© 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

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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

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42

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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:ross.mcmurran@warwick.ac.uk

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

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© 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

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© 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.”

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© 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

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© 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

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