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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 …….
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 …….
Recognising environmental conditions to enable adaptive control and feature enhancement
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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.
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
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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.
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
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