international automotive research centre electrical projects
DESCRIPTION
International Automotive Research Centre ELECTRICAL PROJECTS. Ross McMurran - Project Manager Peter Jones- Principal Investigator Mark Amor-Segan – Principal Engineer Gunny Dhadyalla – Principal Engineer. Function Growth. Processor. Actuator. Sensor. Software. Lane-keeping. - PowerPoint PPT PresentationTRANSCRIPT
© 2007 University of
Warwick
International Automotive Research CentreInternational Automotive Research Centre
ELECTRICAL PROJECTSELECTRICAL PROJECTS
Ross McMurran - Project Manager Ross McMurran - Project Manager Peter Jones- Principal InvestigatorPeter Jones- Principal InvestigatorMark Amor-Segan – Principal EngineerMark Amor-Segan – Principal EngineerGunny Dhadyalla – Principal EngineerGunny Dhadyalla – Principal Engineer
2Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
2000 2010
PTC HeaterTelematics
Satellite Radio
ACC
Adaptive Headlamps
Blind Spot Detection
IVDC
Active steering
EM Valves
ISG
Remote Diagnostics
Keyless Vehicle
E-Connectivity
Brake-by-Wire
El. Water Pump
In Car PC
Fuel Cell
Rear Multi-media
Auto lights
SurroundSound
Voice Activation
Optical Buses
Auto wipers
Steer-by-Wire
Lane-keeping
FunctionGrowth
International Automotive Research Centre:International Automotive Research Centre:Motivation behind Electrical Projects Motivation behind Electrical Projects
The vast majority of new technology looks like this…..
SensorProcessor
ActuatorSoftware
CY1980
ABS
InstrumentsBody Elec.
Engine Control Transmission Control
1990
Airbag
SecurityAdv.
Restraints
ESP EPAS
Adaptive suspension
Navigation
Typical Premium Architecture (Current Generation)
ECU
Bus
Typical Premium Architecture (Current Generation)
ECU
Bus
3Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
Automotive Electronics Complexity IssuesAutomotive Electronics Complexity Issues
Key Key Areas of Areas of ResearchResearch
As “Systems of Systems” become more complex it becomes harder to:
Specify and implement what is required
Predict behaviour (Emergent properties)
Verify complete SoS or sub-systems in isolation
Plan delivery and manage change
Diagnose faults
Maintain delivery skills at pace of technology evolution
Typical Premium Architecture (Current Generation)
ECU
Bus
Typical Premium Architecture (Current Generation)
ECU
Bus
© 2007 University of
Warwick
PARD1 PROJECTS:PARD1 PROJECTS:Electrical Test for Advanced ArchitecturesElectrical Test for Advanced ArchitecturesSoftware IntegrationSoftware IntegrationHMI Assessment MethodologyHMI Assessment MethodologyEnvironmental Condition RecognitionEnvironmental Condition Recognition
Status: Completed Feb 07Status: Completed Feb 07
5Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
PARD1 Completed Project:PARD1 Completed Project:Electrical Test for Advanced ArchitecturesElectrical Test for Advanced Architectures
Manufacturing Test Validation with HIL
Model Based Diagnostics
system
model
inputs outputs
model of normal system
model of unknown system
compare
fault detection & diagnosis
Bayesian Diagnostics
Process Mapping & RADS
EBS FunctionOwner
ArchitectureTeam
EBS Model Developer (Typically Function
Owner) EBS SupplierEBS Vital TeamModel
Reviewer
Determine what models are RequiredWhat I/O of models is required
New Project
Activity
Role that is involved in an interaction
Role that drives an interaction
SupplierRole
OEMRoleKEY:
Trigger, either event or time based
Develop Core Stateflow Modelsusing Generic I/O not application specific
Develop High Level Requirements(May be contained directly in model)
Communicate Functional Requirements
DevelopSAL Spec.
Develop I/O ModelApplication specific I/Oto core model
Release Models & SAL to Supplier
Stage 1 Model Review (I/O)
Stage 2 Model Review (Structure)
Core Models Complete
Test Core Model Functionality
Stage 1 Model Review (I/O)
Stage 2 Model Review (Structure)
Stage 3 Model Review (Complete)Post M1 DJ
Proveout Test
Automated DV
Autocode from models
Handcode low level code from SAL
Integrate to ECU & Platform software
Release ECU
Release SAL (Signal Abstraction Layer) Specification to VITAL Team
Construct SAL Model in Simulink
Release of Models to VITAL Team
Proveout Test Review
Integrate SAL Model to VITAL platform
Basic Interactive TestPower mode etc.
Model Update
OKNot OKAutomated DV Test Review
Model Based Testing
OKNot OK
Proveout Test
Automated DV
Proveout Test Review
Basic Interactive TestPower mode etc.
NOT OKSupplier OK
Not OKModel
Automated DV Test Review
ComponentBased Testing
OKNOT OKSupplier
Not OKModel
Supplier Update
Communicate I/O Requirements
Validation Activity
6Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
PARD1 Completed Project:PARD1 Completed Project: Software Integration ProjectSoftware Integration Project
Agile Development MethodsCurrent Process - Sequential (V-Model)
Requirements
Specification
Coding
Integration
Testing
Potential Agile Process - Iterative Feature Driven
RequirementsFeature
List
Coding
Integration
Testing
Next iteration
Feature List
2-6 wks
Cycle
Completion
Current Process - Sequential (V-Model)
Requirements
Specification
Coding
Integration
Testing
Potential Agile Process - Iterative Feature Driven
RequirementsFeature
List
Coding
Integration
Testing
Next iteration
Feature List
2-6 wks
Cycle
Completion
Ris
k
V-Model
Time
AgileRis
k
V-Model
Time
Agile
Formal Verification Methods Requirements
DesignCorrect?
Requirements Design
Correct?
Development Iteration Timeline
1 324
0 7 14 21 28
Planning Meeting
Development
Integration / Test
Weekly update
days
Development Iteration Timeline
1 324
0 7 14 21 28
Planning Meeting
Development
Integration / Test
Weekly update
days
SysML for Improved Requirements
Intelligent Software Planning
8Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
PARD1 Completed Project:PARD1 Completed Project:Environmental Condition RecognitionEnvironmental Condition Recognition
Recognising environmental conditions to enable adaptive control and feature enhancement
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
A B C D F G H I J K L M N O P Q R S T U V W X Y Z AA AB AC AD AE AF AG AH AI AJ AK AL AM AN AO AP AQ AR AS
Vis
ual
- c
olo
ur
Vis
ual
- C
on
tras
t
Vis
ual
- R
efle
ctiv
ity
Vis
ual
- P
atte
rn
Vis
ual
- L
igh
t In
ten
sity
Vis
ual
- v
isib
ility
(fo
g)
Su
rfac
e ad
hes
ion
Au
dib
le -
vo
lum
e
Au
dib
le -
fre
qu
ency
Au
dib
le
Pat
tern
/ch
arac
teri
stic
Tem
per
atu
re
Hu
mid
ity
Sm
ell
Veh
icle
acc
elle
rati
on
s/
torq
ues
/fo
rces
Pro
xim
ity
Wei
gh
t
Air
pre
ssu
re
Fic
tio
n
Dep
th (
wat
er)
Flo
w R
ate
(Wat
er)
Air
sp
eed
Air
dir
ecti
on
Gro
un
d C
lear
ance
Car
tog
rap
hic
dat
a
Pre
dic
tive
Dat
a
His
tori
c d
ata
Tyr
e d
efle
ctio
n
Wet
nes
s
Sp
eed
To
wb
ar/t
ow
bal
l fo
rces
To
tal s
core
To
tal H
igh
co
rrel
atio
n
To
tal M
ediu
m
corr
elat
ion
To
tal l
ow
co
rrel
atio
n
…
Rain r g g g ? g g 24 1 5 0
swamp g g b g b g b b r r 34 2 4 4
Wet roads r g g r b ? g r r g 49 4 4 1
Snow r r r g r b r ? ? g b g r r g 77 7 4 2
Ice r r b r g r r g 52 5 2 1
Gravel Road b b b r r r b g 34 3 1 4
Rough Tracks g g b r g r b g 32 2 4 2
Wet Grass r g g g b b b r g r b r 52 4 4 4
Mud g g r b g r g g r g 46 3 6 1
Deep Soft Sand g r r b b r r r 50 5 1 2
Boulders r b r b r r g g 44 4 2 2
Water (wading) b r g b b r r r b r r g r r b 83 8 2 5
Ruts r b g r g b 26 2 2 2
Inclines g r r 21 2 1 0
Towing r b r 19 2 0 1
Vehicle Loads & Distribution r b r b r b r r 48 5 0 3
Fog b r b g r r r b g 45 4 2 3
Light Intensity- darkness g g r g g g 24 1 5 0
Light Intensity- brightness (sunlight) b r b r 20 2 0 2
Snow falling g r g r g r ? ? r 45 4 3 0
Wind speed b b b r b 13 1 0 4
Wind direction b r b 11 1 0 2
Humidity r b b b 12 1 0 3
Altitude b r 10 1 0 1
Barometric Pressure r b b 11 1 0 2
Absolute position r 9 1 0 0
Speed over ground g g r b r 25 2 2 1
Temperature r r r 27 3 0 0
Pitch r r g g g b 28 2 3 1
Heave b r r g g 25 2 2 1
Roll b r r g g 25 2 2 1
Longitudinal accell r r r r 36 4 0 0
Lateral Acelleration r g g g 18 1 3 0
Yaw b r b b 12 1 0 3
Surface type g g g r r g g g r 45 3 6 0
Road Geometry g r 12 1 1 0
Traffic Environment Sensing- Blind spot/parking r r r 27 3 0 0
Relative position 0 0 0 0
Road class g r 12 1 1 0
Tyre condition (pressure/wear) b b b r 12 1 0 3
Tyre type b g g 7 0 2 1
Air quality b b r b g 15 1 1 3
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
… 0 0 0 0
… 0 0 0 0
… 0 0 0 0
… 0 0 0 0
… 0 0 0 0
0 0 0 0
Attribute cue
Total score is calculated by rating high correlation=9, medium correlation=3low correlation=1 and summing the total scores
dhadya_g:These are the total number of incidences of high rating
dhadya_g:These are the total number of incidences of medium rating
dhadya_g:These are the total number of incidences of low rating
dhadya_g:Check to see if there has been any research in to measuring wetness, may be also tyre
dhadya_g:Check to see if there is any data that supports aire pressure as an indication fo particular weather conditions.
dhadya_g:Pattern recognition could be looking back at the pattern the wheels have left behind analysing
dhadya_g:Interesting to see if there are different reflectivity characteristics between
dhadya_g:Monitor rapid change in temperature (thermal shock)
dhadya_g:Characteristics of submerged ultrasonic sensor (parking aid) could lead to info. Also if one or more sensor is submerged gives confidence level.
dhadya_g:Does wading change weigth of car (buoyancy)?
dhadya_g:Use ground clearance to surface as indication of submersion.
dhadya_g:Roof rack wind noise. Internal acoustic characteristic changes
dhadya_g:Monitor turbulance
Environmental Condition vs. Attribute Cue
Sen
sor
Sys
tem
1:
Gen
eral
CA
ME
RA
Sen
sor
Sys
tem
2:
CA
ME
RA
att
ach
ed
wit
h w
hee
l
Sen
sor
Sys
tem
3:
INF
RA
RE
D S
EN
SO
R
at f
ron
t
Sen
sor
Sys
tem
4:
INF
RA
RE
D S
EN
SO
R
at b
ack
Sen
sor
Sys
tem
11:
F
og
Sen
sor
Sen
sor
Sys
tem
10:
R
ain
Sen
sor
Sen
sor
Sys
tem
7:
Gen
eral
Tem
per
atu
re
Sen
sor
Sen
sor
Sys
tem
8:
IN
FR
A R
ED
T
emp
erat
ure
Sen
sor
Sen
sor
Sys
tem
9:
Hu
mid
ity
Sen
sor
Sen
sor
Sys
tem
6:
Inte
gra
ted
W
EA
TH
ER
ST
AT
ION
Sen
sor
Sys
tem
5:
INF
RA
RE
D S
EN
SO
R 3
Sen
sor
Sys
tem
12:
M
OS
Bas
ed G
as
Sen
sor
high relevance Cost
medium relevanceSuitability to automotive
low relevance Availability
Visual - colour
Visual - Contrast
Visual - IntensityVisual -
Reflectivity
Visual - PatternVisual - Light
IntensityVisual - visibility
(fog)
Surface adhesion
Audible - volumeAudible - frequencyAudible
Pattern/characteristic
Temperature
Humidity
SmellVehicle accellerations/ torques/forces
Proximity
Sensor Technology
Att
rib
ute
Cu
e
dhadya_g:Check to see if there has been any research in to measuring wetness, may be also tyre
Attribute Cue vs.
Sensor Technology
Mapping Control Application Against Environmental Condition to identify feature enhancements
Function Ice Gravel Road Rough Tracks Wet Grass Mud Deep Soft Sand Boulders Water (wading) Ruts Articulation Inclines Towing
Terrain Optimisation
ACC Automatic Cruise ControlSpeed ConrolHeadway Control
AFS Adaptive Front lighting
Lighting modeBeam direction (cornering)Auto headlamp levelling
PAMShort range proximity detection
TCM (Traction Control Module)Traction control through brake and throttle intervention
ECM (Engine Control Module)
Engine Control through engine torqueVehicle speed control (e.g. boulder crawl)
AudioEntertainment audio volume control etc.
ABS Brake control
AIR_SUS (Air SUSpension controller)Ride heightLoad levelling
DLCT (Drive Line Controller) Transfer caseTransfer box - Hi-Lo ratio control
DLCR (Drive Line Controller – Centre / Rear differential)
Centre differential controlRear differential control
TCU (Transmission Control Unit) Gear selection
EMS (Engine Management System)Combustion managementEmission control
SCS (Slip Control System )Is this just ABS or different?
IPK (Instrument PacK)Driver informationWarnings
Co
ntr
ol
Ap
pli
ca
tio
n /
Sys
tem
Environmental Condition
Environmental Conditionvs.
Control Application
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
A B C D F G H I J K L M N O P Q R S T U V W X Y Z AA AB AC AD AE AF AG AH AI AJ AK AL AM AN AO AP AQ AR AS
Vis
ual
- c
olo
ur
Vis
ual
- C
on
tras
t
Vis
ual
- R
efle
ctiv
ity
Vis
ual
- P
atte
rn
Vis
ual
- L
igh
t In
ten
sity
Vis
ual
- v
isib
ility
(fo
g)
Su
rfac
e ad
hes
ion
Au
dib
le -
vo
lum
e
Au
dib
le -
fre
qu
ency
Au
dib
le
Pat
tern
/ch
arac
teri
stic
Tem
per
atu
re
Hu
mid
ity
Sm
ell
Veh
icle
acc
elle
rati
on
s/
torq
ues
/fo
rces
Pro
xim
ity
Wei
gh
t
Air
pre
ssu
re
Fic
tio
n
Dep
th (
wat
er)
Flo
w R
ate
(Wat
er)
Air
sp
eed
Air
dir
ecti
on
Gro
un
d C
lear
ance
Car
tog
rap
hic
dat
a
Pre
dic
tive
Dat
a
His
tori
c d
ata
Tyr
e d
efle
ctio
n
Wet
nes
s
Sp
eed
To
wb
ar/t
ow
bal
l fo
rces
To
tal s
core
To
tal H
igh
co
rrel
atio
n
To
tal M
ediu
m
corr
elat
ion
To
tal l
ow
co
rrel
atio
n
…
Rain r g g g ? g g 24 1 5 0
swamp g g b g b g b b r r 34 2 4 4
Wet roads r g g r b ? g r r g 49 4 4 1
Snow r r r g r b r ? ? g b g r r g 77 7 4 2
Ice r r b r g r r g 52 5 2 1
Gravel Road b b b r r r b g 34 3 1 4
Rough Tracks g g b r g r b g 32 2 4 2
Wet Grass r g g g b b b r g r b r 52 4 4 4
Mud g g r b g r g g r g 46 3 6 1
Deep Soft Sand g r r b b r r r 50 5 1 2
Boulders r b r b r r g g 44 4 2 2
Water (wading) b r g b b r r r b r r g r r b 83 8 2 5
Ruts r b g r g b 26 2 2 2
Inclines g r r 21 2 1 0
Towing r b r 19 2 0 1
Vehicle Loads & Distribution r b r b r b r r 48 5 0 3
Fog b r b g r r r b g 45 4 2 3
Light Intensity- darkness g g r g g g 24 1 5 0
Light Intensity- brightness (sunlight) b r b r 20 2 0 2
Snow falling g r g r g r ? ? r 45 4 3 0
Wind speed b b b r b 13 1 0 4
Wind direction b r b 11 1 0 2
Humidity r b b b 12 1 0 3
Altitude b r 10 1 0 1
Barometric Pressure r b b 11 1 0 2
Absolute position r 9 1 0 0
Speed over ground g g r b r 25 2 2 1
Temperature r r r 27 3 0 0
Pitch r r g g g b 28 2 3 1
Heave b r r g g 25 2 2 1
Roll b r r g g 25 2 2 1
Longitudinal accell r r r r 36 4 0 0
Lateral Acelleration r g g g 18 1 3 0
Yaw b r b b 12 1 0 3
Surface type g g g r r g g g r 45 3 6 0
Road Geometry g r 12 1 1 0
Traffic Environment Sensing- Blind spot/parking r r r 27 3 0 0
Relative position 0 0 0 0
Road class g r 12 1 1 0
Tyre condition (pressure/wear) b b b r 12 1 0 3
Tyre type b g g 7 0 2 1
Air quality b b r b g 15 1 1 3
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
… 0 0 0 0
… 0 0 0 0
… 0 0 0 0
… 0 0 0 0
… 0 0 0 0
0 0 0 0
Attribute cue
Total score is calculated by rating high correlation=9, medium correlation=3low correlation=1 and summing the total scores
dhadya_g:These are the total number of incidences of high rating
dhadya_g:These are the total number of incidences of medium rating
dhadya_g:These are the total number of incidences of low rating
dhadya_g:Check to see if there has been any research in to measuring wetness, may be also tyre
dhadya_g:Check to see if there is any data that supports aire pressure as an indication fo particular weather conditions.
dhadya_g:Pattern recognition could be looking back at the pattern the wheels have left behind analysing
dhadya_g:Interesting to see if there are different reflectivity characteristics between
dhadya_g:Monitor rapid change in temperature (thermal shock)
dhadya_g:Characteristics of submerged ultrasonic sensor (parking aid) could lead to info. Also if one or more sensor is submerged gives confidence level.
dhadya_g:Does wading change weigth of car (buoyancy)?
dhadya_g:Use ground clearance to surface as indication of submersion.
dhadya_g:Roof rack wind noise. Internal acoustic characteristic changes
dhadya_g:Monitor turbulance
Environmental Condition vs. Attribute Cue
Sen
sor
Sys
tem
1:
Gen
eral
CA
ME
RA
Sen
sor
Sys
tem
2:
CA
ME
RA
att
ach
ed
wit
h w
hee
l
Sen
sor
Sys
tem
3:
INF
RA
RE
D S
EN
SO
R
at f
ron
t
Sen
sor
Sys
tem
4:
INF
RA
RE
D S
EN
SO
R
at b
ack
Sen
sor
Sys
tem
11:
F
og
Sen
sor
Sen
sor
Sys
tem
10:
R
ain
Sen
sor
Sen
sor
Sys
tem
7:
Gen
eral
Tem
per
atu
re
Sen
sor
Sen
sor
Sys
tem
8:
IN
FR
A R
ED
T
emp
erat
ure
Sen
sor
Sen
sor
Sys
tem
9:
Hu
mid
ity
Sen
sor
Sen
sor
Sys
tem
6:
Inte
gra
ted
W
EA
TH
ER
ST
AT
ION
Sen
sor
Sys
tem
5:
INF
RA
RE
D S
EN
SO
R 3
Sen
sor
Sys
tem
12:
M
OS
Bas
ed G
as
Sen
sor
high relevance Cost
medium relevanceSuitability to automotive
low relevance Availability
Visual - colour
Visual - Contrast
Visual - IntensityVisual -
Reflectivity
Visual - PatternVisual - Light
IntensityVisual - visibility
(fog)
Surface adhesion
Audible - volumeAudible - frequencyAudible
Pattern/characteristic
Temperature
Humidity
SmellVehicle accellerations/ torques/forces
Proximity
Sensor Technology
Att
rib
ute
Cu
e
dhadya_g:Check to see if there has been any research in to measuring wetness, may be also tyre
Attribute Cue vs.
Sensor Technology
Mapping Control Application Against Environmental Condition to identify feature enhancements
Function Ice Gravel Road Rough Tracks Wet Grass Mud Deep Soft Sand Boulders Water (wading) Ruts Articulation Inclines Towing
Terrain Optimisation
ACC Automatic Cruise ControlSpeed ConrolHeadway Control
AFS Adaptive Front lighting
Lighting modeBeam direction (cornering)Auto headlamp levelling
PAMShort range proximity detection
TCM (Traction Control Module)Traction control through brake and throttle intervention
ECM (Engine Control Module)
Engine Control through engine torqueVehicle speed control (e.g. boulder crawl)
AudioEntertainment audio volume control etc.
ABS Brake control
AIR_SUS (Air SUSpension controller)Ride heightLoad levelling
DLCT (Drive Line Controller) Transfer caseTransfer box - Hi-Lo ratio control
DLCR (Drive Line Controller – Centre / Rear differential)
Centre differential controlRear differential control
TCU (Transmission Control Unit) Gear selection
EMS (Engine Management System)Combustion managementEmission control
SCS (Slip Control System )Is this just ABS or different?
IPK (Instrument PacK)Driver informationWarnings
Co
ntr
ol
Ap
pli
ca
tio
n /
Sys
tem
Environmental Condition
Environmental Conditionvs.
Control Application
© 2007 University of
Warwick
PARD ELECTRICAL PROJECT PARD ELECTRICAL PROJECT EXTENSION:EXTENSION:HIL Technology Migration HIL Technology Migration HMI Development Tools IntegrationHMI Development Tools IntegrationElectrical TrainingElectrical TrainingDiagnosticsDiagnosticsSoftware PlanningSoftware Planning
Status: March 07 to March 08Status: March 07 to March 08
10Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
Electrical IMDS Training – BackgroundElectrical IMDS Training – Background
Technical Specialists/Experts
Deep Understanding of particular fields but few in number
Project Engineers/Technical Project Mgrs/Trouble-shooters
Broad understanding of a number of fields
Depth of knowledge
Knowledge Gap
Bre
ad
th o
f k
no
wle
dg
e
Technical Specialists/Experts
Deep Understanding of particular fields but few in number
Tranche 1Automotive NetworkingAutomotive DiagnosticsElectrical Test Techniques
ELECTRICALMODULAR TRAINING
• High level of practical ‘hands-on’ content• Tailored to application context• Subject Matter Experts – for content & lecturing• Post Module Assignment
© 2007 University of
Warwick
EVoCS ProjectEVoCS ProjectEvolutionary Validation of Complex Evolutionary Validation of Complex SystemsSystems
Status: Current 2006-2010Status: Current 2006-2010
12Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
EVoCS ProjectEvolutionary Validation of Complex Systems
THE TECHNOLOGY PROGRAMME
Complex Systems of SystemsComplex Systems of Systems A System of Systems (SoS) is composed of parts which: have individual goals and a level of autonomy are linked to achieve a higher level purpose or to share resources e.g. information, interfaces etc.
As SoS become more complex it becomes harder: to predict behaviour (Emergent properties) to verify complete SoS or sub-systems in isolation
Super Systeme.g. Broadcast, Manufacturing & Service systems, Interfaces withConsumer devices, Intelligent Transportation Systems
System of Systemsi.e. Vehicle Electrical System
Systeme.g. Infotainment System
Sub-Systeme.g. FM Radio
Componente.g. Radio Receiver
To maximise confidence in the design and implementation of complex automotive electrical systems through:
Innovative techniques for the validation of the design at a System of Systems level
A platform for the validation of the implementation at a Systems of Systems level
Typical Premium Architecture (Current Generation)
ECU
Bus
Typical Premium Architecture (Current Generation)
ECU
Bus
42 U
9 U
3 U
3 U
3 U
3 U
3 U
1 U
1 U
3 U
2 U
3 U
3 U
4 U
1 U
Power Distribution Pod
Fan Tray
Genix Pods
Adapter and routing Cards
RT-CPU and IO
ECU Connection Modules
Programmable PSU
3 U
Project ObjectivesProject Objectives
Automated Model Based
Testing
Improvedsub-system Validation
Compositional Rules
e.g. Assumption/commitment
Formal Methods
Architectural Modelling
Static Analysis
Tools
Automated Model Based
Testing
Improvedsub-system Validation
Compositional Rules
e.g. Assumption/commitment
Formal Methods
Architectural Modelling
Static Analysis
Tools
Project PartnersProject PartnersProject ScopeProject Scope
With funding fromWith funding from
For further information contact:[email protected]
13Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
EVoCS - System of Systems Design Validation
Typical Premium Architecture (Current Generation)
ECU
Bus
Typical Premium Architecture (Current Generation)
ECU
Bus
Formal
Methods for Dependability
Static Code Analysis
Tools
Design for Robustness
Interaction Modelling
Model Based Development
Processes
Enhanced Physical
Modelling
Test case generation &
coverage metrics
Automated Model Based
Testing
14Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
Low Voltage Testing
EVoCS - System of Systems Validation Platforms
Flexible HIL Platform
HMI Simulation &
Testing
Machine Vision
Validation of Manufacturing
Systems
Test Automation
Next Generation HIL Tests
Platforms for full vehicle
tests
Robustness Testing
© 2007 University of
Warwick
Self Healing Vehicle ProjectSelf Healing Vehicle Project
Status: Submission to WIMRC Board June 07Status: Submission to WIMRC Board June 07
2008 – 20102008 – 2010
Partner interest soughtPartner interest sought
16Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
Self Healing VehicleSelf Healing VehicleBackgroundBackground
Increasing complexity and criticality of applications
• Despite improvements in validation techniques, faults will still get to market,
• Electronics & software will fail.
Human-assisted monitoring, maintenance, and intervention will become prohibitively costly, unacceptably slow, and sometimes ineffective.
An intelligent vehicle needs to play a more proactive role in fault management
17Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
What is a Self Healing Vehicle?What is a Self Healing Vehicle?
“A vehicle with the ability to:
autonomously predict or detect and diagnose failure conditions,
confirm any given diagnosis,
and perform appropriate corrective intervention(s),
• including the use of telematics to interact with external service providers and infrastructures.”
18Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
Self Healing VehicleSelf Healing VehicleConceptConcept
DistributedVehicleElectronicsSystem
Prognostic DiagnosticMonitor
Intelligent Rectification Manager
InterventionInitiator
Interrogation commands
Confirmed/ClassifiedFailure Information
Failure Details +Recommended Intervention
Corrective Actionor Intervention
Verification of Intervention outcomes
Vehicle Data
RemoteTelematics
SupportSystem
Diagnostic & PrognosticInformation, Data Logging
SW Downloads,Enhancements & Upgrades to diagnostic System, Remotecommands.In-vehicle Fault Management System
19Your Project Title Goes Here …….Your Project Title Goes Here …….
© 2007 University of
Warwick
Areas Of Interest For Future ResearchAreas Of Interest For Future Research
Systems Engineering, Model Driven Development & Validation
Requirements Engineering Modelling Formal Methods Automated Model Based Testing Auto-coding
Advanced Vehicle Control Sensing & Data Processing Vision Systems Robotics & Autonomous Vehicles
Robust and Fault Tolerant Systems Design for robustness Advanced Diagnostics
Telematics Data Processing for new applications, e.g. Driver Support,
Prognostics, PAYD Insurance
© 2007 University of
Warwick
EndEnd