Desktop Driving Simulator with Modular Vehicle Model and

Desktop Driving Simulator with Modular Vehicle Model and
Desktop Driving Simulator with Modular
Vehicle Model and Scenario Specification
Master’s Thesis, Master’s Programme Automotive Engineering
ARPIT KARSOLIA
Department of Applied Mechanics
Division of Vehicle Engineering & Autonomous Systems
Vehicle Dynamics group
CHALMERS UNIVERSITY OF TECHNOLOGY
Göteborg, Sweden 2014
Master’s thesis 2014:06
MASTER’S THESIS
Desktop Driving Simulator with Modular
Vehicle Model and Scenario Specification
ARPIT KARSOLIA
Department of Applied Mechanics
Division of Vehicle Engineering & Autonomous Systems
Vehicle Dynamics group
CHALMERS UNIVERSITY OF TECHNOLOGY
Göteborg, Sweden
Desktop Driving Simulator with Modular Vehicle Model and Scenario Specification
ARPIT KARSOLIA
© ARPIT KARSOLIA, 2014
Master’s Thesis 2014:06
ISSN 1652-8557
Department of Applied Mechanics
Division of Vehicle Engineering & Autonomous Systems
Vehicle Dynamics group
Chalmers University of Technology
SE-412 96 Göteborg
Sweden
Telephone: + 46 (0)31-772 1000
Cover:
3-Screen Desktop Simulator as used in simulator trials with 4 camera eye tracking
system.
Chalmers reproservice/Department of Applied Mechanics
Göteborg, Sweden 2014
Desktop Driving Simulator with Modular Vehicle Model and Scenario Specification
Master’s Thesis
ARPIT KARSOLIA
Department of Applied Mechanics
Division of Vehicle Engineering & Autonomous Systems
Vehicle Dynamics group
Vehicle Dynamics
Chalmers University of Technology
ABSTRACT
Driving Simulators are one of the key tools to simulate and verify, interactions between
vehicle & driver in a realistic as well as conditioned traffic environment. Real vehicle
testing and pure simulation (using a driver model) are two alternative tools of collecting
such information. Relevant data from real vehicle testing requires a high degree of
repetition, man-power and is time-consuming, not to mention expensive. Advanced
driver simulators are present in Sweden which provide, very realistic driver experience
and perception. They simulate, very accurately, a real-time scenario in a holistic
environment. But, in the modern world, vehicle & traffic situations have become so
complex that the application and usefulness of driver simulators has moved beyond its
usual definition. Thus, every experiment goes through an intricate, time consuming and
thus expensive process of experimental design & development. The solution proposed
in this thesis is to use a driving simulator which is portable and can simulate an
experiment/scenario at an office level before moving to advanced simulators or real
testing. The Desktop driving simulator would provide a platform to test a potential idea
at a lesser scale and establish its functionality. This thesis will also study the modularity
of the vehicle model for parameterization and simulate common vehicle manoeuvres to
investigate model accuracy. However, results obtained will not be at par with analysis
on advanced simulators, especially regarding driver perception and response but may
provide an indication towards its behaviour and relevance.
Key words: driving simulator, scenario, testing, driver perception, vehicle model.
I
II
Contents
ACRONYMS
II
1
3
2
3
4
INTRODUCTION
1.1
Thesis Description
Goals & Deliverables
Research Questions
Intended User
Test Vehicles Description – Ambulances
Scenario III
Thesis Limitations
1.2
Theory of Vehicle Dynamics
Position of Centre of Mass[4]
Height of Centre of Mass[4]
Vehicle at Braking & Driving[3]
Tire Slip and Vehicle Behaviour
The Magic Formula Tire Model[3]
SIMULATOR DESIGN
4
4
5
5
6
6
7
8
8
8
9
9
10
11
2.1
Components – HW & SW
11
2.2
Visual Display Flowchart
11
2.3
Vehicle Model
Model Communication
Model Structure
Model Blocks
Offline & Online model structure
Electronic Stability Control system
Parameterised Models – Ambulances
12
12
13
14
24
26
28
SIMULATOR SCENARIOS
30
3.1
Obstacle Avoidance
31
3.2
Simulation Tests
Straight Line Braking ISO 21994
Sine wave with Dwell (SWD) TP-126-03
Double Lane Change – ISO 3888-1
32
32
33
34
SIMULATION RESULTS
36
4.1
Obstacle Avoidance
36
4.2
Straight Line Braking
Volvo S40 2L (2007)
Mercedes Sprinter (2013)
36
38
41
4.3
Sine with Dwell Test - Offline
44
III
Mercedes VitoXL (2013)
4.4
5
DLC manoeuvre – Online
CONCLUSIONS & FUTURE WORK
45
49
52
5.1
Conclusions
52
5.2
Future Work
53
6
REFERENCES
54
APPENDIX A – VEHICLE MODEL I/O
56
APPENDIX B – SWD PLOTS VOLVO S40
59
APPENDIX C – DLC PLOTS MERCEDES VITOXL
62
APPENDIX D - VOCABULARY (ISO - 8855)[23]
64
APPENDIX E – VEHICLE PARAMETERS
65
APPENDIX F – LOGGED VARIABLES
70
IV
Preface
In this thesis, offline and online testing of the vehicle model with 2 different sets of
ambulance vehicle data were carried out using Matlab/Simulink & software provided
by VTI. The tests were carried out from May 2014 to August 2014. This thesis is part
of a research project called ASTAZero SIM based on the ASTAZero Track, in Borås
(project funded by Vinnova, reference Diarienummer 2013-04715). The thesis was
carried out at the Department of Applied Mechanics, Division of Vehicle Engineering
& Autonomous systems, Chalmers University of Technology, Sweden. The ASTAZero
SIM project is a joint enterprise of Chalmers University with VTI and SP.
A part of this thesis has been carried out with Matteo Santoro as a fellow thesis
teammate and Prof. Bengt Jacobson & Prof. Jonas Sjöberg as supervisors. All
simulations were carried out with the simulator equipment at Chalmers University of
Technology. I would like to thank Eleni Kalpaxidou, Jonas Andersson Hultgren, Sogol
Kharrazi and Ingvar Näsman from VTI, Martin Skoglund, Niklas Adolfsson & Erik
Torstensson from SP, and Anders Karlsson from Chalmers for their co-operation and
support in dealing with the simulation software and hardware. A special mention to
Artem Kusachov from VTI/Chalmers for taking the time to review important sections
of this report and conveying suggestions to improve its overall credibility.
Finally, I would like to thank Matteo Santoro & my supervisors for helping me through
this thesis and in achieving satisfactory results.
Göteborg November 2014
Arpit Karsolia
V
Notations
a
Longitudinal Position w.r.t Centre of Gravity, C.G.
A0
Frontal Area
ay
Lateral Acceleration
b
Lateral Position w.r.t C.G.
Cax
Coefficient of Air Drag
Cdamp
Damping Coefficient at wheel position, per side (Front, Rear)
fr
Rolling Resistance coefficient
Fx tire
Longitudinal Tire Force
Fy tire
Lateral Tire Force (LF, RF, LR, RR)
Fz
Vertical Force
GRtot
Total Gear ratio
hcg
Height of C.G
hr
Roll Centre Height
hsr
Height of C.G above Roll Axle
hus
Height of unsprung mass centre
Idrv
Driveshaft moment of Inertia
Ieng
Engine moment of Inertia
Ir
Moment of Inertia around Roll axle
Iw
Wheel rotational moment of Inertia
Iy
Pitch Moment of Inertia around C.G
Iz
Moment of Inertia about Z-axis
Karb
Anti-Bar Roll Stiffness (Front, Rear)
Kspr
Spring Coefficient at wheel position, per side (Front, Rear)
lf
Front Axle longitudinal distance from C.G.
lr
Rear Axle longitudinal distance from C.G.
m
Vehicle Mass
ms
Total Sprung Mass
mus
Total Unsprung Mass(Axle)
mus
Unsprung Mass per side (Front, Rear)
Mz
Tire Aligning Torque
Rw
Wheel Radius
VI
SG_ratio
Steering Gear Ratio
Tq_drv
Driveline Torque
Tqbrk
Brake Torque
tw_f
Track width – Front
tw_r
Track width – Rear
Vx slip
Minimum velocity for Longitudinal Slip Calculation
Vx
Longitudinal Vehicle Velocity
Vy
Lateral vehicle velocity
X
Vehicle position in global coordinates (X-axis)
Y
Vehicle position in global coordinates (Y-axis)
Zcg
Vertical Position of C.G.
Zw
Vertical Position of road wheel
γ
Wheel Camber angle
δ
Steering Angle
θ
Pitch Angle
κ
Longitudinal Slip
ρ
Density of Air
Φ
Roll Angle
Ψ
Yaw angle
Ψ0_static
Static Toe Angle
ωwhl
Wheel velocity
VII
Acronyms
ABS
Anti-Lock Braking System
ESC
Electronic Stability Control
DOF
Degree of Freedom
ASTAZero
Active Safety Test Area Zero
HW
Hardware
SW
Software
UDP
User Datagram Protocol
LAN
Local Area Connection
LF
Left Front
RF
Right Front
LR
Left Rear
RR
Right Rear
FWD
Front Wheel Drive
RWD
Rear Wheel Drive
AWD
All Wheel Drive
MF
Magic Formula
CG
Centre of Gravity
EBD
Electronic Brake Force Distribution
DSTC
Dynamic Stability & Traction Control
EBA
Electronic Brake Assist
BAS
Brake Assist System
ASR
Acceleration Skid Control
SWD
Sine with Dwell
DLC
Double Lane Change
CCW
Counter Clockwise
VIII
List of Figures
Figure 1.1
Driving Simulator Hierarchy
Figure 1.2
ASTAZero Track Environment
Figure 1.3
Scenario III
Figure 1.4
Experimental Determination of longitudinal position of centre of mass
Figure 1.5
Automobile subjected to longitudinal forces & subsequent load transfer
Figure 1.6
Curve produced by the original sine version of the Magic Formula
Figure 2.1
Chalmers Simulator structure
Figure 2.2
Flowchart of Visual Display
Figure 2.3
Communication – External Vehicle Model
Figure 2.4
Model Overview
Figure 2.5
Steering Torque structure
Figure 2.6
Brake block
Figure 2.7
Magic Formula 5.2 subsystem
Figure 2.8
Function block – Magic Formula
Figure 2.9
Chassis block
Figure 2.10
Road subsystem
Figure 2.11
ESC Overview
Figure 2.12
ESC Structure
Figure 3.1
Simulation Flowchart
Figure 3.2
Obstacle Avoidance
Figure 3.3
Straight Line Braking
Figure 3.4
Offline Driver Input - Straight Line Braking (S40)
Figure 3.5
Offline Driver Input Test 2 – Sine wave with Dwell (S40)
Figure 3.6
Placing of cones for DLC track
IX
Figure 4.1
Longitudinal Force Coefficient as a function of longitudinal slip
Figure 4.2
Vehicle Speed (km/h) vs Time(s) (S40)
Figure 4.3
X-position (m) vs Time(s) (S40)
Figure 4.4
Vehicle Velocity (m/s) & Wheel Velocity (m/s) vs Time(s) – LF & RR
(S40)
Vehicle Velocity (m/s) & Wheel Velocity (m/s) vs Time(s) – LF & RR
Figure 4.5
(ABS) (Sprinter)
Figure 4.6
Tire Longitudinal Slip vs Time(s) (ABS) (Sprinter)
Figure 4.7
Longitudinal Tire Force (N) vs Longitudinal Slip – LF & RR (ABS)
(Sprinter)
Figure 4.8
Steering Input & Path Plots – Volvo S40 with ESC
Figure 4.9
Vehicle Behaviour for different paths
Figure 4.10
Path Plot – Mercedes VitoXL with/without ESC
Figure 4.11
Brake Torque (Nm) vs Time (s) – Mercedes VitoXL with ESC
Figure 4.12
Lateral slip angle (rad) vs Time (s) – LF, RF, LR, RR Tires (VitoXL)
Figure 4.13
Vehicle body slip angle (deg) vs Time (s) – with/without ESC (VitoXL)
Figure 4.14
DLC path and key positions
Figure 4.15
DLC path with/without ESC (VitoXL)
Figure 4.16
Steering Wheel Angle & X-position vs Time (VitoXL)
X
List of Tables
Table 2.1
Parameters for wheels
Table 2.2
Output signals from driver to model
Table 2.3
Signals from road to model
Table 2.4
Scaled variables
Table 3.1
Vehicle & Simulation Type for given manoeuvre
Table 3.2
SWD settings
Table 3.3
Dimensions of DLC track
Table 4.1
Vehicle Specs
Table 4.2
ABS Simulation Settings (S40)
Table 4.3
Maximum Brake Torque per axle (S40)
Table 4.4
Simulation Stopping Distance & Duration (S40)
Table 4.5
ABS Simulation Settings (Sprinter)
Table 4.6
Maximum Brake Torque per axle (Sprinter)
Table 4.7
Simulation Stopping Distance & Duration – Sprinter
Table 4.8
ESC settings for Mercedes VitoXL
Table 4.9
Steer and Path Points for SWD steer – Mercedes VitoXL with ESC
XI
Coordinate System
Figure – Vehicle Body Coordinate System[4]
OVE = Own Vehicle (simulator vehicle)
OVE origo = centre point of front wheel axis
For the online simulations, three coordinate systems were used –
1. Body Fixed System, right handed Cartesian DIN – system
 X is Forward
 Y is Left
 Z is Upward
 CCW is positive angle
2. Track System, (non-linear road following) right handed system
 s is position of OVE origo along chord line from the beginning of the
road calculated in XY – plane (no elevation taken into account)
 r is lateral position OVE origin with respect to road centre coordinate
line
 h is height above road surface
 yaw is CCW positive angle with respect to centre coordinate line tangent
3.
XII
Inertial System, world global right-handed Cartesian system
 X is east
 Y is north
 Z is up
 Heading is CCW positive angle, with respect to east direction.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
1
2
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
1
Introduction
Driving Simulators provide an essential link between an engineer’s idea for
development and actual testing of the idea itself. The definition of a driving simulator
can range from a basic computer model simulating a particular dynamic element of
vehicle behaviour to a multiple DOF, high fidelity structure, accurately simulating real
time behaviour. On the basis of simulation mode, a driving simulator can also be
defined as an offline or an online mode.
An offline mode would generally be pure simulation (non-real time) & represents
simulations carried out on a PC with a vehicle model designed on platforms such as
Matlab, Simulink, Dymola, etc., with different vehicle parameters fed as inputs. The
vehicle model would range from a quarter car representation to a double track model.
The model inputs can be pre-defined signals written in code or built signal shapes. Most
commonly discussed vehicle model type is the Bicycle (single track) model. It generally
caters for linear vehicle behaviour with specific assumptions. Also, to model certain
active safety systems like ESC, ABS, etc., a bicycle model is used as a reference model
in the design. One of the limitations of offline simulations is that it doesn’t have a
graphical interface or representation to visualize simulations as they are carried out.
With reference to Figure 1.1, a scroll model is another type of vehicle model. It may or
may not be real time. A scroll model allows you to rapidly change the model inputs
using a scroll bar which can be used for pedals & steering wheel inputs. This provides
more control during a simulation.
An online mode refers to a synchronisation of a vehicle model with a graphical
interface/representation while running in real time. The structure would consist of a
hardware element which would be the source of input for the simulation software.
Depending on the level of complexity, the online mode would cover a range of
simulators from an office level desktop simulator to a multiple DOF motion simulator.
This thesis refers to the office-level desktop simulator which provides a simpler &
portable platform for online simulations with varying levels of realism. Figure 1.1
represents a hierarchy of common types of simulators which have been encountered
during the course of this thesis. They have been judged on the basis of model
flexibility/physical portability & levels of realism. For this thesis, flexibility of a model
represents its ability to switch to different vehicles rapidly without requiring complex
model tuning. However, realism would represent how close a simulator is to
representing real vehicle behaviour. On this scale then, the most flexible/portable type
would be an offline simulation setup and the most realistic would be a real test vehicle.
However, this plot is used for representation purposes based on understanding during
the course of this thesis and may not be accurate globally. Also, as simulators are judged
on the basis of driver’s perception of his surroundings and ‘feel’, the position of the
different simulators in Figure 1.1 may vary comprehensively.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
3
1
Offline Simulations
Scroll Model
Desktop Simulator (1-Screen)
Desktop Simulator (3-Screen)
Flexibility/Physical Portability
Fixed Frame Simulator
Motion Platform with limited motion
(Chalmers S2 Simulator)
Test Vehicle
Motion Platform with high motion envelope
(VTI Sim IV)
0
0
1
Realism
Figure 1.1 Driving Simulator hierarchy
The Chalmers Desktop Simulator is presented under the ASTAZero project based on
the test track in Borås. The ASTAZero project represents a test environment for
technological advancements on road & traffic safety systems. One of the first projects
involves enhancement of safety protocols for ambulance drivers by simulating
dangerous road situations in a conditioned test environment. This thesis deals with one
of the driving scenarios suggested by Region Västra Götaland (VGR) and aims to
simulate this scenario while using the standard ambulance vehicles.
1.1
Thesis Description
The Chalmers Desktop Simulator comprises of a hardware setup supported by a vehicle
model designed in Simulink & a ‘graphical representation’ designed in Qt Creator. A
comprehensive evaluation of the vehicle model while establishing its modularity with
offline and online manoeuvres is described in this thesis along with an advanced case
study on a particular scenario suggested by Region Västra Götaland (VGR). The offline
and online manoeuvres are chosen such that they indicate possible driver behaviour
during the advanced scenario.
This thesis also intends to provide a parameterised vehicle model having ESC & ABS
functionalities with moderate levels of tuning conducted to incorporate the different
ambulance vehicles to be used for testing.
This thesis should be able to provide a platform for further development of the desktop
simulator at Chalmers and contribute towards the ASTAZero project in a minor
capacity.
Goals & Deliverables
1. Establish modularity of vehicle model
2. Verification of the vehicle model by displaying flexibility of model equations
& parameters for parameterization of multiple vehicles.
4
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
a. Offline Simulations

Straight line Braking Test (also Online)

Sine With Dwell steer
b. Online Simulations

Simple Case - Double Lane Change (ISO 3881)
3. Establish ESC and ABS functionality for the vehicle model and tune parameters
accordingly.
4. Case study of ‘Scenario III’ (advanced) to describe/show vehicle behaviour.
Research Questions
1. Do ABS/ESC subsystems display functionality when integrated into vehicle
model and in simulator? Do they activate/function for online simulations? If
yes, how accurate are the results?
2. (a) Is it possible to parameterize the vehicle model with a few parameters and
still represent a decent behaviour when changing between different vehicle
types?
(b) As vehicle model is parameterized to multiple vehicles (Sedan passenger car
and various weight ambulances), do ABS/ESC subsystems survive
parameterization?
3. Is simulator realistic enough to show/measure difference with/without
ABS/ESC systems?
Intended User
As stated earlier, the Desktop simulator is intended for usage at an office-level. The
targeted user of this simulator would be an engineer working on the simulation team
for a particular project, as the simulator would be able to provide him/her with sufficient
simulation data to further investigate the potential idea before moving toward higher
fidelity simulators. However, results are not intended to be at par with higher fidelity
simulators.
For the ASTAZero project, this simulator is seen as a training tool to get potential
drivers familiar with the ASTAZero environment, the scenarios and provide them
adequate training before moving to the track.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
5
Test Vehicles Description – Ambulances
The external vehicle model is parameterized to standard ambulance vehicles which are
utilised by Region Västra Götaland (VGR) in Sweden currently. The ambulances are
the Mercedes VitoXL(AWD) and Sprinter (RWD) with low roof and high roof options.
Vehicle specifications can be found in Appendix. A base vehicle (S40) was also used
to judge the functionality of the ABS & ESC systems.
However, during the course of this thesis, a complete set of parameter values, as needed
by the vehicle model, could not be accurately compiled. Due to this, a method of scaling
parameter values from the base vehicle was implemented to complete the list for the
ambulances.
Scenario III
The three preliminary scenarios suggested by VGR are to be tested on the ASTAZero
Track as shown in Figure 1.2.
Figure 1.2[8] ASTAZero Track Environment
One of the three scenarios suggested, is focused on, in this thesis. Scenario III uses the
rural road environment on the ASTAZero track. As the track consists of a loop, the
scenario will be performed depending on the driver lap.
6
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Figure 1.3 Scenario III
Figure 1.3 illustrates the scenario III and is basically a visual obstruction scenario. The
test vehicle approaches an intersection or a crossing but the driver’s vision is obstructed
so it is unable to see the ‘balloon’ car approaching the crossing. As the balloon car joins
the road, the test vehicle employs evasive driving behaviour which may either be full
brake or a single/double lane change. In this thesis, the original scenario is modified
and instead of a balloon car, position triggered cones are utilised to simulate the same
response. The modified scenario will elaborated upon in Section 3.1.
Thesis Limitations
1. As the vehicle model demands more vehicle parameters than publically
available, it hinders the accuracy of the simulation results.
2. For this simulator, flexibility and physical portability is an integral part of the
purpose. This implies that it should be a self-sufficient package. The usage of
an Ethernet-specific external PC for real time simulation is considered as an
accepted exception from this intention.
3. Scenario modularity was restricted to 3 different environments – rural road,
highway and country-side. Modularity couldn’t be explored for roads with
different friction conditions.
4. In terms of visual display, the driver does not see the width of the car and does
not experience (visually) pitch and roll movements.
5. As the project is still ongoing, the thesis does not intend to document the vehicle
model and scenarios model entirely as modular parts of the overall software
architecture. Instead, the thesis refers to future documentation from overall
ASTAZero SIM project
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
7
1.2
Theory of Vehicle Dynamics
Position of Centre of Mass[4]
Figure 1.4 Experimental Determination of longitudinal position of centre of mass[4]
Equations to determine longitudinal position using equilibrium equations, with
reference to Figure 1.4(a) 𝐹𝑧1 + 𝐹𝑧2 = 𝑚𝑔
(1.1)
𝑙𝐹𝑧1 = 𝑏 ∗ 𝑚 ∗ 𝑔
(1.2)
𝑎=𝑙
𝐹𝑧2
𝐹𝑧1 + 𝐹𝑧2
(1.3)
𝑏=𝑙
𝐹𝑧1
𝐹𝑧1 + 𝐹𝑧2
(1.4)
Height of Centre of Mass[4]
With reference to Figure 1.4(b), the front axle is set on a platform with height h with
respect to the platform on which the rear axle is located. If hG is greater than radius
under load of the wheels, the force Fz1’, measured at the front axle is much smaller than
that measured on level road, then –
𝐹𝑧1 ′ = 𝐹𝑧1 − ∆𝐹𝑧
(1.5)
𝐹𝑧2 ′ = 𝐹𝑧2 + ∆𝐹𝑧
(1.6)
So, the equilibrium equation for rotations about the centre of the front axle is –
𝑚𝑔[𝑎 cos(𝛼) + (ℎ𝐺 − 𝑅𝑙1 ) sin(𝛼)]
= (𝐹𝑧2 + ∆𝐹𝑧 )[𝑙 cos (𝛼) + (𝑅𝑙2 − 𝑅𝑙1 ) sin(𝛼)]
(1.7)
This implies, the centre of mass –
ℎ𝐺 =
8
𝐹𝑧2 + ∆𝐹𝑧
𝑙
𝑎
[
+ 𝑅𝑙2 − 𝑅𝑙1 ] −
+ 𝑅𝑙1
𝑚𝑔
tan(𝛼)
tan(𝛼)
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
(1.8)
Vehicle at Braking & Driving[3]
Figure 1.5 Automobile subjected to longitudinal forces & subsequent load transfer[3]
As shown in Figure 1.5, when a vehicle is subjected to longitudinal forces from braking,
to compensate for wind drag or down or upward slopes, longitudinal load transfer
occurs.
Change in tire normal loads causes change in cornering stiffness’s & the peak side
forces on the axle’s change. This effects the handling behaviour of the vehicle with the
increase or decrease of understeer gradient.
Braking forces also give rise to a state of combined slip and hence effecting lateral
forces. At hard braking, tending the wheels to lock, stability and steer ability
deteriorates severely.
Tire Slip and Vehicle Behaviour
The directional behaviour of a vehicle is deeply influenced by longitudinal forces
between tires and road. Longitudinal force causes a reduction in cornering stiffness,
hence, when applied to the front axle, the vehicle becomes more understeer or less
understeer. Whereas, when applied in the rear, it causes the opposite effect[4].
For a linearized model[4],
2
𝐹𝑥
𝐶𝑖 = 𝐶0𝑖 √1 − ( 𝑖 )
𝜇𝑝 𝐹𝑧𝑖
(1.9)
A larger ratio Fx / Fz at the rear wheels makes the vehicle more oversteer and readily
introduces a critical speed[4]. As limiting conditions are reached, a spinout is expected
unless the driver reduces the longitudinal forces and counter steers[4]. To avoid this,
anti-spin and anti-lock devices are essential[4]. Poor road conditions (road friction) also
influence the Fx / Fz ratio hence contributing to vehicle behaviour.
A change in tire lateral slip also effects the tire cornering stiffness’s, thereby influencing
tire forces (lateral & longitudinal) and hence vehicle stability.
Magic formula is an example displaying the influence of tire slip on tire forces.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
9
The Magic Formula Tire Model[3]
Figure 1.6 Curve produced by the original sine version of the Magic Formula[3]
The magic formula y(x) typically produces a curve that passes through the origin x = y
= 0, reaches maximum and subsequently tends to horizontal asymptote.
𝑦 = 𝐷𝑠𝑖𝑛[𝐶 arctan{𝐵𝑥 − 𝐸(𝐵𝑥 − 𝑎𝑟𝑐𝑡𝑎𝑛𝐵𝑥)}]
𝑌(𝑋) = 𝑦(𝑥) + 𝑆𝑣
𝑥 = 𝑋 + 𝑆𝐻
Where,
Y: output variable Fx, Fy or Mz
X: input variable tanα or κ
And,
B: Stiffness factor
C: Shape factor
D: Peak value
E: Curvature factor
SH: Horizontal shift
SV: Vertical shift
For given values of coefficients B, C, D & E, the curve shows an anti-symmetric shape
with respect to the origin. To allow the curve to have an offset with respect to the origin,
two shifts SH & SV have been introduced.
10
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
2
Simulator Design
2.1
Components – HW & SW
Figure 2.1 Chalmers Simulator structure
Figure 2.1 displays the hardware and software components of the Chalmers desktop
simulator.
Hardware –
1. Steering wheel, Pedals, Gear Lever – Logitech G27
2. Simulator PC
3. xPC Target PC – for real time applications
Software –
1. Vehicle Model modelled in Simulink (Simulator PC) compiled for real time
usage (xPC target PC).
2. vsim12 project written in Qt Creator communicating with –
1. Visir – For display
2. Siren – For audio
3. Simulation files in vsim12 (core) – For simulation settings
A detailed explanation of the individual components can be found in[1].
2.2
Visual Display Flowchart
As Figure 2.1 illustrates, the simulator software has two parts to it. The external vehicle
model is responsible for depicting the dynamic behaviour of the vehicle to be simulated
and the visual display takes care of the driver view, scenery and environment models.
A detailed explanation of the external vehicle model will be done in the Section 2.3.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
11
Figure 2.2 Flowchart of Visual Display
The ‘visual display’ or what the driver can see through the monitor while driving is the
output of running the vsim12 project. As shown above, vsim12 has three essential
components which interact with each other to simulate the driving experience.
The simulation core represents a number of project files through which a simulation
can be controlled and dictated. The settings mentioned under simulation core in Figure
2.2 are just some of the essential files governing a simulation.
The simulation video is provided through an application called Visir which interacts
receives input from vsim12 and creates the driver’s view while driving.
The simulation audio is created by running two applications namely Csound & Siren in
which a predesigned sound file is loaded which replicates the sound of a vehicle (either
car or truck). Hence, while driving, at the moment, the engine revving along with wind
resistance is audible over the speakers.
2.3
Vehicle Model
Model Communication
The external vehicle model fed into the simulator was originally provided by VTI. It is
a double track model with individual subsystems for different components of a vehicle.
The vehicle model interacts with the simulator software via xPC target computer to run
in real time.
The communication is carried out using UDP (User Datagram Protocol) via LAN
cables.
12
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Figure 2.3 Communication – External Vehicle Model
As illustrated in Figure 2.3, the external vehicle model communicates with the vsim12
project (written in Qt Creator) via UDP and uses the IP addresses of both the host and
target computers. xPC target is an environment which uses a target PC, separate from
the host PC, for running real-time applications[5]. UDP is a transport protocol similar to
TCP, however unlike TCP, UDP provides a direct method to send and receive packets
over an IP network[5]. UDP uses this direct method at the expense of reliability by
limiting error checking and recovery[5].
However, there are other ways of making the simulation run in real time which remove
the usage of a ‘second’ PC. Simulink Coder is a solution which converts the vehicle
model directly into readable code for simulator software, however, it couldn’t be used
in this thesis as it would require more computing time but is a probable solution for
future simulations.
The steering wheel and pedals are responsible for the input to the vehicle model along
with the vehicle parameters. The entire vehicle model is downloaded onto the xPC
target computer and this communicates with vsim12. It can be said that the vehicle
model is essentially running on the xPC target computer.
As steering feedback is also calculated in the vehicle model, it is sent back to the
steering wheels and its intensity of the different force effects can be dictated using
Logitech’s steering software.
Model Structure
The vehicle model contains 7 interconnected blocks representing essential vehicle
components. The model, itself, has its own set of I/O signals which communicate with
vsim12. A complete list of I/O signals is attached as Appendix.
Upon close examination of the logged data, it was concluded that the model follows
‘Modified ISO 8855’ as a technical standard. The Modified ISO 8855 is quite similar
to ISO 8855[23] in many cases except the measurement of tire side slip angles which is
considered opposite. Key definitions are described in Appendix D.
The important degrees of freedom considered while modelling were:


6x1 DOF – Body (translational & rotational) at COG
2x4 DOF – Wheels (rotational and vertical)
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
13
Notation – Vehicle Model comprises 7 Blocks (ex. Steer, Wheel, etc)
- Each block has number of subsystems
Figure 2.4 Model Overview
As illustrated in Figure 2.4, each block has their individual set of I/O interacting with
other blocks in the model. Each block also contains a number of subsystems which help
create the final bus signal for the block output. An overview of the various blocks will
be given in the next section. Also, the ESC system will be explained in Section 2.3.5
The model has ‘guards’ to check/reset the simulation when it’s completed or displays
errors. A watchdog timer is used to perform a system reboot when a programmable
timeout occurs[5]. Along with the reset function, the watchdog is responsible for
resetting the model to its original state.
Depending on the type of mode, the model I/O is given through Simulink scripts
(offline) or through vsim12 (online). This will be further explained in the Section 2.3.4.
Model Blocks
2.3.3.1 Steer Block
The block subsystems are:


Steering Angle – LF, RF, LR, RR
Steering Wheel Torque
The steer block uses the steering wheel angle and computes the wheel angles for all 4
wheels and also the steering wheel torque. The model, in its current state, caters for 2
wheel steering so front wheel subsystems receive the steering input to calculate their
wheel angles. The model, presently, does not model compliance in steering system.
14
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Besides having no steering input, the rear wheels also have no torsion bar angle. The
‘directness’ of steering feel during online simulations inspired a need for a simple delay
factor to be added to the subsystem while calculating wheel angle.
The formula used to calculate the road wheel angle was 𝛿𝑤 =
𝛿−𝑎𝑛𝑔𝑙𝑒_𝑡𝑏
𝑆𝐺_𝑟𝑎𝑡𝑖𝑜
+ (𝐹𝑦𝑡𝑖𝑟𝑒 ∗ 𝐶𝛿𝐹𝑦 ) + (𝑀𝑧 ∗ 𝐶𝛿𝑀𝑧 ) + (∅𝑚𝑜𝑡 ∗ 𝐶𝛿𝜑 ) + 𝜓𝑜𝑠𝑡𝑎𝑡𝑖𝑐
(2.1)
Where,
δw = road wheel angle (rad)
angle_tb = Torsion bar angle (rad)
CδFy = Suspension compliance for Lateral Force (rad/N)
CδMz = Suspension torsional compliance (rad/Nm)
Cδφ = Roll Steer coefficient
Φmot = Roll angle due to motion (rad)
The steering coefficients & static toe angle shown in Equation 2.1 are considered for
individual axles and are taken from the vehicle parameters. Also the roll steer was
calculated using the roll angle due to motion.
As mentioned above, steering wheel torque is another variable calculated in the steer
block as a separate subsystem. The steer block is modelled on a servo steering system
dependant on speed. Hence, the servo characteristics are provided for three different
ranges of velocities.
However, the current model only uses coefficient values for the low speed range as
there is a need for parameter tuning for the speed dependant servo steering to be
effective. The servo pressure is a function of the steering wheel torque.
Figure 2.5 Steering Torque structure
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
15
The calculation for steering torque (Figure 2.5) is complex as it constitutes calculating
the root of a 5th order polynomial so a Matlab script was written to define this. The
steering torque considers spring and damper effects in its calculation. However, the
feedback experienced in the simulator also incorporates the friction and vehicle inertia
effects which can be triggered as required. Since these settings are within the steering
wheel equipment, they are not open for the user. This implies that the steering feedback
calculated in the vehicle model is received by VTI’s software but is not adequately fed
to the Logitech steering console due to its construction. The force feedback felt by the
driver is manually adjusted through Logitech’s console interface. The iterating
frequency for force feedback was restricted by the steering wheel’s capabilities.
Besides the steering torque, the torsion bar angle is also calculated in the subsystem
using the steering torque and torsion bar stiffness.
2.3.3.2 Suspension Block
The block subsystems are:

Suspension Spring & Damper – LF, RF, LR, RR
The Suspension block represents a simple spring-damper subsystem whose output is
the vertical force on each wheel.
Depending on the mode of operation, road profile is fed into this subsystem via vsim12
project (online) or Matlab code (offline). For all offline simulations, the road profile is
flat which represents no vertical coordinates. But in the online mode, depending on the
road chosen, vertical coordinates maybe provided. The coordinates are extracted for
each wheel and sent to respective subsystems.
The coordinates for the wheel positions were calculated from the vehicle’s centre of
gravity. The front and rear suspensions have different coefficients for spring & damper
and along with the tire stiffness’ help calculate the vertical loads.
Table 2.1 Parameters for wheels
Description
Symbol
Left Front
Right Front
Left Rear
Right Rear
Longitudinal
Position w.r.t CG
a
lf
lf
-lr
-lr
Lateral Position
w.r.t CG
b
tw_f/2
-tw_f/2
tw_r/2
-tw_r/2
Unsprung Mass per
side
mus
mus_f/2
mus_f/2
mus_r/2
mus_r/2
The equation to calculate vertical load
16
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
𝜑 ∗ (−𝐾𝑎𝑟𝑏)
𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝 = [(
) + {(𝑍𝑐𝑔 + 𝜑 ∗ 𝑏 − 𝜃 ∗ 𝑎 − 𝑍𝑤 )(−𝐾𝑠𝑝𝑟)}
2𝑏
̇ + 𝜑̇ ∗ 𝑏 − 𝜃̇ ∗ 𝑎 − 𝑍𝑤̇ )(−𝐶𝑑𝑎𝑚𝑝)}]
+ {(𝑍𝑐𝑔
(2.2)
Where,
Fz_spr_damp = Vertical Spring & Damper Force
2.3.3.3 Driveline Block
The block subsystems are –



Gearbox
Clutch
Engine
The Driveline block receives input signals such as pedal positions, wheel velocities,
etc. The model, in its current state, can cater for FWD & RWD vehicles only. However,
the 2.8ton Mercedes VitoXL is an AWD vehicle so the model was tuned to incorporate
this. This will be explained in Section 2.3.5
The Gearbox subsystem is functional for both manual and automatic transmission. For
this thesis, the automatic transmission setting could only be used as the paddles or gear
lever positions hadn’t been coded into the vsim12 project.
The automatic transmission was designed with a shift logic which simply shifts up or
down at certain vehicle speeds. There is no shift delay modelled in the system so the
shifting is instantaneous. The velocity settings are taken from the vehicle parameters.
As the gear is selected, it would extract the required gear ratio from a look-up table and
calculate the total gear ratio via the differential gear. So, depending on which axle is
powered, the total gear ratio would be sent to the wheels on that axle.
The Clutch subsystem is a simple system depending on the pedal position provided
either by the pedal (online) or written script (offline). While running simulation with
automatic transmission it was noticed that the subsystem did not have a model for a
torque converter. This could be seen as future work. The clutch position has a range
from 0 to 1 wherein a fully pressed pedal would be 1 (clutch disengaged) and 0, a free
pedal (clutch engaged). However, the clutch subsystem could not be tested in a manual
setting as all simulations were carried out with automatic transmission
Lastly, the Engine subsystem calculates the engine torque based on throttle and engine
speed. Starting from the wheel velocities on the left & right, a range of calculation steps
(including a low pass filter & speed limiter) are used to calculate the engine speed. For
every vehicle, an engine map is fed into the model which calculates the required engine
torque for a particular throttle setting.
The driveline block uses a driveshaft moment of inertia value of 0.7 which is used to
calculate the complete driveline inertia.
2
𝐼𝑑𝑟𝑣𝑒𝑓𝑓 = 𝐼𝑑𝑟𝑣 + (0.5 ∗ 𝐼𝑒𝑛𝑔 ∗ 𝐺𝑅𝑡𝑜𝑡
)
(2.3)
Where,
Idrveff = Effective Driveline Inertia
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
17
2.3.3.4 Brake Block
Figure 2.6 Brake block
As Figure 2.6 indicates, the brakes block receives brake pressure for all 4 wheels and
uses torque line pressure gradient to convert the pressure into brake torque.
This model does not have an ABS model, hence it was modelled in [1].
2.3.3.5 Wheels Block
The block subsystems are –




Magic Formula 5.2 – LF
Magic Formula 5.2 – RF
Magic Formula 5.2 – LR
Magic Formula 5.2 – RR
The wheels block, after the chassis block, is the most comprehensively modelled system
in this vehicle model. Among inputs from other blocks, it receives inputs such as
camber track wall and friction values from vsim12 (road) for its calculations.
Figure 2.7 Magic Formula 5.2 subsystem
18
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Figure 2.7 shows the I/O to the MF 5.2 subsystem. The MF 5.2 subsystem calculates a
number of wheel variables over a particular manoeuvre. Stated below are some of the
equations used to calculate the wheel variables Wheel rotational speed 𝜔𝑤ℎ𝑙 = ∫ 𝜔̇ 𝑤ℎ𝑙 =
𝑇𝑞_𝑑𝑟𝑣 − 𝑇𝑞𝑏𝑟𝑘 − (𝐹𝑧 ∗ 𝑅𝑤 ∗ 𝑓𝑟 ) − (𝐹𝑥𝑡𝑖𝑟𝑒 ∗ 𝑅𝑤 )
𝐼𝑤 + 𝐼𝑑𝑟𝑣_𝑒𝑓𝑓
(2.4)
Longitudinal Slip for traction 𝜅=
(𝜔𝑤ℎ𝑙 ∗ 𝑅𝑤 ) − (𝑉𝑥 + 𝜓̇ ∗ (−𝑏))
max (𝑎𝑏𝑠(𝜔𝑤ℎ𝑙 ∗ 𝑅𝑤 ) , 𝑉𝑥𝑠𝑙𝑖𝑝 )
(2.5)
Longitudinal Slip for braking 𝜅=
(𝜔𝑤ℎ𝑙 ∗ 𝑅𝑤 ) − (𝑉𝑥 + 𝜓̇ ∗ (−𝑏))
max (𝑉𝑥𝑠𝑙𝑖𝑝 , 𝑎𝑏𝑠 (𝑉𝑥 + 𝜓̇ ∗ (−𝑏)))
(2.6)
Lateral Slip –
𝑉𝑦 + 𝑎 ∗ 𝜓̇
𝛼𝑡1 = 𝛿 − arctan [
]
𝑉𝑥 − 𝑏 ∗ 𝜓̇
𝛼𝑡2 = ∫
(2.7)
𝑉𝑥 (𝛼𝑡1 − 𝛼𝑡2 )
𝐹𝑧 ∗ 𝐶𝐹𝛼
Camber Angle –
𝛾 = [ 𝛾0 + 𝛾𝑟𝑜𝑎𝑑 + (𝜑𝑚𝑜𝑡 ∗ 𝐶𝛾𝜑 ) + (𝐹𝑦𝑡𝑖𝑟𝑒 ∗ 𝐶𝛾𝐹𝑦 )]
(2.8)
Where,
γ0 = Static Camber angle (rad)
γroad = Camber Trackwall (rad)
Cγφ = Roll Camber Coefficient (Front, Rear)
CγFy = Coefficient for Camber due to lateral force (rad/N) (Front, Rear)
CFα = Tire Cornering Stiffness (N/rad)
The tire forces and aligning torque are calculated using Pacejka’s Magic Formula
version 5.2 (2001). The MF tire calculates the forces (Fx, Fy) & moments (Mx, My, Mz)
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
19
acting on the tire under pure & combined slip conditions on arbitrary 3D roads using
longitudinal, lateral & turn slip, camber angle & vertical force (Fz) as input quantities[6].
The general form of the formula that holds for given values of vertical load & camber
angle reads[3]:
𝑦 = 𝐷𝑠𝑖𝑛[𝐶 arctan{𝐵𝑥 − 𝐸(𝐵𝑥 − 𝑎𝑟𝑐𝑡𝑎𝑛𝐵𝑥)}]
𝑌(𝑋) = 𝑦(𝑥) + 𝑆𝑣
𝑥 = 𝑋 + 𝑆𝐻
Where,
Y: output variable Fx, Fy or Mz
X: input variable tanα or κ
Figure 2.8 Function block – Magic Formula
For this model, Figure 2.8 shows the I/O to the function block. As the magic formula
consists of a set of equations along with scaling factors, a Matlab script is fed into the
model which lists the magic formula parameters[6] for different road conditions such as
dry, wet, snowy, etc.
The function block contains the full set of equations from Pacejka’s magic formula.
However, turn slip or path curvature is not modelled in the subsystem.
Tire forces in the vehicle body coordinate system –
𝐹𝑥𝑏𝑜𝑑𝑦 = (𝐹𝑥𝑡𝑖𝑟𝑒 ∗ cos 𝛿) − (𝐹𝑦𝑡𝑖𝑟𝑒 ∗ sin 𝛿)
(2.9)
𝐹𝑦𝑏𝑜𝑑𝑦 = (𝐹𝑥𝑡𝑖𝑟𝑒 ∗ sin 𝛿) + (𝐹𝑦𝑡𝑖𝑟𝑒 ∗ cos 𝛿)
(2.10)
Where,
Fx body = Longitudinal Tire Force in vehicle body coordinate systems (N)
Fy body = Lateral Tire Force in vehicle body coordinate systems (N)
20
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
2.3.3.6 Axles Block
The block subsystems are –


Axle Load – Front
Axle Load – Rear
The vertical load on each wheel on an axle is a combination of different loads acting on
the wheel.
For front axle,
𝑆𝑝𝑟𝑢𝑛𝑔 𝑀𝑎𝑠𝑠 = 𝑚𝑠𝑓𝑟𝑜𝑛𝑡 =
𝑚𝑠 ∗ 𝑙𝑟
𝑙𝑓 + 𝑙𝑟
(2.11)
𝑠𝑡𝑎𝑡𝑖𝑐 𝑙𝑜𝑎𝑑 = (𝑚𝑠𝑓𝑟𝑜𝑛𝑡 + 𝑚𝑢𝑠_𝑓) ∗ 𝑔
(2.12)
For rear axle,
𝑆𝑝𝑟𝑢𝑛𝑔 𝑀𝑎𝑠𝑠 = 𝑚𝑠𝑟𝑒𝑎𝑟 =
𝑚𝑠 ∗ 𝑙𝑓
𝑙𝑓 + 𝑙𝑟
(2.13)
𝑠𝑡𝑎𝑡𝑖𝑐 𝑙𝑜𝑎𝑑 = (𝑚𝑠𝑟𝑒𝑎𝑟 + 𝑚𝑢𝑠_𝑟) ∗ 𝑔
(2.14)
This implies,
Right wheel 𝐹𝑧𝑟𝑖𝑔ℎ𝑡 = [(0.5 ∗ 𝑠𝑡𝑎𝑡𝑖𝑐 𝑙𝑜𝑎𝑑 ∗ cos(𝐴𝑣𝑒𝑠𝑙𝑜𝑝𝑒 )) + 𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑟𝑖𝑔ℎ𝑡 + (𝑎𝑦
(𝑚𝑠∗ℎ𝑟+𝑚𝑢𝑠∗ℎ𝑢𝑠)
𝑡𝑤
)]
(2.15)
Left wheel,
𝐹𝑧𝑙𝑒𝑓𝑡 = [(0.5 ∗ 𝑠𝑡𝑎𝑡𝑖𝑐 𝑙𝑜𝑎𝑑 ∗ cos(𝐴𝑣𝑒𝑠𝑙𝑜𝑝𝑒 )) + 𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑙𝑒𝑓𝑡 − (𝑎𝑦
(𝑚𝑠∗ℎ𝑟+𝑚𝑢𝑠∗ℎ𝑢𝑠)
𝑡𝑤
)]
(2.16)
2.3.3.7 Chassis Block
The block subsystems are –






Speed & Acceleration Calculation
Yaw Calculation
Roll Calculation
Pitch Calculation
Vertical Movement of CG
Position in global coordinate
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
21
Figure 2.9 Chassis block
All the subsystems labelled above calculate the essential variables to study the vehicle
state in terms of vehicle position (in x & y), movement (in x, y & z) & rotation (in x, y,
& z).
The various equations used in the model are –
𝐹𝑥_𝑠𝑙𝑜𝑝𝑒 = 𝐴𝑣𝑒𝑠𝑙𝑜𝑝𝑒 ∗ (−𝑚 ∗ 𝑔)
(2.17)
For vehicle velocities,
𝐹𝑥_𝑎𝑖𝑟 = 0.5 ∗ 𝜌 ∗ 𝐶𝑎𝑥 ∗ 𝐴0 ∗ 𝑉𝑥2
𝑉𝑥 = ∫ 𝑉𝑥̇ = [(𝑉𝑦 ∗ 𝜓̇) +
1
(𝐹
+ 𝐹𝑥_𝑏𝑜𝑑𝑦_𝑟𝑓 + 𝐹𝑥_𝑏𝑜𝑑𝑦_𝑙𝑟 + 𝐹𝑥_𝑏𝑜𝑑𝑦_𝑟𝑟
𝑚 𝑥_𝑏𝑜𝑑𝑦_𝑙𝑓
+ 𝐹𝑥_𝑎𝑖𝑟 + 𝐹𝑥_𝑠𝑙𝑜𝑝𝑒 )]
𝑉𝑦 = ∫ 𝑉𝑦̇ = [
(2.18)
(2.19)
1
(𝐹
+ 𝐹𝑦_𝑏𝑜𝑑𝑦_𝑟𝑓 + 𝐹𝑦_𝑏𝑜𝑑𝑦_𝑙𝑟 + 𝐹𝑦_𝑏𝑜𝑑𝑦_𝑟𝑟 + 𝐹𝑦_𝑠𝑙𝑜𝑝𝑒 )
𝑚 𝑦_𝑏𝑜𝑑𝑦_𝑙𝑓
− (𝑉𝑦 ∗ 𝜓̇)]
(2.20)
For vehicle acceleration,
𝑎𝑥 =
𝑎𝑦 =
22
1
(𝐹
+ 𝐹𝑥_𝑏𝑜𝑑𝑦_𝑟𝑓 + 𝐹𝑥_𝑏𝑜𝑑𝑦_𝑙𝑟 + 𝐹𝑥_𝑏𝑜𝑑𝑦_𝑟𝑟 + 𝐹𝑥_𝑎𝑖𝑟
𝑚 𝑥_𝑏𝑜𝑑𝑦_𝑙𝑓
+ 𝐹𝑥_𝑠𝑙𝑜𝑝𝑒 )
1
(𝐹
+ 𝐹𝑦_𝑏𝑜𝑑𝑦_𝑟𝑓 + 𝐹𝑦_𝑏𝑜𝑑𝑦_𝑙𝑟 + 𝐹𝑦_𝑏𝑜𝑑𝑦_𝑟𝑟 + 𝐹𝑦_𝑠𝑙𝑜𝑝𝑒 )
𝑚 𝑦_𝑏𝑜𝑑𝑦_𝑙𝑓
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
(2.21)
(2.22)
With reference to Table 2.1 –
Vehicle yaw angle,
𝜓 = ∫ 𝜓̇ = ∫ 𝜓̈
1
= [ {(𝐹𝑦_𝑏𝑜𝑑𝑦_𝑙𝑓 ∗ 𝑎𝑙𝑓 ) + (𝐹𝑦_𝑏𝑜𝑑𝑦_𝑟𝑓 ∗ 𝑎𝑟𝑓 ) + (𝐹𝑦_𝑏𝑜𝑑𝑦_𝑙𝑟 ∗ 𝑎𝑙𝑟 )
𝐼𝑧
+ (𝐹𝑦_𝑏𝑜𝑑𝑦_𝑟𝑟 ∗ 𝑎𝑟𝑟 ) − (𝐹𝑥_𝑏𝑜𝑑𝑦_𝑟𝑟
∗ 𝑏𝑟𝑟 )−(𝐹𝑥_𝑏𝑜𝑑𝑦_𝑙𝑟 ∗ 𝑏𝑙𝑟 ) − (𝐹𝑥_𝑏𝑜𝑑𝑦_𝑟𝑓 ∗ 𝑏𝑟𝑓 ) − (𝐹𝑥_𝑏𝑜𝑑𝑦_𝑙𝑓 ∗ 𝑏𝑙𝑓 )
+ 𝑀𝑧_𝑙𝑓 + 𝑀𝑧_𝑟𝑓 + 𝑀𝑧_𝑙𝑟 + 𝑀𝑧_𝑟𝑟 }]
(2.23)
Vehicle roll angle,
𝜑 = ∫ 𝜑̇ = ∫ 𝜑̈
1
= [ {(𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑙𝑓 ∗ 𝑏𝑙𝑓 ) + (𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑟𝑓 ∗ 𝑏𝑟𝑓 )
𝐼𝑟
+ (𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑙𝑟 ∗ 𝑏𝑙𝑟 ) + (𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑟𝑟 ∗ 𝑏𝑟𝑟 ) + (𝑎𝑦 ∗ 𝑚𝑠 ∗ ℎ𝑠𝑟)
+ (𝜑 ∗ 𝑚𝑠 ∗ ℎ𝑠𝑟 ∗ 𝑔)}]
(2.24)
Vehicle pitch angle,
𝜃 = ∫ 𝜃̇ = ∫ 𝜃̈
1
{(𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑙𝑓 ∗ 𝑎𝑙𝑓 ) + (𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑟𝑓 ∗ 𝑎𝑟𝑓 )
𝐼𝑦
+ (𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑙𝑟 ∗ 𝑎𝑙𝑟 ) + (𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑟𝑟 ∗ 𝑎𝑟𝑟 ) + (0.8
= [−
∗ ℎ𝑐𝑔 (𝐹𝑥_𝑏𝑜𝑑𝑦_𝑙𝑓 + 𝐹𝑥_𝑏𝑜𝑑𝑦_𝑟𝑓 + 𝐹𝑥_𝑏𝑜𝑑𝑦_𝑙𝑟 + 𝐹𝑥_𝑏𝑜𝑑𝑦_𝑟𝑟 ))}] (2.25)
Vertical movement of CG,
̇ = ∫ 𝑍𝑐𝑔
̈
𝑍𝑐𝑔 = ∫ 𝑍𝑐𝑔
1
(𝐹
+ 𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑟𝑓 + 𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑙𝑟
𝑚𝑠 𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑙𝑓
+ 𝐹𝑧_𝑠𝑝𝑟_𝑑𝑎𝑚𝑝_𝑟𝑟 )]
=[
(2.26)
Position in global coordinates,
𝑋 = ∫ 𝑉𝑥_𝑔𝑙𝑜𝑏𝑎𝑙 = [(𝑉𝑥 ∗ cos 𝜓) − (𝑉𝑦 ∗ sin 𝜓)]
(2.27)
𝑌 = ∫ 𝑉𝑦_𝑔𝑙𝑜𝑏𝑎𝑙 = [(𝑉𝑦 ∗ cos 𝜓) + (𝑉𝑥 ∗ sin 𝜓)]
(2.28)
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
23
Offline & Online model structure
The offline and online structures differ in the way they provide I/O for the vehicle
model.
2.3.4.1 Offline Model
The I/O for offline mode comprises of 2 subsystems –


Driver
Road
The output signals shown in both these subsystems are coded in Matlab and depending
on the offline manoeuvre, different inputs can be fed.
The vehicle model doesn’t contain a ‘typical’ driver model as it just supplies the
intended driver output signals to the vehicle model (open loop). For the offline model,
no stimulus is provided to the driver but with a proper driver model, stimulus can be
created to increase levels of realism.
The signals in the Driver subsystem are shown in Table 2.2
Table 2.2 – Output signals from driver to model
S.No
Signal Name
Unit
Description
1.
reset
-
Step signal
2.
SWA_in
[rad]
Steering wheel Angle
3.
throttle_in
-
Acceleration Pedal (0-1)
4.
clutch_pedal
-
Clutch Pedal (0-1)
5.
gear_manual
-
Manual Gear
6.
BRK_lf_in
[Pa]
Brake Pressure LF
7.
BRK_rf_in
[Pa]
Brake Pressure RF
8.
BRK_lr_in
[Pa]
Brake Pressure LR
9.
BRK_rr_in
[Pa]
Brake Pressure RR
10.
Vx_max
[m/sec]
Max. Longitudinal Velocity
11.
auto_gear
[]
Automatic Gear Flag
All the signals stated in Table 2.2 are compiled from a Matlab script or prescribed in
the vehicle parameters. This replaces the actual input typically provided by a driver in
24
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
a real car. But in the case of brakes, instead of brake position, brake pressure is supplied
to the model.
The offline simulations for this thesis will be further elaborated upon in Section 4.1.1.
The signals in the road subsystems are shown in Figure 2.10. The feedback provided to
the road subsystem is the vehicle position in X direction based on the global coordinate
system. However, with a ‘dedicated’ road model, the input signals to the road model
can arbitrarily range from no input signals (constant road) to signals modelled
depending on the definition of the surrounding environment the vehicle is.
Figure 2.10 Road subsystem
1
𝐴𝑣𝑒 𝑠𝑙𝑜𝑝𝑒 = [ (𝑑𝑧𝑑𝑥𝑙𝑓 + 𝑑𝑧𝑑𝑥𝑟𝑓 + 𝑑𝑧𝑑𝑥𝑙𝑟 + 𝑑𝑧𝑑𝑥𝑟𝑟 )]
4
(2.29)
Table 2.3 – Signals from road to model
S.No
Bus Name
Signal
Description
1.
LF, RF, LR,
RR
z
Road coordinate in z-direction
dzdx
Road coordinate in z-direction
with respect to x
dzdy
Road coordinate in z-direction
with respect to y
camber_trackwall
(γroad)
Camber angle due to inclination of
road
2.
Ave slope
3.
mu
Average slope of road
LF
Coefficient of friction (tire – road)
RF
LR
RR
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
25
For the simulations in this thesis, the road coordinates are assumed for a level road with
no banking or inclination angles & dry conditions. Hence, the coefficient values for
tire-road friction are set as 1.
2.3.4.2 Online Model
The online model communicates with the simulator software (vsim12 project written
on Qt Creator) and is the one used for all the online simulations in this thesis. Hence,
the road and driver inputs to the model are received from vsim12 through UDP packets
(refer Figure 2.3) and certain variables are transmitted back to vsim12 from the model.
A complete list of vehicle I/O is attached as Appendix A.
The relative position and orientation of the test driver in the simulated vehicle is added
in vsim12. A total of 6 parameters (scalar) are written in an xml file (frame.xml) on the
project along with the relative position & orientation of the simulator screen(s) and the
rear view mirrors. Besides this, the physical position of the driver from the simulator
screen and the gaze angle can also be fed into frame.xml.
In comparison with the offline model, there is more feedback provided to vsim12 in the
online model. The feedback is provided from model blocks such as Steer, Driveline,
Chassis, Wheels and Axles (refer Figure 2.4 for I/O structure). This is mainly done,
among other things, for data logging. Total number of signals logged are 138, however
a lot of the variables are logged more than once, hence a rough number would be close
to 100.
The data communication takes place with the help of two subsystems, namely, UDP
I/O and UDP processing. In UDP processing, feedback signals from the model are
combined to create a bus signal called UDP output. Also the input signals to the model
from vsim12 are combined to create the UDP input bus signal. In UDP I/O, the packing
and unpacking of the bus signals takes place.
A Matlab script is written to indicate the simulator communication parameters such as
port numbers, simulation time step, etc. For this thesis, the number of signals received
from vsim12 is restricted to 35. These signals include steering wheel angle, accelerator
pedal position, etc.
Electronic Stability Control system
The Electronic stability control system used in this thesis is taken as a reference from a
student thesis[7] conducted on the Chalmers Motion Platform simulator (S2) and
adapted to the vehicle model. A detailed description of the system can be found in[7].
26
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Figure 2.11 ESC Overview
The ESC system is designed as a yaw control by brake system which is a common
system designed for understeer - oversteer mitigation. The ESC system designed for the
Chalmers Motion Platform Simulator (S2) implements yaw control as well as side slip
control. It uses a single track bicycle model as the linear reference model to compute
the desired yaw rate and side slip.
Figure 2.12 ESC Structure
With reference to Figure 2.12, the actual and desired values are compared and fed into
a PD controller to compute the required torque. The required torques from the yaw rate
control and side slip control are arbitrated to determine ‘superiority’ and the
correctional torque is finally distributed to the wheels depending on the brake
distribution.
For this thesis, the side slip control was switched off and emphasis was laid on the yaw
rate control. This then skips the arbitration and computes brake torque directly from he
requested torque from the yaw control system.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
27
Parameterised Models – Ambulances
A combination of generic values & scaling of parameters was adopted to play a key
role for the parameterisation of the ambulance vehicles. An attempt was made to
determine the least number of parameters, which when scaled, influence the vehicle
behaviour. As the parameters of the S40 were deemed accurate and the model
accordingly parameterised, parameters for the ambulances were scaled using this.
The total number of parameters demanded by the external vehicle model are about 75.
The chosen parameters which were used to scale the remaining parameters are:
Scaling Parameters:
1.
2.
3.
4.
Vehicle Mass, m
Vehicle wheel base, wb
Wheel radius, Rw
Centre of Gravity height, hCG
Table 2.4 – Scaled variables
Vehicle Parameter
Scaled with
Vehicle Mass (Sprung, Unsprung)
m
Vehicle Moment of Inertia (Ix, Iy, Iz)
m
Unsprung Mass Height (F & R)
hCG
Axle Distances (F & R)
wb
Wheel rotational Inertia
Rw2
Tire (Stiffness, Damping & Lateral
Stiffness)
m*g
Suspension Coefficients (Spring,
Damper, Anti-Roll)
m*g
Roll Axle Height (F & R)
hCG
Steering Gear Ratio
wb
Torsion bar stiffness
wb
Drive shaft Moment of Inertia
m
Table 2.4 indicates a certain set of parameters which were scaled according to the
chosen parameters. To complete the remaining parameters, values from a typical van[4]
were considered.
28
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
2.3.6.1 Non-Generic parameterization
As mentioned in Section 1.1.4, the Mercedes VitoXL & Sprinter vans were utilised as
test vehicles. The main modifications made to the vehicle model, additional to the
scaling described above are:
a. Steering Block

Delay function to steering input as steering sensitivity was perceived to be
high during online driving – by VTI

The steering system incorporated in this model was a servo steered speed
dependant rack & pinion system. As the servo characteristic curves were
restricted to low speed velocity range, a certain level of parameter tuning was
carried out to approach a perceivable range of steering torque.
b. Driveline Block

Engine – As engine specs for OM 651 powering the ambulances weren’t
complete, a generic torque speed curve of the OM651[22] used in the
Mercedes C-Class was used and scaled up to the rated torque and speed
conditions of the ambulances.
c. Wheels Block

As the model uses the Magic Formula 5.2 to calculate the tire forces, the tire
coefficients were modified/scaled with reference to the typical van[4] & the
base vehicle.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
29
3
Simulator Scenarios
The simulation scenarios are chosen such that they supplement the scenarios as
suggested by representatives from VGR. The simulations are structured in such a way
that they provide information to answer the research questions stated earlier in the
thesis. Contemplating driver behaviour for the advanced case, straight line braking, sine
with dwell and double lane change tests were chosen, see Table 3.1.
As stated in Section 1.1.4, a base vehicle (Volvo S40) was used to judge the working
of the active safety systems. It was assumed that, as the vehicle model was received
with the base vehicle parameters, it is parameterised to the S40 and displays accurate
results.
Table 3.1 – Vehicle & Simulation Type for given manoeuvre
Driving Manoeuvre
Type of Simulation
Test Vehicles
Active Safety
System
Obstacle Avoidance
Online
Mercedes
Sprinter
ESC+ABS
Straight Line
Braking, SLB
Offline & Online
S40 & Mercedes
Sprinter
ABS
Sine with Dwell,
SWD
Offline
S40 & Mercedes
VitoXL
ESC+ABS
Double Lane Change,
DLC
Online
Mercedes VitoXL
ESC+ABS
(see Section 1.1.5)
Figure 3.1 Simulation Flowchart
30
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Referring to the research questions 1 & 2 in Section 1.1.2, a simulation flowchart was
prepared (Figure 3.1) which shows the decision making plan used for the simulations.
The important questions asked are –
For ABS
1. Does ABS work with the base vehicle?
a. Plot X(t) – with/without ABS
b. Plot Vx(t) – with/without ABS
c. Plot κ(t) – with/without ABS (LF,RF,LR,RR)
d. Plot Vx & ωwhl vs time – with/without ABS (LF,RF,LR,RR)
2. Does ABS survive parameterization/scaling with the test vehicle? (same plots
as above)
3. Does ABS function when shifting to online simulation? (same plots)
For ESC
1. Does ESC work with the base vehicle? – see Appendix C
a. Plot X vs Y – with/without ESC
b. Plot Vx(t) – with/without ESC
c. Plot 𝜓̇(t) – with/without ESC
d. Plot 𝜑̇ (t) – with/without ESC.
2. Does ESC survive parameterization/scaling with the test vehicle? (same plots
as above)
3. Does ESC function when shifting to online simulation? (same plots)
3.1
Obstacle Avoidance
As the ASTAZero graphical environment for the simulator wasn’t operational during
the thesis, the tests were carried out on sample roads provided by VTI. The obstacle
avoidance scenario was simulated on the ‘rural_1’ road environment as specified by
VTI.
The modified scenario recreates the original scenario (Section 1.1.5) using position
triggered cones which replace the function of the balloon car.
The ‘element of surprise’ in the original scenario established by the visual obstruction
will be replaced by position triggering in the modified scenario. If timed accurately, it
should be able to recreate the same driving situation/behaviour.
When faced with such a driving situation, the driver would either proceed with a full
brake condition or perform a lane change in order to negotiate the car/cones.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
31
Figure 3.2 Obstacle Avoidance
The ‘rural_1’ road environment chosen for this manoeuvre is also a looped track with
a typical rural setting. Oncoming traffic contributes to the driver’s decision making
when the scenario is triggered. For this thesis, position triggering couldn’t be achieved
so time triggering was used to trigger the cones after a stipulated amount of time. This
time was compounded after repeated tests on the road environment to determine the
best suitable position depending on the relative vehicle speed.
E.g. Time trigger = 200s, so as the simulation time crosses 200s, the cones are placed
80m away from the vehicle’s position at t = 200s. It is possible to optimize the position
of the cones from the car through repeated iterations of the scenario and depending on
relative vehicle speed. This would influence the vehicle to perform full braking or lane
change manoeuvres as the best optimized solution to avoid collision.
3.2
Simulation Tests
Straight Line Braking ISO 21994
The stopping distance of a road vehicle is an important part of vehicle performance &
active safety[21]. The straight line braking test represents an important test to gauge the
longitudinal behaviour of a vehicle.
Figure 3.3 Straight Line Braking
32
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
This tests was performed in collaboration with another student thesis[1]. A detailed
description of the ABS system can be found in[1].
Figure 3.4 Offline Driver Input - Straight Line Braking (S40)
The straight line braking test requires the driver to achieve a set speed of 100 km/h
within the shortest possible time with a speed margin of 2 km/h. As two vehicles (S40
and Mercedes Sprinter) are to be tested, after repeated tests, a suitable 0-100 km/h time
was determined for each of them and fed in the driver input. Total simulation time was
20 sec.
A delay time of 0.5 sec was provided between throttle off and brake on, to attempt a
realistic driver response time. A maximum brake pressure of 100 bar at full brake (for
cars) was used.
Sine wave with Dwell (SWD) TP-126-03
In the case of Sine wave with Dwell, the manoeuvre settings are seen in Table 3.2
Table 3.2 – SWD settings
SWD Setting
Values
Turn Frequency
0.7
Pre – Test velocity
87 km/h
Amplitude Test
2*90*pi/180
Time pause (dwell)
500 ms
Test Velocity
80 km/h
Total simulation time
15 sec
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
33
Driver Input
Steering wheel angle (deg)
200
100
0
-100
-200
10
10.5
11
11.5
12
Time (s)
12.5
13
13.5
14
1
Throttle
0.8
0.6
0.4
0.2
0
0
5
10
15
20
25
Time (s)
6
x 10
Figure 3.5 Offline Driver Input Test 2 – Sine wave with Dwell (S40)
Brake pressure (Pa)
10
8
6
The4 SWD tests were performed offline for two different steering amplitudes. For the 1st
test,2 the simulation settings are set according to ISO standards[14] as shown in Table 3.2.
This test was carried out so as to determine whether the ESC intervenes to stabilise the
0
0
10
15
20
25
30
35
vehicle
or not. 5
Time (s)
To visualize the ESC performance to a greater extent, the steering amplitude was
doubled in Test 2. This renders the vehicle highly unstable and shows ESC mitigation
more clearly. Simulation results for Test 2 have been discussed in Section 4.2.
Double Lane Change – ISO 3888-1
It was difficult to recreate the SWD manoeuvre online. Hence, the double lane change
manoeuvre was chosen as both are evasive driving manoeuvres and have certain
similarities. The online simulations weren’t performed by professional drivers, but it
serves our purpose of seeing ESC intervene when needed.
Figure 3.6 Placing of cones for DLC track[16]
34
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Table 3.3 Dimensions of DLC track[16]
Section
Length
Lane Offset
Width
1
15
-
1.1 * vehicle width + 0.25
2
30
-
-
3
25
3.5
1.2 * vehicle width + 0.25
4
25
-
-
5
15
-
1.3 * vehicle width + 0.25
6
15
-
1.3 * vehicle width + 0.25
The DLC track was setup as shown in Figure 3.6 according to the dimensions specified
in Table 3.3. The dimensions of the cones were set according to ISO standards[16].
According to VTI’s software, the vehicle shape is specified from the external vehicle
model but also in the software itself. However, the vehicle width used to specify the
dimensions of the track is from the external vehicle model. The chosen drivers were
advised to keep an entry velocity, for section 1, as 80 km/h. This was deemed sufficient
to investigate ESC mitigation.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
35
4
Simulation Results
The simulation results were compiled following Table 2.3 and the flowchart in Figure
3.1.
4.1
Obstacle Avoidance
With reference to Section 3.1, the obstacle avoidance scenario was carried out with
multiple drivers. Data logging was not considered necessary as the evaluation was more
subjective than objective. In the original scenario (Section 1.1.5), the test driver is to be
evaluated with respect to his response time and chosen ‘avoidance’ measure. A driver
trainer discusses the ‘ideal’ possible behaviour with the test driver with respect to a
particular scenario and provides feedback for the overall drive.
As this ‘modified’ obstacle avoidance was constructed to give an idea about the braking
and yawing behaviour of the ambulance vehicle model, it influenced the ABS and ESC
model tuning. As mentioned in Section 3.1, positioning of the cones was varied to test
the full brake condition, i.e. vehicle stopping distance & time in the offline mode. With
a cone position of 60m from vehicle, full brake must stop the vehicle around 25m before
the cone for the ideal stopping distance (referring to Table 4.1). However, with the
current ABS tuning, the vehicle stopped 10m before the obstacle.
ESC mitigation was more difficult to investigate as, most of the test drivers chose to
brake rather than swerve away from the obstacle. However, when advised to swerve
instead of brake, the vehicle velocity was too low to see any significant intervention.
Moreover, as will be explained in Section 4.4, the single lane change or double lane
changes are influenced by driver skill.
4.2
Straight Line Braking
For the straight line braking test, technical specifications regarding the acceleration time
and stopping distance were comprehended. While parameterizing, an approach was
made to achieve similar values to validate changes made in the vehicle model. The
acceleration/braking specs are as follows Table 4.1 Vehicle Specs
Vehicle
Active Safety
Systems
Time (0-100
km/h)
Stopping Details
(100-0 km/h)
Volvo S40 2L
ABS, EBD, EBA,
9.5s
37m in 3.5s
(2007)
DSTC
Mercedes Sprinter
3L
Adaptive ESC
(2013)
(ABS, EBD, BAS,
ASR)
(from specs)
10.3s
34.3m in 4s
(from track testing)
It must be noted that exact values may not be achievable owing to the various systems
co-interacting with each other in an actual car.
36
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Figure 4.1 Longitudinal Force Coefficient as a function of longitudinal slip[4]
𝐿𝑜𝑛𝑔𝑖𝑡𝑢𝑑𝑖𝑛𝑎𝑙 𝐹𝑜𝑟𝑐𝑒 𝐶𝑜𝑒𝑓𝑓, 𝜇𝑥 =
𝐹𝑥
𝐹𝑧
(4.1)
Where,
μsb = Sliding value of longitudinal force coefficient
μpb = Peak value of longitudinal force coefficient
Figure 4.1 illustrates the non-linear relationship of the longitudinal force with slip. For
the full brake condition, as the traction approaches its peak value, the force decreases
and the wheel tends to lock (quickly). This reduction of tractive force, besides causing
slipping (state of combined slip), also effects the handling and stability of the vehicle.
Hence, the load transfer from the rear axle to the front axle can be computed by[3],
Δ𝐹𝑧 =
ℎ𝐶𝑂𝐺
𝑙
𝐹𝐿 , (𝑎𝑡 𝑙𝑜𝑤 𝑠𝑝𝑒𝑒𝑑𝑠)
𝐹𝑧1 = 𝐹𝑧1,𝑠𝑡𝑎𝑡𝑖𝑐 + Δ𝐹𝑧 , 𝐹𝑧2 = 𝐹𝑧2,𝑠𝑡𝑎𝑡𝑖𝑐 − Δ𝐹𝑧
(4.2)
(4.3)
Where,
FL = Longitudinal force corresponding to inertial force at braking
During braking, the suspension prevents the load transfer from the rear to the front from
being too rapid and thus when the vehicle begins to brake, the vertical loads are the
same as those at constant speed.
Due to the dynamic load transfer, the cornering stiffness’s & peak side forces for the
front and rear change, increasing at the front, reducing at the rear. Locking of the rear
wheels causes the vehicle to become unstable (extreme oversteer – fish tail) and locking
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
37
of the front wheels makes the vehicle uncontrollable (extreme understeer), i.e. travel on
a straight trajectory.
To establish a relationship between the braking moments for the front and rear wheels,
a ratio is defined[4]
𝐾𝑏 =
𝑀𝑏1
𝑀𝑏2
(4.4)
Hence, the total braking force acting on the vehicle when the wheels lock[4],
When rear wheels lock,
𝐹𝑥1 + 𝐹𝑥2 = 𝐹𝑥2 (1 + 𝐾𝑏 )
(4.5)
When front wheels lock,
𝐹𝑥1 + 𝐹𝑥2 = 𝐹𝑥1 (1 +
1
)
𝐾𝑏
(4.6)
It is highly important for the ABS to intervene on all four wheels to prevent such a
condition.
Volvo S40 2L (2007)
As mentioned before, a Volvo S40 was used as the base vehicle to test the functioning
of the ABS.
Table 4.2 ABS Simulation Settings (S40)
ABS Settings
Values
Desired Slip
-0.15
Backlash (Deadband width)
0.0001
Controller Gain (I)
6
Controller Gain (P)
15
The ABS system was designed such that when the controller detects the longitudinal
tire slip to be nearing the desired value, the ABS kicks in and mitigates the brake torque
till the tire slip reaches a lower value. It mitigates to confine the wheel slip to remain
within a narrow range around the slip value. A backlash setting was added to prevent
the ABS controller from acting too much, too fast.
The following plots display the extent at which the ABS controls the tire slip and
prevents the wheels to lock.
38
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Figure 4.2 Vehicle Speed (km/h) vs Time(s) (S40)
The acceleration (0-100km/h) time during simulations was calculated to be 13.5s. As
mentioned in Section 3.2.1, a delay time of 0.5s was considered between full throttle to
full brake conditions.
Figure 4.2 displays the braking situation with ABS on/off. The ABS intervention causes
the stopping duration to reduce by 2 seconds moving it closer to the original specs.
350
320
300
280
260
X-Position(m)
240
200
150
100
50
with ABS
without ABS
0
0
2
4
6
8
10
Time (s)
12
14
16
17
18
19
20
Figure 4.3 X-position (m) vs Time(s) (S40)
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
39
15
15
10
10
5
2
4
6
8
10
Time (s)
12
14
16
Speed(m/s)
30
18
20
15
15
10
10
5
5
5
0
20
0
25
30
Vx
Vx RR 25
20
20
15
15
10
10
5
0
0
2
4
6
8
10
Time (s)
12
14
16
18
0
2
4
6
8
10
Time (s)
12
14
16
30
18
0
20
25
30
Vx
Vx RR 25
20
20
15
15
10
10
5
5
5
0
20
0
Speed(m/s)
0
20
Speed(m/s)
20
Wheel Velocity(m/s)
20
0
25
30
Vx
Vx LF 25
Wheel Velocity(m/s)
Speed(m/s)
25
30
0
2
4
6
8
10
Time (s)
12
14
16
18
Wheel Velocity(m/s)
Without ABS/ESC
30
Vx
Vx LF 25
Wheel Velocity(m/s)
With ABS/ESC
30
0
20
Figure 4.4 Vehicle Velocity (m/s) & Wheel Velocity (m/s) vs Time(s) – LF & RR (S40)
This reduction of 2 seconds causes a difference of 30m in the stopping distance, as
shown in Figure 4.3
Figure 4.4 show the velocity comparison between the vehicle and wheel against time.
As is the case with a vehicle without ABS, on a full brake condition, the wheel
approaches locking condition at increasingly negative longitudinal slip values and
hence begins sliding for the duration the vehicle takes to stop. In the case of the S40,
for the off condition of ABS, the wheels stops rotating almost within a second of full
brake and slide for the remaining duration. This can be seen for all 4 wheels.
Analysis
Table 4.3 Maximum Brake Torque per axle
Vehicle
Axle
Max. Brake Torque (Nm)
Volvo S40
Front
2070
Volvo S40
Rear
1035
With reference to Table 4.1 & 4.4, for the situation when ABS is turned on, the technical
and simulation results are similar to one another. However, on comparing the on/off
ABS simulation results, the large difference in stopping distances can be attributed to a
rigid tire model.
40
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Table 4.4 Simulation Stopping Distance & Duration (S40)
Vehicle
ABS
Stopping Distance Simulation
Stopping
Duration
Volvo S40
Off
70 m
5s
Volvo S40
On
40 m
3s
In the case of active ABS, its influence can be seen clearly in Figure 4.4. With ABS,
the brake torque to the wheels is fluctuated with reference to the max brake torque and
the range of longitudinal slip around the desired value. Hence, from these results, it can
be implied that the ABS is functional and its influence can be tested on the ambulances.
Mercedes Sprinter (2013)
In accordance with the flowchart shown in Figure 3.1, as the ABS was deemed
functional, the simulations were carried out for the rear wheel drive Mercedes Sprinter.
Before moving to online simulations, parameter tuning for the ABS was carried out
after repeated offline tests. A comparison of longitudinal behaviour between offline and
online behaviour was carried out.
Table 4.5 ABS Simulation Settings (Sprinter)
ABS Settings
Values
Desired Slip
-0.15
Backlash (Deadband width)
0.05
Controller Gain (I)
2
Controller Gain (P)
10
Table 4.5 displays the ABS settings used for both offline and online simulations for the
Mercedes Sprinter. The typical cycle frequency for ABS control is close to 10Hz and
this was considered a benchmark for parameter tuning.
The general specification mentioned in Table 4.1 was considered as benchmark for
braking tests with the Mercedes Sprinter.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
41
25
30
Vx
Vx LF 25
20
20
20
20
15
15
15
15
10
10
10
10
5
5
5
5
16
16.5
17
Time (s)
17.5
18
Speed(m/s)
30
18.5
0
19
25
30
Vx
Vx RR 25
20
20
15
15
10
10
5
5
0
15
15.5
16
16.5
17
Time (s)
17.5
18
18.5
0
21.5
22
22.5
23
23.5
Time (s)
24
30
0
19
0
25
24.5
25
30
Vx
Vx RR 25
20
20
15
15
10
10
5
5
0
21.5
22
22.5
23
23.5
Time (s)
24
Wheel Velocity(m/s)
15.5
Speed(m/s)
0
15
Speed(m/s)
25
Vx
Vx LF 25
Wheel Velocity(m/s)
30
Wheel Velocity(m/s)
ONLINE
30
Wheel Velocity(m/s)
Speed(m/s)
OFFLINE
30
0
25
24.5
Figure 4.5 Vehicle Velocity (m/s) & Wheel Velocity (m/s) vs Time(s) – LF & RR (with
ABS)
With reference to Figure 4.5, the velocity comparison was made for the Left Front and
Right Rear tires. For both the cases, it appeared that the front wheels stopped rotating
0.5sec before the rear wheels.
An initial dip in the wheel velocity for the front wheels, not so visible in the rear, at full
brake condition, can be attributed to the difference in brake setups between the front
and the rear axles especially with respect to the brake pressure gradient.
OFFLINE
throttle = 0
brake = 0 to 1
Tire longitudinal slip
0
-0.1
Vx = x to 0
-0.2
Left Front
Right Front
-0.3
Left Rear
Vx (wheels) = x to 0
Right Rear
-0.4
15
15.3
15.5
16
16.5
17
Time (s)
17.5
18
18.5
19
ONLINE
throttle = 0
brake = 0 to 1
Tire longitudinal slip
0
-0.1
Vx = x to 0
-0.2
Left Front
Right Front
Left Rear
Right Rear
-0.3
Vx (wheels) = x to 0
-0.4
21.5
22
22.5
23
23.5
24
Time (s)
Figure 4.6 Tire Longitudinal Slip vs Time(s) (with ABS)
42
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
24.5
25
As stated earlier, the typical cycle frequency for ABS control is close to 10Hz, similar
for all 4 wheels. However, in Figure 4.6, for both the offline and online cases, the cycle
frequency is different for the front and rear axles.
The cycle frequency for the front axle is close to 8-9Hz whereas at the rear, it is
approximately 3-4Hz. A further tuning of the ABS controller is needed to achieve
realistic values.
Figure 4.7 Longitudinal Tire Force (N) vs Longitudinal Slip – LF & RR (with ABS)
With reference to Figure 4.1 showing a plot of the longitudinal force coefficient (μx) vs
longitudinal slip (κ), from the view point of handling, if the wheels were to lock, locking
of the rear wheels must be avoided as it triggers directional instability i.e.,
𝜇𝑥2 < 𝜇𝑥1
(4.7)
Where,
μx1 = Longitudinal slip coefficient for front wheels (LF, RF)
μx2 = Longitudinal slip coefficient for rear wheels (LR, RR)
In the case of the ambulances, the longitudinal slip is higher at the rear which implies
that the rear wheels brake more than required and the braking capacity of the front
wheels is under exploited[4]. This can be attributed to unavailability of actual brake
system dimensions to compute accurate brake torques. However, this may be achieved
by further tuning.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
43
Table 4.6 Maximum Brake Torque per axle
Vehicle
Axle
Max. Brake Torque (Nm)
Mercedes Sprinter
Front
4300
Mercedes Sprinter
Rear
2150
Analysis
Table 4.7 Simulation Stopping Distance & Duration – Sprinter
Vehicle
ABS
Stopping Distance –
Simulation
Stopping
Duration
Mercedes Sprinter
Off
58 m
4.3 s
Mercedes Sprinter
On
42 m
3.3 s
Mercedes Sprinter
(Online)
On
50 m
3.7 s
With reference to Table 4.6 & 4.7 and comparison with Table 4.1, besides the influence
of ABS, it is evident that there is a big difference in the results obtained between offline
and online simulations. Contributing factors to this difference are attributed, not only
to the rigid tire model but also to human delay.
In the case of the offline simulations, the delay mentioned of in Section 3.2.1 is to do
with the estimated time taken by the ‘driver’ to switch between the two pedals (throttle
& brake). However, the time taken from brake pedal position = 0 to 100 is not taken
into account. But in the case of online simulations, this factor along with HW delay
(pedal sensors) causes the difference in the simulation results.
It is evident that the ABS model requires more tuning to achieve closer results but, with
reference to research question 2(b) & 3 in Section 1.1.2, the ABS model does survive
parameterisation and is reasonably active in the online mode.
4.3
Sine with Dwell Test - Offline
With reference to Section 3.2.3, the SWD tests were performed with different
amplitudes. However after studying the data from Test 1 (Steering Amplitude 90
degrees), the intervention from the ESC system is not very distinguishable hence the
results from Test 2 are discussed in the subsequent sections. Also, it was necessary to
add tuning factor to the model as the braking interventions weren’t in the correct range
for ESC interventions.
SWD Plots for the base vehicle Volvo S40 are added as Appendix B.
44
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Table 4.8 ESC settings for Mercedes VitoXL
ESC Settings
Values
Cornering Stiffness (Front Axle)
60000 N/rad
Cornering Stiffness (Rear Axle)
90000 N/rad
Velocity Threshold
10 km/h
Yaw Rate Threshold
5 deg/s
Controller Gain (P)
5000
Controller Gain (D)
0
The above settings have been chosen after multiple simulations with witness the most
‘visible’ ESC intervention.
Mercedes VitoXL (2013)
Driver Input
Steering wheel angle (deg)
200
100
X: 11.44
Y: 0.2262
X: 10.72
Y: 0
X: 12.65
Y: 0
0
-100
X: 11.79
Y: -180
-200
10
10.5
11
11.5
X: 12.3
Y: -180
12
Time (s)
12.5
13
13.5
14
X (m) vs Time (s)
240
220
X (m)
200
180
X: 10.72
Y: 142.6
160
X: 11.44
Y: 158.1
140
120
10
10.5
11
11.5
X: 11.79
Y: 165.2
12
X: 12.23
Y: 174
X: 12.65
Y: 182
12.5
Time (s)
13
13.5
14
14.5
15
Figure 4.8 Steering Input & Path Plots – Volvo S40 with ESC
With reference to Figure 4.8, the combination of 2 plots provides an indication of the
time taken and position coordinates of the vehicle with respect to the steering
manoeuvre. A set of 5 points (Table 4.8) have been chosen on the steering angle vs time
plot to distinguish the path points and witness the ESC interventions.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
45
Figure 4.9 Vehicle Behaviour for different paths
To break down the possible ESC interventions, Figure 4.9 was constructed. For the
SWD manoeuver, the positions 1, 2 and 3 are relevant. As emphasis is based on the
oversteer interventions by the ESC, oversteer during the manoeuver is expected in later
stages.
10
5
X: 158.1
Y: 0.9861
X: 142.6
Y: 0.03884
X: 165.2
Y: 2.239
X: 174
Y: 3.159
Without ESC
X: 182
Y: 2.883
With ESC
Y (m)
0
Oversteer ESC Mitigation
-5
-10
-15
140
150
160
170
180
190
200
210
220
230
X (m)
Figure 4.10 Path Plot – Mercedes VitoXL with/without ESC
A path plot (Figure 4.10) was constructed for the manoeuvre with/without ESC enabled.
For the simulation with ESC enabled, the brake torque values are shown in Table 4.11.
A considerable difference in the final paths is visible after oversteer intervention takes
place.
46
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Table 4.9 Steer and Path Points for SWD steer – Mercedes VitoXL with ESC
Steer Point Time (s)
X (m)
Y(m)
10.72
142.6
0.038
11.44
158.1
0.986
11.79
165.2
2.239
12.23
174
3.159
12.65
182
2.888
With ESC
1200
Left Front
Right Front
Left Rear
Right Rear
1000
Brake Torque(Nm)
800
600
400
200
0
10
10.5
11
11.5
Time (s)
12
12.5
13
Figure 4.11 Brake Torque (Nm) vs Time (s) – Mercedes VitoXL with ESC
As shown in Figure 4.11, the ESC interventions occur at different time stamps during
the entire manoeuver. By referring to Table 4.8, Figure 4.8 & Figure 4.10, it is easy to
distinguish when the ESC intervenes and how the vehicle path alters due to these
interventions. As emphasis is laid on oversteer intervention, the time interval between
12 – 12.8s is studied.
The stoppage of brake torque for an interval between 12.35 & 12.45s is considered an
anomaly as the ESC intervention must mitigate consistently. One of the reasons for this
behaviour could be that the plots for reference and actual velocity follow the same
trajectory during that short interval, which means ESC does not initialize.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
47
Left Front
Right Front
0.4
Without ESC
With ESC
0.2
Tire lateral slip(rad)
Tire lateral slip(rad)
0.4
0
-0.2
-0.4
-0.6
10
Without ESC
With ESC
0.2
0
-0.2
-0.4
10.5
11
11.5
12
12.5
Time (s)
13
13.5
14
14.5
-0.6
10
15
10.5
11
11.5
12
Left Rear
13.5
14
14.5
15
0.2
Without ESC
With ESC
0.1
Tire lateral slip(rad)
Tire lateral slip(rad)
13
Right Rear
0.2
0
-0.1
-0.2
-0.3
10
12.5
Time (s)
Without ESC
With ESC
0.1
0
-0.1
-0.2
10.5
11
11.5
12
12.5
Time (s)
13
13.5
14
14.5
-0.3
10
15
10.5
11
11.5
12
12.5
Time (s)
13
13.5
14
14.5
15
Figure 4.12 Lateral slip angle (rad) vs Time (s) – LF, RF, LR, RR Tires
15
Without ESC
With ESC
X: 12.3
Y: 12.29
X: 12.65
Y: 9.571
10
Vehicle body slip angle (deg)
SWA = -180 to 0 deg
To the Left
5
SWA = 0 to 125 deg
To the Left
0
X: 11.8
Y: -0.8609
-5
SWA = 180 to -180 deg
To the Right
-10
10
10.5
11
11.5
Dwell Period
12
Time (s)
12.5
13
13.5
14
Figure 4.13 Vehicle body slip angle (deg) vs Time (s) – with/without ESC
During that interval, according to the steering manoeuver, the vehicle exits the ‘dwell’
zone. According to Figure 4.9, slip is expected at the rear wheels due to which the
reference velocity is higher than the actual wheel velocity. Condition 3 is applicable
and it is visible in the path plots that the vehicle without ESC oversteers more. Hence,
the LF and LR wheels are braked for the interval which leads to path correction and the
vehicle achieves a more stable path (Figure 4.10).
48
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
With reference to Pacejka[3], at low speeds the vehicle slip is negative for right-hand
turns. As the slip angles become sufficiently large, the vehicle slip changes into positive
values beyond a certain speed.
Considering the above reference, for a left turn when the slip angles are not sufficiently
large, Figure 4.13 shows a positive body slip angle but as the tire slip angles increase,
the body slip changes to negative values. As the vehicle manoeuvers to the left at the
beginning of the SWD, a small peak is visible in Figure 4.13 in correlation with the
above statement.
Also, considering the oversteer mitigation zone, a considerable difference in the tire
and body slip angles is witnessed supplementing the path correction and stable
behaviour discussed in the previous paragraphs.
Conclusion can be drawn that ESC intervenes during oversteer in the dwell period and
reduces the side slip. With reference to research question 2(b) in Section 1.1.2, ESC
system survives parameterization and intervenes when expected.
4.4
DLC manoeuvre – Online
Figure 4.14 DLC path and key positions
Figure 4.14 illustrates the key positions on the DLC track where understeer or oversteer
behaviour is expected. Position no 3 (driver turns right, rear axle ‘kicks’ out) was
evaluated as vehicle oversteer zone (see Figure 4.9).
With reference to Section 3.2.3 explaining the dimensions of the DLC track, it is evident
that the manoeuvre is complicated and heavily influenced by driver skills[4]. As it is a
defined path, subjective evaluation may relay more information than objective
measurements. Professional drivers’ were not used for this simulation so results may
be less indicative of vehicle behaviour than driver skills.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
49
10
Without ESC
Y(m)
5
With ESC
0
-5
-10
740
760
780
800
820
840
860
880
X(m)
Figure 4.15 DLC path with/without ESC (VitoXL)
With ESC
X: 37.77
Y: 1.121
1.5
1
SWA(rad)
X: 42.72
Y: 0.104
X: 35.99
Y: 0
0.5
0
X: 39.45
Y: -0.586
X: 41.38
Y: 0.012
-0.5
X: 40.85
Y: -1.52
-1
-1.5
-2
35
36
37
38
39
40
41
42
43
44
Time (s)
900
X(m)
850
800
X: 35.99
Y: 719.2
750
X: 40.85
Y: 809
X: 39.45
Y: 787
X: 37.77
Y: 758.1
X: 42.72
Y: 842.8
X: 41.38
Y: 817.9
700
650
35
36
37
38
39
40
41
42
43
44
Time (s)
Figure 4.16 Steering Wheel Angle & X-position vs Time (VitoXL)
Figure 4.15 displays the path plotted by a test driver driving the DLC track with/without
ESC. With reference to Figure 4.15 and 4.16, it can be estimated that the vehicle path
has been altered during the time interval of 41-43 sec which correlates with position 3
in Figure 4.14. However, due to the rigidity of the logging files in the vsim12 project,
brake torque is not logged from the system, hence it was impossible to deduce if the
ESC intervened during those intervals or not.
The plots displayed in Appendix C show evidence of ESC intervention but it cannot be
clearly stated if ESC enabled the test driver to manage a more stable path through the
track.
For this reason, plots contemplated during this manoeuvre have not been discussed
further.
50
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Analysis
As mentioned in ISO 3888-1[16], repeatability of tests with increasing speeds would
provide an outlook about the maximum attainable entry speed without hitting any of
the cones. However, when using available drivers, it was impossible to achieve a
consistent clean manoeuvre. It was difficult for regular drivers to provide a ‘clean’ lap
without knocking over the cones. Even when the entry velocity was reduced, one or the
other cones would always get knocked over as the lack of perception of vehicle width,
visually, made it impossible for the driver to ‘get his bearings’ while driving and adjust
his path accordingly.
Lastly, as the input (steering) is not directly comparable (two different runs), it is
impossible to maintain consistency.
With reference to research questions 2(b) in Section 1.1.5, it cannot be perceived, in the
present state, that the ESC survived parameterization in the online mode.
With respect to research question 3 in Section 1.1.5, the desktop simulator, in its present
state, is not considered realistic enough to perceive ESC mitigation.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
51
5
Conclusions & Future Work
5.1
Conclusions
The Chalmers Desktop simulator attempts to bridge the gap between higher fidelity
simulators with motion platforms and offline simulations. A fundamental base has been
constructed for further development and can be an exciting tool for simulation and
scenario testing.
As mentioned earlier, this kind of simulator can be multi-purpose depending on the
user. For the ASTAZero project, it is useful as a driver training & risk management tool
for ambulance drivers with emphasis on driver behaviour than vehicle behaviour.
However, for vehicle model testing, this simulator can be seen as an additional step
towards model development and establishing modularity.
In this thesis, a vehicle model is integrated with the software provided by VTI and an
attempt is made to explore the modularity of the vehicle model. The vehicle model was
parameterized, to a certain extent, to the ambulance vehicles and the general driver
feedback was good. A method of scaling with respect to base vehicle (Volvo S40) was
adapted to parameterize certain parameters requested by the vehicle model. The
parameterisation was verified versus simple specification data, but not against detailed
test data.
ABS and ESC systems were integrated into the vehicle model with limited tuning which
make them functional but not optimal. Simulation results verify the functions of the
ABS and ESC systems but could not validated from track test data.
Regarding the research questions (Section 1.1.5) this thesis has tackled, conclusion can
be drawn that the ABS & ESC systems display functionality when fed into the model
and simulator. ABS activates during online simulations but ESC intervention couldn’t
be perceived (research question 1).
Scaling of vehicle parameters with a few parameters was successfully carried out and
reasonable vehicle behaviour could be extracted (research question 2(a)). However,
being an interesting solution to lack of actual parameters, it may not be ideal.
ABS and ESC systems survived parameterization in the offline mode as they both
intervened when necessary but for the online mode, only ABS can be considered active
(research question 2(b)).
In the overall sense of a driver simulator, this thesis can state that the simulator is
realistic enough to comprehend difference with/without ABS but not realistic enough
for ESC activation.
Finally, care must be taken while developing this simulator in the future as it is not
intended to be realistic but flexible. Certain solutions may increase realism but care
must be taken such that it doesn’t lose flexibility. Additions like manual transmission,
cruise control system, etc. can be seen as potential improvements.
52
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
5.2
Future Work
1. Design traction control, EBD, Torque Converter & Power steering systems
2. ABS & ESC functioning for different friction surfaces.
3. Graphical Interface needed to limit access to VTI SW.
4. Pitch and roll motions to be implemented in driver display to increase level of
realism.
5. Implement front hood as visual in desktop simulator for better perception of
vehicles boundaries.
6. Remove the dependence on xPC target for real time communication,
irrespective of how model executable is generated (Simulink Coder, FMU
toolbox, etc).
7. Establish a coordinate translator between vehicle model (in ISO8855) and
environment model.
8. The vehicle model is not as robust as perceived earlier, it tends to crash when
steering too vigorously in certain situations.
Note – Some deliverables (3, 4, and 6) may have been achieved. Refer to future
ASTAZero SIM documentation[8]
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
53
6
References
[1] Santoro M. (2014): Development of a Parameterized Passenger Vehicle Model
for Longitudinal Dynamics for a Desktop Driving Simulator. Master’s Thesis.
Department of Signals & Systems, Chalmers University of Technology,
2014:08, Göteborg, Sweden, 2014, 22-37 pp.
[2] Morando A. (2014): Development and improvement of the Chalmers’ driving
simulator. Master’s Thesis. Department of Signals & Systems, Chalmers
University of Technology, EX006/2014, ISSN 99-2747920-4, Göteborg,
Sweden, 2014.
[3] Pacejka H. (2006): Tyre & Vehicle Dynamics. Elsevier, Jordan Hill, Oxford. 1441 pp
[4] Genta. G., and Morello. L (2009): The Automotive Chassis Volume 2: System
Design. Springer Science+Business Media B.V – 2009. 14-31 pp
[5] Mathworks (2014) : Simulink Real Time
[6] TNO Automotive (2008) : MF-Tyre & MF-Swift 6.1 User Manual 2008, TNO
Automotive, The Netheralands. 23-24 pp
[7] Benito G. & Nilsson H., (2006) : Vehicle Stability Control for Roadside
Departure Incidents by Steering Wheel Torque Superposition. Master’s Thesis.
Department of Applied Mechanics & Signals and Systems, Chalmers University
of Technology, EX020/2006) Göteborg, Sweden 2006, 30 pp.
[8] [ONLINE]: http://www.astazero.com/the-test-site/about/
[9] Olstam J.J (2005): A model for simulation and generation of surrounding
vehicles in driving simulators. Department of Science & Technology,
Linköpings universitet, LIU-TEK-LIC 2005:58, ISBN 91-85457-51-5, ISSN
0280-7971, Norrköping, Sweden, 2005.
[10] Liu A. & Chang S. (1995): Force Feedback in a Stationary Driving Simulator.
IEEE 0-7803-2559-1/95, Nissan Cambridge Basic Research, Cambridge, MA
02142, 1995
[11] Nagiri S., Doi S., Matsushima S. & Asano K. (1994): Generating method of
steering reaction torque on driving simulator. SSDI 0389-4304(93)E0010-C,
JSAE Review 15(1994) 73-86, Toyota Central Research and Development
Laboratories Inc, Aichi, Japan 480-11, 1994
[12] van Putten BJS. (2008): Design of an Electronic Stability Program for vehicle
simulation software. Master Traineeship. Department of Mechanical
Engineering, Automotive Engineering Science, Eindhoven University of
Technology, DCT 2008. 138, Eindhoven, the Netherlands, 2008.
[13] Kinjawadekar T.S. (2009): Model-based Design of Electronic Stability Control
System for Passenger Cars Using CarSim and Matlab-Simulink. Master’s
Thesis. Department of Mechanical Engineering, The Ohio State University,
USA, 2009.
54
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
[14] National Highway Traffic Safety Administration (2011): Laboratory Test
Procedure for FMVSS 126, Electronic Stability Control Systems, Office of
Vehicle Safety Compliance, Mail Code: NVS – 220, Washington DC, 2011
[15] International Organization for Standardization (2012): Passenger cars –
Steady-state circular driving behaviour – Open-loop test methods, ISO
4138:2012(E), Switzerland, 2012.
[16] Swedish Institute for Standards (1999): Passenger cars – Test track for a
severe lane-change manoeuvre – Part 1: Double lane-change, SS-ISO 3888:1,
Stockholm, Sweden, 1999.
[17] International Organization for Standardization (2011): Road vehicles – Lateral
transient response test methods – Open-loop test methods, ISO 7401:2011(E),
Switzerland, 2011.
[18] Swedish Institute for Standards (2006): Road vehicles – Vehicle dynamics test
methods – Part 1: General conditions for passenger cars, SS-ISO 150371:2006, Stockholm, Sweden, 2006.
[19] Swedish Institute for Standards (2011): Road vehicles – Vehicle dynamics and
road-holding ability - Vocabulary, SS-ISO 8855:2011, Stockholm, Sweden,
2011.
[20] Abou-Zeid M., Kaysi I. & Al-Naghi H. (2011): Measuring Aggressive Driving
Behaviour Using a Driving Simulator: An Exploratory Study. 3rd International
Conference on Road Safety and Simulation, American University of Beirut,
Indianapolis, USA, 2011.
[21] Swedish Institute for Standards (2007): Passenger cars – Stopping Distance at
straight line braking with ABS – Open-loop test method, SS-ISO 21994:2007,
Stockholm, Sweden, 2007.
[22] Lükert P., Busenthür D., Arndt S. & Sass H. (2013): The Mercedes-Benz OM
651 Four Cylinder Diesel Engine for Worldwide Use. 22nd Aachen Colloquium
Automobile and Engine Technology, Daimler AG, Stuttgart, Germany, 2013.
[23] International Organization for Standardization (2011): Road Vehicles – Vehicle
Dynamics and road-holding ability - Vocabulary, ISO 8855:2011, IDT,
Switzerland, 2011.
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
55
Appendix A – Vehicle Model I/O
Sender
Receiver Bus Name
Signal Name
Description
vsim 12
VDM
watchdog
Linux watchdog counter
vsim 12
VDM
resetIn
Signal to reset the model to
original state
vsim 12
VDM
Fxyz_ext_cg
External Forces at centre of
gravity (Fx, Fy,Fz) (N)
vsim 12
VDM
Mxyz_ext_cg
External torque at centre of
gravity (Mx,My,Mz) (N-m)
vsim 12
vsim 12
vsim 12
vsim 12
vsim 12
VDM
VDM
VDM
VDM
VDM
SWA
gear_manual
clutch_pedal
throttle
brake_pedal
vsim 12
VDM
P_brk_whls
vsim 12
vsim 12
VDM
VDM
z_dzdx_dzdy
mu
vsim12
VDM
Vx_max
vsim12
VDM
auto_gear
Steering wheel angle (rad)
Gear (1-12), 0 = neutral
(0-1)
(0-1)
Pressure 0-inf or pos 0-100
Brake Pressure [LF,RF,LR,RR]
(Pa)
4 wheels*[z,dzdx,dzdy] (m)
Friction coeff. For 4 wheels
Max. Longitudinal Velocity
(m/s)
Automatic Gear Flag
VDM
VDM
VDM
VDM /
Driveline
VDM /
Driveline
VDM /
Steer
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
vsim 12
vsim 12
vsim 12
xPC watchdog
watchdog
IDNR
Linux watchdog counter
ID number
vsim 12
w_eng
Engine Speed (rad/s)
vsim 12
Tq_eng
Engine Torque (N-m)
vsim 12
Tq_SW
Steering Wheel Torque (N-m)
Vx
Longitudinal Velocity at C.G.
(m/s)
(psi_dot*lf) +
Vy
Lateral Velocity at C.G. (m/s)
Zcg_dot
Vertical Velocity of C.G. (m/s)
phi_dot
Roll Velocity at C.G. (rad/s)
teta_dot
Pitch Velocity at C.G. (rad/s)
psi_dot
Yaw Velocity at C.G. (rad/s)
56
vsim 12
vsim 12
vsim 12
vsim 12
vsim 12
vsim 12
vsim 12
vsim 12
simrefFront
Sensor
simrefFront
Sensor
simrefFront
Sensor
simrefFront
Sensor
simrefFront
Sensor
simrefFront
Sensor
simrefFront
Sensor
simrefFront
Sensor
ax
(psi_2dot*lf) +
ay
Longitudinal Acceleration at
C.G. (m/s)
Lateral Acceleration at C.G.
(m/s)
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
vsim 12
vsim 12
vsim 12
vsim 12
vsim 12
vsim 12
vsim 12
simrefFront
Sensor
simrefFront
Sensor
simrefFront
Sensor
simrefFront
Sensor
simrefFront
Sensor
simrefFront
Sensor
simrefFront
Sensor
Zcg_2dot
phi_2dot
teta_2dot
psi_2dot
Vertical Acceleration of C.G
(m/s^2)
Roll Acceleration at C.G.
(rad/s^2)
Pitch Acceleration at C.G.
(rad/s^2)
Yaw Acceleration at C.G.
(rad/s^2)
phi
Roll Angle (rad)
teta
Pitch Angle (rad)
psi
Yaw Angle (rad)
Longitudinal Tire Force in Body
Coordinate system
(LF,RF,LR,RR) (N)
Lateral Tire Force in Body
Coordinate system
(LF,RF,LR,RR) (N)
Vertical Force (LF,RF,LR,RR)
(N)
Aligning Torque (LF,RF,LR,RR)
(N-m)
Longitudinal Tire Slip
(LF,RF,LR,RR)
Lateral Tire Slip (LF,RF,LR,RR)
(rad)
Wheel Velocity (LF,RF,LR,RR)
(rad/s)
Longitudinal Velocity at C.G.
(m/s)
VDM /
Wheels
vsim 12
log_vector
Fx_body
VDM /
Wheels
vsim 12
log_vector
Fy_body
vsim 12
log_vector
Fz
vsim 12
log_vector
Mz
vsim 12
log_vector
LongSlip
vsim 12
log_vector
LatSlip
vsim 12
log_vector
w_whl
vsim 12
CGSensor
Vx
vsim 12
CGSensor
Vy
Lateral Velocity at C.G. (m/s)
vsim 12
CGSensor
Zcg_dot
Vertical Velocity of C.G. (m/s)
vsim 12
CGSensor
phi_dot
Roll Velocity at C.G. (rad/s)
vsim 12
CGSensor
teta_dot
Pitch Velocity at C.G. (rad/s)
vsim 12
CGSensor
psi_dot
Yaw Velocity at C.G. (rad/s)
vsim 12
CGSensor
ax
vsim 12
CGSensor
ay
vsim 12
CGSensor
Zcg_2dot
vsim 12
CGSensor
phi_2dot
vsim 12
CGSensor
teta_2dot
vsim 12
CGSensor
psi_2dot
VDM /
Axles
VDM /
Wheels
VDM /
Wheels
VDM /
Wheels
VDM /
Wheels
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Longitudinal Acceleration at
C.G. (m/s)
Lateral Acceleration at C.G.
(m/s)
Vertical Acceleration of C.G
(m/s^2)
Roll Acceleration at C.G.
(rad/s^2)
Pitch Acceleration at C.G.
(rad/s^2)
Yaw Acceleration at C.G.
(rad/s^2)
57
VDM /
Chassis
VDM /
Chassis
VDM /
Chassis
VDM /
Steer
VDM /
Chassis
58
vsim 12
CGSensor
phi
Roll Angle (rad)
vsim 12
CGSensor
teta
Pitch Angle (rad)
vsim 12
CGSensor
psi
Yaw Angle (rad)
vsim 12
front_wheel_a
ngle
0.5(delta_lf+delta_rf)
vsim 12
Z_cab
[Zcg - {1/4(z_dzdx_dzdy_LF+
z_dzdx_dzdy_RF+z_dzdx_dzdy_
LR+z_dzdx_dzdy_RR)}]
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Appendix B – SWD Plots Volvo S40
1. Steering Input & Path Plots with ESC
Steering wheel angle (deg)
Steering Input
200
X: 11.43
Y: 4.184
X: 10.72
Y: 0
100
X: 12.66
Y: 0
0
X: 11.79
Y: -180
-100
-200
10
10.5
11
11.5
X: 12.3
Y: -180
12
Time (s)
Y vs X
12.5
13
13.5
14
Y (m)
5
0
-5
150
160
170
180
190
200
210
220
230
240
X (m)
X vs Time
X (m)
300
200
100
0
0
5
10
15
Time (s)
2. Brake Torque (Nm) vs Time (s) with ESC
With ESC
1200
1000
Left Front
Right Front
Left Rear
Right Rear
Brake Torque(Nm)
800
600
400
200
0
10.5
11
11.5
12
12.5
13
Time (s)
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
59
3. Path Plot with/without ESC
10
Without ESC
5
X: 158
Y: 0.009143
X: 193.2
Y: 4.202
X: 182.9
Y: 2.955
X: 175.1
Y: 1.241
X: 199.8
Y: 3.954
With ESC
0
Y (m)
Oversteer ESC Mitigation
-5
-10
-15
-20
150
160
170
180
190
200
210
220
230
240
X (m)
4. Lateral slip angle (rad) vs Time (s) – LF, RF, LR, RR Tires
Left Front
Right Front
0.2
Without ESC
With ESC
Tire lateral slip(rad)
Tire lateral slip(rad)
0.2
0
-0.2
-0.4
10.5
11
11.5
12
12.5
13
Time (s)
13.5
14
14.5
Without ESC
With ESC
0
-0.2
-0.4
10.5
15
11
11.5
12
Left Rear
Tire lateral slip(rad)
Tire lateral slip(rad)
14
14.5
15
0.2
Without ESC
With ESC
0
-0.2
60
13.5
Right Rear
0.2
-0.4
10.5
12.5
13
Time (s)
11
11.5
12
12.5
13
Time (s)
13.5
14
14.5
15
Without ESC
With ESC
0
-0.2
-0.4
10.5
11
11.5
12
12.5
13
Time (s)
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
13.5
14
14.5
15
5. Vehicle body slip angle (deg) vs Time (s) – with/without ESC
15
Without ESC
With ESC
Vehicle body slip angle (deg)
10
5
0
-5
-10
10
10.5
11
11.5
12
12.5
Time (s)
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
13
13.5
14
14.5
15
61
Appendix C – DLC Plots Mercedes VitoXL
1. Lateral Velocity (m/s) & Acceleration (m/s2) vs Time (s) with/without ESC
Lateral Velocity (km/h)
2
Without ESC
With ESC
1
0
-1
-2
-3
35
44
43
42
41
40
39
38
37
36
Time (s)
Lateral Acceleration (m/s 2)
6
Without ESC
With ESC
4
2
0
-2
-4
35
44
43
42
41
40
39
38
37
36
Time (s)
2. Yaw angle (rad) & velocity (rad/s) and Roll angle (rad) & velocity (rad/s) vs
Time (s) with/without ESC
0.06
0.15
Without ESC
With ESC
Without ESC
With ESC
0.1
Yaw Angle(rad)
Roll Angle(rad)
0.04
0.02
0
0.05
0
-0.05
-0.1
-0.02
-0.15
-0.04
35
36
37
38
39
40
41
Time (s)
42
43
44
-0.2
35
45
0.15
37
38
39
40
41
Time (s)
42
45
Yaw Rate (rad/s))
0
0.1
0
-0.1
-0.2
-0.05
Without ESC
With ESC
-0.3
62
44
0.2
0.05
-0.1
35
43
0.3
Without ESC
With ESC
0.1
Roll Rate(rad/s)
36
36
37
38
39
40
41
Time (s)
42
43
44
45
-0.4
35
36
37
38
39
40
41
Time (s)
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
42
43
44
45
3. Tire Lateral Slip (rad) vs Time (s) with/without ESC – LF, RF, LR, RR
Without ESC
Tire Lateral slip (rad)
0.1
Left Front
Right Front
Left Rear
Right Rear
0.05
0
-0.05
-0.1
35
36
37
38
39
40
Time (s)
41
42
43
44
45
With ESC
0.06
Left Front
Right Front
Left Rear
Right Rear
Tire Lateral slip (rad)
0.04
0.02
0
-0.02
-0.04
-0.06
35
36
37
38
39
40
Time (s)
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
41
42
43
44
45
63
Appendix D - Vocabulary (ISO - 8855)[23]
Reference Frame
Geometric Environment in which all points remain fixed with respect to each other at
all times.
Axis System
Set of three orthogonal directions associated with X, Y & Z axes.
Vehicle Axis System
Axis system fixed in the reference frame of the vehicle sprung mass, so that the Xvehicle
axis is substantially horizontal and forwards (with the vehicle at rest), and is parallel to
the vehicle's longitudinal plane of symmetry, and the Yvehicle axis is perpendicular to the
vehicle's longitudinal plane of symmetry and points to the left with the Zvehicle axis
pointing upward.
Figure - Slip angles for a single track two-axle model[23]
The following angles are shown positive, vehicle side slip angle, β, front steer angle, δf
and side slip angle at the front axle, βf.
The following angles are shown negative, front axle slip angle, αf, rear steer angle, δr,
side slip angle at the rear axle, βf and rear axle slip angle, αr.
64
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Appendix E – Vehicle Parameters
Vehicle Parameters
Maximum
Speed
Coefficient of
Friction(road)
Unit
Mercedes
Sprinter 319
BlueTec
Panel
Low Roof
Mercedes Vito 116
CDI
Extra Long 4*4
Vx_max
[m/s]
161/3.6
174/3.6
mu
[]
0.750
0.750
Aerodynamic Data
Projected
Frontal Area
Air Density
Drag
Coefficient
A0
[m2]
4.400
4.4
rho
[kg/m2]
1.225
1.225
Cax
[]
0.370
0.37
Mass, Inertia, Dimension
Vehicle Mass
m
[kg]
3500
2800
Unsprung
Mass,Front
mus_f
[kg]
226.247
181
mus_r
[kg]
188.792
151.03
Iz
[kgm2]
6007.954
4806.36
Iy
[kgm2]
5061.461
4049.16
Ix
[kgm2]
1619.667
1295.73
h_CG
[m]
0.679
0.679
Unsprung Mass
CG height
hus_f
[m]
0.774
0.619
Unsprung Mass
CG height
hus_r
[m]
0.774
0.619
wb
[m]
3.665
3.43
lf
[m]
1.523
1.425
tw_f
[m]
1.710
1.63
tw_r
[m]
1.716
1.63
Unsprung
Mass,Rear
Moment of
Inertia about Z
axis
Pitch moment of
inertia around
C.G
Moment of
Inertia around
roll axis
Centre of
Gravity height
Wheel base
Front Axle
distance from
CG
Track width
Front
Track width
Rear
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
65
Wheel radius
Wheel rotational
moment of
Inertia
Tire Stiffness
Tire Damping
Tire Lateral
Stiffness
Rolling
Resistance
Coefficient
Tire Relaxation
length
Toe-in, Front
Toe-in, Rear
Static Camber
Angle, Front
right
Static Camber
Angle, Rear
Right
Roll Camber
Coefficient,
Front
Roll Camber
Coefficient,
Rear
Rw
Wheel
[m]
0.300
0.3
Iw
[kgm2]
0.820
0.82
Ktire
Ctire
[N/m]
[N/m]
379609.544
506.146
303687.6356
404.9168474
Ky_tire
[N/m]
202458.424
161966.739
fr
[]
0.009
0.0094
Sigma
[]
0.001
0.003
0.000872665
0.002617994
toe_f
toe_r
Toe, Camber
[rad]
[rad]
gama0_f
[rad]
-0.004
-0.004014257
gama0_r
[rad]
-0.017
-0.017104227
C_gama_phi_f
[]
0.788
0.788
C_gama_phi_r
[]
0.718
0.718
Coefficient for
camber due to
lateral force,
Front
C_gama_Fy_f
[rad/N]
0.000
4.76475E-06
Coefficient for
camber due to
lateral force,
Rear
C_gama_Fy_r
[rad/N]
0.000
8.02851E-06
Suspension
Spring
Coefficient at
wheel position,
per side, Front
Kspr_f
[N/m]
50614.606
40491.68474
Damping
Coefficient at
wheel position,
per side, Front
Cdamp_f
[Ns/m]
11388.286
9110.629067
66
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
Anti-Roll bar
stiffness, Front
Spring
Coefficient at
wheel position,
per side, Rear
Karb_f
[Nm/rad]
85098.337
68078.67
Kspr_r
[N/m]
60737.527
48590.02
Damping
Coefficient at
wheel position,
per side, Rear
Cdamp_r
[Ns/m]
7592.191
6073.75
Anti Roll bar
stiffness, Rear
Karb_r
[Nm/rad]
13746.927
10997.54
Roll axle height,
Front
hr_f
[m]
0.087
0.086
Roll axle height,
Rear
hr_r
[m]
0.097
0.097
Steering system
Steering Gear
Ratio
King pin
inclination
King pin offset
(roll steer
radius)
Caster Angle
Caster offset
Suspension
Compliance for
Lateral Force,
Front
Suspension
compliance for
Lateral Force,
Rear
Suspension
Torsional
Compliance,
Rear,
Roll steer
coefficient,
Front
Roll steer
coefficient, Rear
SG_ratio
[]
23.571
22.06
teta_kp
[rad]
0.239
0.238
d_kp
[m]
-0.0007
-0.0007
teta_cst
d_cst
[rad]
[m]
0.045
0.006
0.045
0.006
C_delta_Fy_f
[rad/N]
-1.73835E-06
-1.73835E-06
C_delta_Fy_r
[rad/N]
3.83972E-07
3.83972E-07
C_delta_Mz_r
[rad/Nm]
0.000023
0.000023
C_delta_phi_f
[rad/rad]
-0.107
-0.107
C_delta_phi_r
[rad/rad]
-0.013
-0.0125
Servo Steering system
Torsion bar
stiffness
Ktb
[Nm/rad]
141.570
132.492
Piston Area
Ap
[m2]
0.001
0.000804
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
67
Rack Ratio =
rack linear
motion per turn
of steering
wheel
Steering wheel
torque below
which there will
be no servo
pressure
Maximum
Pressure
nr
[m/turn]
0.050
0.05
T0
[Nm]
1.000
1
Pmax
[bar]
90.000
90
Brake
Brake torqueline
press.grad.Left
Front
Brake torqueline
press.grad.Right
Front
C_brk_lf
[Nm/Pa]
0.00021
0.000207
C_brk_rf
[Nm/Pa]
0.00021
0.000207
Brake torqueline
press.grad.Left
Rear
C_brk_lr
[Nm/Pa]
0.00021
0.0000495
Brake torqueline
press.grad.Right
Rear
C_brk_rr
[Nm/Pa]
0.00021
0.0000495
1.006
0.94
0.2
0.2
Driveline
Drive-shaft
moment of
inertia per side
Idrv
[kgm2]
Engine
Engine
Rotational
moment of
inertia
Ieng
[kgm2]
Engine Throttle
eng_throttle
[%]
Engine Speed
eng_speed
[rad/s]
Engine Torque
Engine Idle
speed
eng_torque
[Nm]
[0 2.5 9 12
18 21 25
30 35 40
50 100]
[0 105 130
145 190 235
280 325 370
400 415 440]
-
w_eng_idle
[rad/s]
100
68
[0 2.5 9 12 18
21 25 30 35 40
50 100]
[0 105 157 209 262
314 367 419 471
524 681]
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
100
Maximum
engine speed
w_eng_max
[rad/s]
630
712
Gearbox
Gear ratios
Speed for
shifting up the
gear, automatic
gearbox
Speed for
shifting down
the gear,
automatic
gearbox
End gear ratio
gear_ratio
[]
[5.076 2.610
1.518 1.0
0.791 0.675]
[3.595 2.186 1.405
1.0 0.831]
gear_up_Vx
[m/s]
[1.25 9.0
17.0 21.5
25.5]
[7.0 13.0 21.0
26.5]
gear_down_V
x
[m/s]
[1 5 14
19.5 24.5]
[6.5 12.5 19.5
25.5]
endgear_ratio
[]
3.923
3.273
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
69
Appendix F – Logged Variables
70
S.NO
DATA FIELD
DESCRIPTION
UNIT
1
timer
Absolute simulator time since
program start
s
2
odometer
Distance driven
m
3
road_id
Current road id
-
4
s
longitudinal position, s in Track
system
m
5
r
lateral position, r in Track system
m
6
yaw
Yaw angle relative to road tangent
rad
7
vx
Body fixed longitudinal velocity
m/s
8
vy
Body fixed lateral velocity
m/s
9
ax
Body fixed longitudinal
acceleration
m/s2
10
ay
Body fixed lateral acceleration
m/s2
11
yaw_vel
Yaw velocity
rad/s
12
eng_torq
Engine torque
Nm
13
engine_rps
Engine revolution
rad/s
14
throttle
Throttle position, 0 no throttle
-
15
brake_pedal_active
Brake pedal active
-
16
brake_pedal_press
Brake pedal pressure
kPa
17
brake_force
18
stw_angle
19
stw_torq
Steering wheel torque
Nm
20
left_indicator
Left indicator, 1 = active
-
Approx. brake force applied to
pedal
Steering wheel angle, CCW
positive
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
N
rad
21
right_indicator
Right indicator, 1 = active
-
22
23
24
25
26
27
28
gear
event_id
event_state
event_state_timer
X
Y
Data Field
watchdog
s
m
m
Unit
-
29
resetIn
30-32
33-35
36
37
38
39
40
41-52
53-56
57-60
61
62
63
64
65
66
67
68
Fxyz_ext_cg
Mxyz_ext_cg
SWA
gear_manual
Clutch
throttle
brake_pedal_input
z_dzdx_dzdy
mu
P_brk_wheels
Vx_max
auto_gear
xPC watchdog
watchdog
IDNR
w_eng
Tq_eng
Tq_SW
Gear number
Active event's id (see below)
Current state in active event
Time spent in current state
Global X coordinate
Global Y coordinate
Description
Linux watchdog counter
Signal to reset the model to
original state
External Forces at centre of gravity
External torque at centre of gravity
Steering wheel angle
Gear (1-12), 0 = neutral
(0-1)
(0-1)
Pressure 0-inf or pos 0-100
4 wheels*[z,dzdx,dzdy]
Friction coeff. For 4 wheels
Brake Pressure [LF,RF,LR,RR]
Max. Longitudinal Velocity
Automatic Gear Flag
69
Vx
70
71
72
73
74
75
76
77
78
79
80
(psi_dot*lf) + Vy
Zcg_dot
phi_dot
teta_dot
psi_dot
ax
(psi_2dot*lf) + ay
Zcg_2dot
phi_2dot
teta_2dot
psi_2dot
N
Nm
rad
%
%
%
m
Pa
m/s
-
Linux watchdog counter
ID Number
Engine Speed
Engine Torque
Steering Wheel Torque
Vehicle Model longitudinal
velocity
Vehicle Model Lateral velocity
Vertical Velocity of COG
Roll Velocity at COG
Pitch Velocity at COG
Yaw Velocity at COG
Longitudinal Acceleration
Lateral Acceleration
Vertical Acceleration of COG
Roll Acceleration at COG
Pitch Acceleration at COG
Yaw Acceleration at COG
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
rad/s
Nm
Nm
m/s
m/s
m/s
rad/s
rad/s
rad/s
m/s^2
m/s^2
m/s^2
rad/s^2
rad/s^2
rad/s^2
71
81
82
83
phi
teta
psi
84-87
Fx_body
88-91
Fy_body
92-95
Fz
96-99
Mz
100-103
LongSlip
104-107
108-111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129-138
LatSlip
w_whl
Zero Vector
Vx
Vy
Zcg_dot
phi_dot
teta_dot
psi_dot
ax
ay
Zcg_2dot
phi_2dot
teta_2dot
psi_2dot
phi
teta
psi
front_wheel_angle
Z_cab
72
Roll Angle at COG
Pitch Angle at COG
Yaw Angle at COG
Longitudinal Tire Force in body
coordiante system[LF,RF,LR,RR]
Lateral Tire Force in body
coordinate system [LF,RF,LR,RR ]
Vertical Tire Force
[LF,RF,LR,RR]
Tire Aligning Torque
[LF,RF,LR,RR]
Tire Longitudinal Slip
[LF,RF,LR,RR]
Tire Lateral Slip[LF,RF,LR,RR]
Wheel Velocity
Longitudinal Velocity
Lateral Velocity
Vertical Velocity of COG
Roll Velocity at COG
Pitch Velocity at COG
Yaw Velocity at COG
Longitudinal Acceleration
Lateral Acceleration
Vertical Acceleration of COG
Roll Acceleration at COG
Pitch Acceleration at COG
Yaw Acceleration at COG
Roll Angle at COG
Pitch Angle at COG
Yaw Angle at COG
0.5(delta_lf+delta_rf)
CHALMERS, Applied Mechanics, Master’s Thesis 2014:06
rad
rad
rad
N
N
N
Nm
rad
rad/s
m/s
m/s
m/s
rad/s
rad/s
rad/s
m/s^2
m/s^2
m/s^2
rad/s^2
rad/s^2
rad/s^2
rad
rad
rad
rad
m
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertisement