resistance, the rolling resistance, slope resistance and the consumption of the electrical

consumer like the headlamps, the air conditioning, etc. Finally, the vehicle model is the global chain

that integrates the previous described models in a full vehicle model with three degrees of freedom.



Figure 29: General model description.

The main aim of the model is to calculate the energy consumption, which means the output current of

the batteries. The general architecture is defined according to the inputs of the driver and the road.

The output of the system is the energy consumed by the electric motor and other parameters that

define the vehicle state and provide feedback to the model.

The Leaf’s e-machine is an interior permanent magnet synchronous motor (IPM). Generating

maximum torque of 280 Nm and maximum power of 80 kW, the motor provides high levels of

performance and achieves a top speed of 10,360 rpm. The motor map of the Nissan Leafs describes

the output torque according to the motor speed and the throttle pedal position (Figure 30). This curve

defines the behaviour of the electric motor and therefore the current output of the batteries.

Figure 30: Curve of the Nissan Leaf’s electric motor.




6.3.1 Assumptions

Coefficient friction between wheels and road is constant.

No slip between wheels and road.

Air density constant.

No wind speed.

Wheel radius constant.

BMS and inverter always allow the electric motor to consume the power it needs (in traction

mode) or delivering the power it can (on regenerative braking), depending only on SoC and

not having into account other parameters such as batteries’ temperature.

Weather conditions are not taken into account.

The justification of these assumptions is similar to the one for PCo1a.

6.3.2 Test Scenarios

In order to test the PCo3 model, a real road scenario has been defined. This scenario consists on a

small rural road section and a main highway section close to CTAG main building (along A-55 road).

The total length of the test track is 14.3 kilometres (Figure 31).

This scenario was chosen trying to put together conditions with high energy saving potential (rural

roads) and conditions with low energy saving potential (highway), as well as road sections with

different speed limits, in order to evaluate model behaviour in a wide range of conditions.

6.3.3 Measured signals

During the validation tests, information from vehicle CAN bus and additional sensors installed on-

board was collected.

From CAN bus it has been extracted:

Accelerator position.

Brake pedal on/off sensor.

Brake circuit pressure.

Exterior/interior temperature.

Air conditioning state.

Lights condition.

Rain sensor.

SoC from the battery pack.

Steering wheel position.

Electric motor speed and torque.

From sensors installed on-board, it has been obtained:



Position

Longitudinal and lateral acceleration

Altitude

6.4 Experimental test track

CTAG test track consists on a small rural road section and a main highway section close to CTAG main

building (along A-55 road). One driver drove for 4 runs of this test track. In order to make the results

independent from the driver type the vehicle speed was set using cruise control.

Figure 31: CTAG test track.



In order to implement the real corridor characteristics into the simulation tools to allow to compare

results between real tests and simulations, the road sections have been split into segments and, for

each one of them, the following information is obtained:

Coordinates of the beginning and end nodes of the segment.

Length.

Slope (in percentage).

Altitude value above WGS84 ellipsoid.

Expected speed

Speed limit.

Curvature radius.

This information was achieved through a logging process using a GPS antenna to define the path of

the track, and then extracting the information related to the previous recorded path from digital

maps.

All this information has been loaded in the simulation program in order to define the closest possible

simulation scenario to the real one.


To validate the model developed and described in deliverable 21.2, the PCo3 vehicle was driven along

the route described in the section 6.3. The trip has a length of 14.3 km with different slope values and

is part of the A-55 highway.

During the test the vehicle was equipped with a RT3100 sensor to measure the vehicle speed and the

data from the GPS. The rest of the parameters are logged via CAN through a CANcase.

The test was done using the cruise control in order to obtain the most constant speed possible. The

scope of the test was to check the model at different speeds and with different modes (Drive and Eco

mode; Figure 32) and to validate the model developed in a road with different slopes (Figure 33). The

output current of the batteries (Figure 35) and the voltage of the batteries (Figure 36) were logged

with CANalyzer and the results are compared with the data of the model stored in the MicroAutoBox

from DSpace. The energy consumption of the batteries is computed as the product between these

variables (Figure 37). The SoC of the batteries is shown in percentage in Figure 38 and the motor

torque in Figure 34.



Figure 32: Time vs. Vehicle Speed.

Figure 33: Time vs. Slope.



Figure 34: Time vs. Motor Torque.

Figure 35: Time vs. Battery Current.



Figure 36: Time vs. Battery Voltage.

Figure 37: Time vs. Power consumption.



Figure 38: Time vs. State of Charge.


The input of the test is the vehicle speed instead of the throttle position (Figure 32). The speed during

the trip was set using cruise control (without pressing the throttle pedal) in order to achieve the most

constant vehicle speed possible. During the highest speed (100 km/h) used in the test, two different

driving modes were set (Drive and Eco mode) to define the influence of the driving mode on the

energy consumption.

The energy consumption of the batteries is calculated as the product between the output battery

current and voltage along an interval of time. These data are coming from the CAN bus.

Figure 38 represents the state of charge, which is the amount of energy left in a battery compared

with the energy it had when it was full and is expressed as a percentage. This parameter is calculated

through the voltage method. This method converts a reading of the battery voltage to SoC, using the

known discharge curve (voltage vs. SoC) of the battery.

Regarding the difference between the model and the real test comes out especially on down hills and

up hills. Despite of having the height profile of the A-55 highway, there is a difference between the

torque needed to overcome the up hills in the model and in the Nissan Leaf. This difference is

probably due to the hypothesis done to model the curve of the electric motor. On down hills the



motor works as a generator and the input current into the batteries is controlled by the inverter and

depends on many factors. Taking into account all these factor is complex, and some work in the

improvement of the powertrain model must be done to reduce this difference. Additionally the

output current of the batteries not only depends on the motor torque requirements, there are some

variables (mainly related with battery temperature and BMS behaviour) that are not directly known

and have not been took into account in the model.

In Figure 38 the state of charge of the batteries during the trip is plotted. The difference between the

model and the data logged in CAN bus increases with vehicle speed. This difference depends on the

vehicle speed and the parameters defined in the models that are varying with this factor, (like

aerodynamic and rolling resistance). Normally the aerodynamic resistance is set as the main

percentage of the total losses in a vehicle at high speed (more than 70 km/h). This parameter plays a

vital factor in the model but is not directly known too. There are variables that are not considered in

the model and it can increase the importance of the aerodynamic resistance as wind direction, air

pressure, climate conditions (during a raining day the aerodynamic resistance is higher) and height of

the highway (air density depends on the height), etc.

In the model it seems not to be difference between the Eco Mode and the Drive Mode in the

behaviour of the graphics. In the Eco Mode the motor torque should be higher in the down hills

because the regenerative braking should be higher but due to the Cruise Control, the speed control

manage the torque of the motor, so the vehicle attempt to follow the reference and not to maximize

the regeneration.


The PCo3 model has a good overall behaviour, the signals coming from the CAN bus and the output

parameters of the model have a similar behaviour with some difference at high speed and during

transitory moments as speed changes. These differences are due to the hypothesis done in the model.

The assumptions were done to have simple and robust model. We are currently working in some

improvements of the powertrain model to have a better understanding of the performance of the

Nissan Leaf.

With the data collected during the validation tests, it is possible to confirm that the model behaviour

represents the real behaviour of the vehicle in terms of energy. Different speed profiles followed by

the real vehicle lead to different energy consumption results in the same way as occurs using these

speed profiles as input for the vehicle model. If a ranking by energy consumption were established for

the different speed profiles, the final results would be the same whether evaluating the real vehicle or

using the vehicle model. Due to this reason, the vehicle model is valid for the purpose of choosing the

speed profile that represents the lower energy consumption from a list of possible speed profiles.

In terms of energetic consumption values, we are unable to compute an overall accuracy for the

model with the data we have collected. The validation tests were performed on an open road (with

the purpose of representing real driving situations) where many parameters are unknown or not



under control. Therefore, evaluating the differences between real vehicle and vehicle model in order

to give an accuracy percentage value is not possible at this moment. We are unable to distinguish

between the differences caused by the boundary conditions or by model assumptions/simplifications

with the test performed and the data collected. Anyway, more controlled tests can be performed in

further steps if the model performance value becomes necessary for the ecoDriver project.




The PCo4 is a parallel diesel ICE – electric hybrid Bus (HEV). This vehicle, with a Volvo 7700 powertrain

is described in the Annex A.4 of the D21.1 deliverable.


The objective is to validate the PCo4 model being developed and explained in the WP21.2. Unlike the

other ecoDriver vehicles, the hybrid electric Bus is not a UNIVLEEDS research vehicle. The installation

of sensors and collection of data in Leeds needs the support of the public transport operator FIRST

Bus and will be carried out as part of the SP3 activities.

The hybrid Bus powertrain in Leeds has been validated with an alternative validation dataset sourced

to inform the development, and later validation, of the PCo4 Energy Use and Emissions Model. The

Field Operational Tests (FOTs) were conducted with the single-decker version of the hybrid Bus Volvo

7700 powertrain instrumented with:

• Racelogic VBox Lite GPS (http://www.velocitybox.co.uk/);

• Semtech-DS Portable Emission Measurement System (PEMS);

• CAN analyser; and

• Battery status/ energy flows.

Nineteen sets of observations were made over three urban Public Transport routes, totalling over

10hours of driving and 220 kilometres. The tracked vehicle replicated a standard urban Bus journey,

stopping at Bus stops but not picking up passengers due to the presence of the emission

measurement instrumentation, batteries and calibration gases. The vehicle was driven by a single

professional driver who usually operated the vehicle on the Bus route. The Bus was driven normally in

both peak and off-peak periods, with the vehicle dynamics largely dictated by the density of urban

junctions (signal controlled and priority), necessity to service Bus stops and surrounding traffic. The

routes selected and “real-world” drive-cycle information are considered to represent typical urban

driving conditions and Bus operations in a European City. Approximately 50% of the observations

were analysed and used to develop and specify the PCo4 Energy Use and Emission powertrain model.

The remaining 50% of data was held back and used to validate model.


The inputs to the forward facing parallel HEV Bus model are any given drive cycle or velocity profile

with coordinated road inclination (gradient %). The energy available to the vehicle from the HEV

battery (State Of Charge - SOC) and Fuel level (diesel) are also required.

The powertrain model has sub-models for the:

Driver;

Supervisory Vehicle Controller;

Hybrid Powertrain system; and

Drivetrain System and Vehicle dynamics.

http://www.velocitybox.co.uk/



Supervisory Vehicle

Controller

Hybrid Powertrain

SystemDriver

Drivetrain System & Vehicle

Dynamics

Throttle Dem. Brake Dem.

ICEEM

A/T DifferentialMech. BrakesResist. Force

ICE ControlEM ControlBraking System ControlA/T Control

Global InputsGlobal Outputs

Fuel ConsumptionCO2 EmissionsBattery SOC

Vehicle SpeedRoad GradientInitial SOCInitial Fuel Level

Figure 39: Schematic of the PCo4 parallel HEV forward facing powertrain model

7.2.1 Driver model

The driver model reproduces the throttle and brake commands by comparing the desired vehicle

speed (global input) with the actual vehicle speed which is calculated internally in the model. The

error between the desired and the actual speed is multiplied by a first order transfer function and

then it is amplified by a PI controller to generate the percentage throttle and brake demands. It

should be noted that the main purpose of the transfer function is to allow the study of different driver

behaviours. The mathematical expression for the throttle and brake commands are given below:

where, the subscripts th and br refer to throttle and brake correspondingly, VD and VA are the desired

and actual vehicle speed, t is the time constant related with the driver behaviour, q is constant related

again with the driving style while kP and kI are the proportional and integral gains of the PI controller

respectively.

7.2.2 Hybrid Powertrain System

The hybrid powertrain system consists of the diesel engine and the electric motor/generator, these

components are modelled according to map-based approach to keep the overall formulation of the

model as simple as possible.

The model of the diesel engine includes two look-up tables one for the engine torque and the other

for the engine fuel flow (Figure 40). Both of the engine outputs are determined according to the

engine speed and load (throttle demand) points; it should be noted that the throttle demand is

generated from the supervisory vehicle controller depending on the demands of the driver and the

operating condition of the vehicle. The resultant torque of the powertrain system depends on the

𝑇ℎ𝑟𝑜𝑡𝑡𝑙𝑒 % 𝑠 = 𝑉𝐷 − 𝑉𝐴 ∗ 1

𝑞𝑡ℎ + 𝑡𝑡ℎ 𝑠 ∗ 𝑘𝑃,𝑡ℎ +

𝑘𝐼,𝑡ℎ

𝑠

𝐵𝑟𝑎𝑘𝑒 % 𝑠 = 𝑉𝐴 − 𝑉𝐷 ∗ 1

𝑞𝑏𝑟 + 𝑡𝑏𝑟 𝑠 ∗ 𝑘𝑃,𝑏𝑟 +

𝑘𝐼,𝑏𝑟

𝑠



operating mode of the vehicle and the clutch state which is determined by the supervisory vehicle

controller.

Torque Map

050

100

5001000150020002500

0

200

400

600

800

[%][RPM]

[Nm

]

Fuel Flow Map

050

100

50010001500200025000

20

40

[%][RPM]

[kg/h

]

Engine Speed [RPM]

Throttle Demand[%]

Torque[Nm]

Fuel Flow [kg/h]

Figure 40: Diesel engine map-based model, block diagram

Engine Speed[RPM]

Motor Load Demand [%]

Generator Load Demand [%]

+

+

050

100

01000200030000

200

400

600

[%][RPM]

[Nm

]

Motor Torque Map

050

100 0 1000 2000 3000

-600

-400

-200

0

[RPM][%]

[Nm

]

Generator Torque Map

Torque[Nm]

Figure 41:Electric motor/generator map-based model, block diagram

7.2.3 Drivetrain System & Vehicle Dynamics

The output torque of the powertrain system is the input to the drivetrain system sub-model which

consists of an A/T, a differential and the mechanical brakes. The final torque on the wheel after the

drivetrain system is evaluated according to the expression:



where, the subscripts W, PT, A/T, DI and Bmax refer to wheel, powertrain, automatic transmission,

differential and maximum brake capability respectively, τ is the torque, n is the efficiency fraction and

i is the gear ratio. The efficiencies and the differential gear ratio are assumed to be constant values

while the gear ratio of the automatic transmission is determined depending on the speed of the

engine based on logical threshold control methodology. The maximum brake torque (τBmax) is

multiplied with the Mechanical Brake Demand factor (generated from the supervisory vehicle

controller based on the commands of the driver) in order to determine the braking torque caused by

the mechanical brakes.

Knowing the final torque applied on the wheel, it is now possible to compute the acceleration and the

speed of the vehicle. A simple one wheel vehicle model derived from second Newton’s Law is used to

calculate the angular acceleration of the wheel (aW).

where, JE is the equivalent inertia of the vehicle while τR is the vehicle’s load torque caused by the

running resistance force. The vehicle’s equivalent inertia is calculated as follows:

Here, JW is the wheel moment of inertia, rW is the wheel radius and mT is the total mass of the vehicle

which may vary depending on the fuel level in the tank or vehicle’s additional weight i.e. auxiliary

equipment, number of passengers etc.

The load torque (τR) due to running resistances is caused by the forces acting on the vehicle which are

the drag force (Fd ), the climbing force (Fcl ) and the rolling force (Fr).

where, ρ is the air density, Cd and Cr are the drag and rolling resistance coefficients respectively, A is

the cross section area of the vehicle, g is the gravitational acceleration, φ is the road gradient and VA is

the vehicle speed which can be trivially calculated according to:

7.2.4 Supervisory Vehicle Controller

The main function of the supervisory vehicle controller is to distribute the driver’s throttle and brake

demands to each different plant i.e. electric motor/generator, diesel engine and mechanical brakes

based on the demands of the driver and on the operating condition of the vehicle. The logical

𝜏𝑊 = 𝜏𝑃𝑇 ∗ 𝑖𝐴/𝑇 ∗ 𝑛𝐴/𝑇 ∗ 𝑖𝐷𝐼 ∗ 𝑛𝐷𝐼 − 𝜏𝐵𝑚𝑎𝑥∗ 𝑀𝑒𝑐ℎ. 𝐵𝑟𝑎𝑘𝑒 𝐷𝑒𝑚𝑎𝑛𝑑

𝛼𝑊 =𝜏𝑊 − 𝜏𝑅

𝐽𝐸

𝐽𝐸 = 𝐽𝑊 + 𝑚𝑇 ∗ 𝑟𝑊 2

𝜏𝑅 = 𝐹𝑑 + 𝐹𝑐𝑙 + 𝐹𝑟 ∗ 𝑟𝑊 = 1

2𝜌𝐶𝑑𝐴 𝑉𝐴

2 + 𝑚𝑇𝑔 sin 𝜑 + 𝐶𝑟𝑚𝑇𝑔 cos 𝜑 ∗ 𝑟𝑊

𝑉𝐴 = 𝑟𝑊 ∗ 𝛼𝑊 𝑑𝑡 ∗ 3.6



threshold approach employed is based on Chu et al. (1999) to develop the model of the controller.

The throttle and brake demands are converted into power demands by knowing the maximum power

capability of the corresponding plant. The power demands are then compared with the actual power

of each plant as it is calculated dynamically from the model; the comparison between the actual and

the demanded powers yields the final throttle and brake demands which are the inputs of the

powertrain and drivetrain system.

The controller developed considers five modes to propel the vehicle, two modes are during braking

operation and two modes during emergency operation (Table 7). Finally, the inputs and outputs of the

supervisory vehicle controller are summarised in Figure 42.

Table 7: Parallel HEV model operating modes.

Propelling Operation Braking Operation Emergency Operation

Mode 1: Start/Stop Mode 1: Regenerative Mode 1: Fuel Tank Empty

Mode 2: Electric Motor Only Mode 2: Hybrid (Regen. + Mech.) Mode 2: Low SOC

Mode 3: Diesel Engine Only

Mode 4: Diesel + Motor (Boost)

Mode 5: Diesel + Generator

(Charge)

Figure 42:Supervisory vehicle controller model inputs-outputs

7.2.5 Fuel Consumption Estimation

The litres of diesel fuel consumed for each travelled kilometre are calculated by using the fuel flow

map of the diesel engine model and the vehicle distance travelled. The mathematical expression

which describes the fuel consumption (fc ) in litres per kilometre of the HEV:

Supervisory Vehicle

Controller

Inputs

Driver Throttle DemandDriver Brake Demand Vehicle SpeedEngine SpeedBattery SoCFuel Level

Outputs

Engine Throttle DemandMotor Load Demand Generator Load DemandMech. Brakes DemandClutch State

𝑓𝑐 =1

3600∗

𝑚 𝑓 𝑑𝑡

𝜌𝑓 ∗ 𝑋𝐴



where, 𝑚 is the fuel flow is as it is evaluated from the diesel engine model, 𝜌 is the diesel fuel

density, 𝑋 is the vehicle distance travelled which is calculated from the model and

is a unit

conversion factor.

7.2.6 CO2 Emissions Estimation

Assuming that the diesel engine operates mostly with lean air to fuel ratios, it can be said that the

amount of carbon dioxide in the combustion products is directly proportional to the amount of

fuel burned from the engine. This statement can be proved by solving the fuel’s combustion reaction

for lean mixtures:

where the subscripts 𝑥 and define the amount of carbon and hydrogen in the fuel respectively

while , and

are the mole numbers of each individual combustion product.

As a consequence the grams of 𝐶 emitted from the engine per kilometre travelled can be related to

the fuel consumption (Eq. 8) with a simple emissions factor

and, 𝐸𝐹

is the 𝐶 emission factor which is defined as:

where,

is the number of 𝐶 moles in the combustion products, is the number of fuel

moles in the reactants (unity), 𝑀 is the molar mass of 𝐶 , 𝑀

is the molar mass of fuel, 𝜌 is

the fuel density and is a unit conversion factor.

7.2.7 Battery SOC Estimation

A simple, fast and reliable SOC estimation method with the minimum number of physical and

experimental parameters was developed. The battery SOC is estimated based on a simple energy-

based model.

The percentage fraction between the consumed/produced energy of the motor/generator and the

total battery energy (battery capacity), is subtracted from the initial battery SOC to estimate the

actual battery 𝐶

𝐶𝑥𝐻 + 𝑥 +

4 ∗ 2 + 3.76𝑁2 → 𝐶 2

𝐶 2 + 𝐻2 𝐻2 + 𝑁2𝑁2

𝑒𝐶 2= 𝑓𝑐 ∗ 𝐸𝐹𝐶 2

𝐸𝐹𝐶 2=

𝐶 2∗ 𝑀𝐶 2

𝐶𝑥𝐻 ∗ 𝑀𝐶𝑥𝐻

∗ 𝜌𝑓 ∗ 103

𝐶 = 𝐶𝑖 − 𝑃𝑀 𝐺 ∗ 𝑛𝑀 𝐺 𝑑𝑡

𝐸𝐵 ∗

10−4

3.6



Here, 𝐶 is the initial SOC, 𝑃 and 𝑛 are the power and efficiency of the motor/generator

respectively, 𝐸 it the total battery capacity and

is a unit conversion factor.


An ITS Research partner has provided data from a series of Field Operational Tests (FOTs) of a Volvo

7700 Bus (hybrid powertrain). The Volvo 7700 vehicle was instrumented with:

• Racelogic VBox Lite GPS (http://www.velocitybox.co.uk/);

• Semtech-DS Portable Emission Measurement System (PEMS);

• CAN analyser; and

• Battery status/ energy flows.

All observations were coordinated at a 1Hz time resolution. Parameters accessed via the CAN analyser

included the engine and powertrain operation, and the driver-vehicle interface. The Sensors Inc.

(http://www.sensors-inc.com/) Semtech-DS measures instantaneous exhaust flow rate and tail-pipe

concentrations of CO2, NO, NO2, NOX, HC and Soot (opacity) to estimate second by second emission

rates (grams.sec-1).

The parameters recorded are summarised in Table 8. Local altitude reference points were available,

and have been used in combination with a digital terrain map and the GPS position and altitude data,

to develop an accurate elevation profile and derived road gradient (%).

Table 8: Recorded parameters.

GPS CAN Analyzer PEMS

Position Gear Exhaust flow rate

GPS speed Brake position Gaseous Emission rates:

Altitude (estimate) Accelerator position CO2, CO, NO, NO2, NOX, HC

Clutch activation/ slip Particle (Soot) Emission rate

Engine torque Ambient conditions:

Road speed Temperature, RH, Pressure

Engine speed

Temperatures (various)

Engine mode (ICE or EM)




The experimental results from three of the available drive cycles have been analysed in detail for this

report.

Firstly, for the model validation it is important to make sure that the simulation model is capable of

reproducing the actual vehicle speed profile. Figure 43 illustrates the experimental and simulated

vehicle speed results for the three different drive cycles. Figure 44 presents only a short period of the

Drive Cycle which demonstrates that the model replicates precisely the actual vehicle speed profile.

The normalised gross error (NMGE) between the 1Hz experimental and modelled speed profiles for

the three sample drive cycles were 0.06675, 0.04193 and 0.05816. These low values demonstrate the

model consistently reproduces the experimental (actual) vehicle speed profile.



Figure 43: Vehicle speed profile and road gradient for three sample drive cycles

0 500 10000

10

20

30

40

50

60

Time [sec]

Speed [

km

/h]

Drive Cycle 1, 5.5 (km)

0 500 1000

-4

-2

0

2

4

6

Time [sec]

Gra

die

nt

[Deg]


0 500 1000 1500 2000 25000

10

20

30

40

50

60

Time [sec]

Speed [

km

/h]


0 500 1000 1500 2000 2500

-3

-2

-1

0

1

2

3

Time [sec]

Gra

die

nt

[Deg]


0 500 1000 1500-6

-4

-2

0

2

4

6

Time [sec]

Gra

die

nt

[Deg]


0 500 1000 15000

10

20

30

40

50

60

Time [sec]

Speed [

km

/h]


Simulation Experiment



Figure 44: Comparison between simulation and experimental vehicle speed


The aggregate simulation and experimental results of fuel consumption and CO2 emissions for the

three sample drive cycles are presented in Table 9. The model predicts the aggregate fuel

consumption and CO2 emissions within ±10%.

Table 9: Simulation and experimental results of the average fuel consumption and CO2 emissions for three sample drive cycles.

Drive Cycle

Fuel Consumption

Emissions

Estimation Accuracy

Simulation Experiment Simulation Experiment Fuel

Consumption

Emissions

To evaluate driver behaviour influences and the potential impact of ecoDriver aids, the model must

also predict “instantaneous” values of these fuel consumption and CO2 well. The simulation and

experimental results for a small period of the Drive Cycle 3 are illustrated in Figure 45 (a) along with a

visualisation of conditional quantiles of the instantaneous CO2 model performance for drive cycle 3

(b). These results show the limitations of the present simplified estimation approach since as it is

observed the model does not reproduce perfectly the experimental measurements. The main reason

1280 1300 1320 1340 1360 1380 1400 1420 14400

5

10

15

20

25

30

35

40

Time [sec]

Speed [

km

/h]


Experiment

Simulation



behind these inaccuracies is suggested to be due to the control strategy of the supervisory vehicle

controller. The model strategy was developed with a simple logical threshold method while the real

controller strategy is unknown. In Figure 46 the simulation and experimental results of the battery

SOC estimation for each drive cycle. The graph show a good agreement between simulation and

experimental results, however there some parts where the prediction is not that accurate; again the

cause of these errors is the strategy of the supervisory vehicle controller. Nevertheless observing

Figure 47, a closer look in Drive Cycle 3 clearly shows that the model is capable of predicting the

general trend of the battery SOC during the various operating conditions of the vehicle i.e.

acceleration and deceleration.

Figure 45: Simulation and experimental results of (a) instantaneous CO2 emissions and (b) model performance

1300 1320 1340 1360 1380 1400 1420 1440 14600

2

4

6

8

10

12

14

16Drive Cycle 3, 8.5 (km)

Time [sec]

Insta

nta

neous C

O2 [

g/s

]

Simulation

Experiment

predicted value

10 20 30 40

10

20

30

40

0

200

400

600

800

sa

mp

le s

ize

fo

r h

isto

gra

ms

25/75th percentile10/90th percentile

medianperfect model

ob

se

rve

d v

alu

e



Figure 46: Simulation and experimental results of battery SOC

0 200 400 600 800 1000 120030

35

40


Time [sec]

SO

C [

%]

0 500 1000 1500 2000 250035

40

45

Time [sec]

SO

C [

%]


0 500 1000 150030

40

50


Time [sec]

SO

C [

%]

Simulation

Experiment



Figure 47: Battery SOC during deceleration and acceleration modes


The introduction of model-based solutions in the area of HEVs can reduce the cost and improve the

efficiency of these vehicles. The PCo4 model is simple, fast and reliable model-based estimation

method of fuel consumption, CO2 emissions and battery SOC. It was developed on the basis of a

classical forward facing vehicle model. The model was developed with the minimum number of

physical and experimental parameters. Although the main processes were modelled at a basic level,

compared to the complexity of the real system, the comparison of the simulation and experimental

results proves that the right trends were observed. Estimation inaccuracies are mainly located on the

supervisory vehicle controller since the actual control strategy of the vehicle was unknown, thus it is

strongly believed by knowing the exact controller characteristics may lead to the eradication of these

inaccuracies. The model can give a useful insight on how the processes and model parameters affect

the performance and particularly the fuel consumption, CO2 emissions and battery SOC of the vehicle

while it also has the potential to be used as a platform for hybrid vehicle control system design. As the

model is able to replicate the interaction of the driver, powertrain and vehicle dynamics second-by-

second, it can be used to evaluate the performance of different driver behaviours in the ecoDriver

demonstrations and trials in real time.

250 300 350 400 450 500 550 600 650 7000

20

40


Time [sec]

Speed [

km

/h]

250 300 350 400 450 500 550 600 650 70030

32

34

36


Time [sec]

SO

C [

%]

Simulation

Experiment

8. Models comparison in SP2



Table 10 shows a global comparison between the five models presented, simulated and validated in

SP2 and destined to be used by the ecoDriver system.



Table 10: Global comparison of the five powertrain models of SP2.

PCo1a PCo1b PCo2 PCo3 PCo4

Vehicle/Energy type

Renault Clio III Eco 2

passenger car with a

manual transmission /

conventional powertrain

Renault Clio III Eco 2

passenger car with a

manual transmission /


PASSAT CC

passenger car with a DSG

transmission /


NISSAN Leaf / Battery

electric vehicle

Volvo 7700 Bus for public

transportation / diesel-

electric hybrid

Scenarios

SC1-

Acceleration/deceleration

on plane road

SC2- Constant speed with

gear changes

SC3- Realistic trip

SC1-

Acceleration/deceleration

on plane road

SC2- Constant speed with

gear changes

SC3- Realistic trip

SC1- While approaching

an intersection

SC2- Constant driving

situations

SC3- Realistic trip

SC1- High energy saving

potential (rural roads)

and low energy saving

potential (highways)

SC1- Volvo 7700 Bus

for public transportation

/ diesel-electric hybrid

Drivers

21 drivers :

1)Normal driving

2)With an oral

presentation of eco-

driving rules before

testing

21 drivers :

1)Normal driving

With an oral presentation

of eco-driving rules

before testing

Target speed profiles

issued from real world

measurements taken by

average drivers.

No specific eco-driving

characteristics are

respected.

1 driver with the vehicle

speed set using cruise

control (constant speed

scenario) :

1)Drive mode

2)Eco-mode

3 drive cycles.

Compared Model/real

variables

Speed, Fuel consumption,

Gear

Speed, Fuel consumption. Speed, Fuel consumption,

Gear

Speed, SOC. Speed, SOC, CO2

emissions.

Estimation error on

instantaneous energy

consumption

< 2ml/s Only few deviations on

speed or consumption

10%



PCo1a PCo1b PCo2 PCo3 PCo4

Steady state reaching <10% <10% <5% Output current of

batteries

<10%

Cumulated energy

consumption

Good tracking Good tracking Good tracking Good tracking Good tracking

Realtime application OK OK OK OK OK

Conclusion Widely usable for

ecoDriver system. The

powertrain model and

the energy consumption

model are precise enough

for the real applications

in ecoDriver. Represents

the average behaviour of

the vehicle longitudinal

dynamics and its energy

consumption. Errors are

acceptable in the real

application.

Not usable to represent

deceleration phases

when vehicle is

decelerating but still

consumes. It is

compensated by

empirical efficiency ratio.

Rather good final

estimation for ecoDriver

system. For nomadic

system, it can compute

cumulated fuel

consumption but not

instantaneous one.

The target speed profile

provided to the model

must take into account all

infrastructure elements

(curvature, intersections)

generated in advance of

the model simulation.

Driver is represented by a

PID-controller. It does not

cover highly dynamic

driving situations (fast

acceleration and

deceleration

manoeuvers).

Some difference at high

speed and during

transitory moments as

speed changes. The

vehicle model is valid for

the purpose of choosing

the speed profile that

represents the lower

energy consumption from

a list of possible speed

profiles. Unable to

compute an overall

accuracy for the model

with the data we have

collected.

It is observed the model

does not reproduce

perfectly the

experimental

measurements. The main

reason behind these

inaccuracies is suggested

to be due to the control

strategy of the

supervisory vehicle

controller (actual control

strategy of the vehicle

was unknown). The

model is capable of

predicting the general

trend of the battery SOC

during the various

operating conditions of

the vehicle.

9. Implications for the ecoDriver project


9. Implications for the ecoDriver project

WP21 is an essential first step in the ecoDriver project because of its interaction with the other SPs.

Indeed, the powertrain models will be used by SP2 in WP22 and WP23 to develop the Vehicle Energy

and Environment Estimator (VE3); SP1 will also use the simplified models to focus on the HMI

strategies and development; SP3 will interact with SP2 for the different measurements needed in the

project. Finally, SP4 will make several scenarios to test the ecoDriver system based on the different

powertrain models.

By the end of month 9 of the project, all the WP21 models will be available for the other SPs as

MATLAB/SIMULINK models.

References


References

L. Chu, Q. Wang, Y. Li, Z. Ma, Z. Zhao, and D. Liu. 1999. Study of the electronic control strategy for the

power train of hybrid electric vehicle," in Vehicle Electronics Conference, 1999. (IVEC '99) Proceedings

of the IEEE International, 1999, pp. 383-386 vol.1.

Disclaimer This document reflects only the author’s views. The European Union is not liable for any use that may be made of the information herein contained.

For more information about

ecoDriver project

Prof. Oliver Carsten

University of Leeds (coordinator)

Woodhouse Lane

LS2 9JT Leeds

United Kingdom

[email protected]

www.ecodriver-project.eu

How to cite this document

Lydie NOUVELIERE, Qi CHENG, Luis CONDE, Oscar DIAZ, Olivier ORFILA, Samuel

RIOS, Guillaume SAINT-PIERRE, Cyndie ANDRIEU, Philipp SEEWALD, James TATE,

Philipp THEMANN, Adrian ZLOCKI (2012). D21.3: Powertrain Model Validation.

ecoDriver Project. Retrieved from www.ecodriver-project.eu.

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D21.3: Powertrain Model Validation