CO2Pilot
March 18 th, 2014 I 1
March 18 th, 2014
CO2PilotFrom hybrid vehicles eco-driving to automated driving
May 28th, 2013
15e cycle de conférences : Utilisation rationnelle de l’énergie et environnement
Vanessa Picron
Affordable hybrid
CO2Pilot concept
Market analysis
Agenda
March 18 th, 2014 I 2
CO2Pilot concept
Conclusion
From eco-driving to automated driving
Agenda
Affordable hybrid
CO2Pilot concept
Market analysis
March 18 th, 2014 I 3
CO2Pilot concept
Conclusion
From eco-driving to automated driving
CO2 worldwide market driver
2020 5 l/100 km (117g)
4,5l/100km (106g) (Passenger Cars only )
202554.5 mpg
equivalent NEDC
3.9 l/100 km (93g)
March 18 th, 2014 I 4
China 2020(cars only):106
3.9 l/100 km (93g)
2020�2021 95g CO2/km 4.0 l/100 km
Energy (kJ)Steadystate
Idle AccelDecel
(losses)Decel
(potential regen)
From wheelto powertrain
1345 - 2815 384 1522
Source Valeo - Based on NEDC, gasoline engine on sedan vehicle
Hybridization as a key solution for CO2 reduction
March 18 th, 2014 I 5
Breakdown of decel energy
Air resistance 11%
Brakingenergy49%
Rolling friction 9%
Friction losses9%
Pumping losses22%
From wheel to powertrain 1/4 of energy can be recovered during decelerations
Important lever to reduce CO2 emissions
But with a key issue : cost to benefit ratio
And a high variety of solutions
Hybridization variety of solutions
March 18 th, 2014 I 6
Electric motor on Combustion Engine(Buick LaCrosse)
Electric motor in transmission(Toyota PRIUS)
Electric motor onthe rear-axle(PSA 3008 HY4)
Hybridization market: Worldwide TrendVehicles <6T, Oil barrel $120 2020, Li-Ion Battery 300 €/kWh 2020
Source: 2013 Valeo Powertrain Forecast
Stop-Start
98.2 % IC
E
Growth of
Stop-Start
FULL as niche, MILD
Internal CombustionEngine
March 18 th, 2014 I 7
• BEV/FCEV: only 1.6% in 2023, still a limited market (lower segments), urban usage or image product• EREV: not confirmed• FULL / PHEV: faster growth than in last forecast, g rowing weight of PHEV from 2018 – 2019• MILD: market take off delay, rather in 2018• Stop-Start: getting mainstream with regular growth from now – still 23% CONV, mainly in BRICS
MILD
FULLPHEV BEV
1.8 %T
rend
s
FULL as niche,
then growth
MILD
take-off Emergence
of PHEV
EREV
Hybridization market: Europe TrendVehicles <6T, Oil barrel $120 2020, Li-Ion Battery 300 €/kWh 2020
Internal CombustionEngine
Fast growthEmergence Growth of
Source: 2013 Valeo Powertrain Forecast
Rising importance of PHEV
No real EREV/BEV take off
97.0 % IC
E
March 18 th, 2014 I 8
Stop-Start
MILD
FULL
PHEVEREV BEV
Fast growthof Stop-Start
Emergence of Electric
Growth of MILD / FULL
• BEV/FCEV: lower forecast than in the past (A / B / C + LCV), EREV remaining a niche• FULL / PHEV: growing significance, with higher weig ht of PHEV in sales• MILD: somewhat postponed – take off expected in 2018• Stop-Start: becoming standard within the next 6 yea rs, almost 0% conventional engines in 2023 • Significant Hybrid growth expected before 2020 to r each 95g (expected 103g 2020, 88g 2023)
3.0 %T
rend
s
Affordable hybrid
CO2Pilot concept
Market analysis
Agenda
March 18 th, 2014 I 9
CO2Pilot concept
Conclusion
From eco-driving to automated driving
Target: Improve Hybrid powertrain cost affordability ���� Define the best cost vs. CO2 benefits ratio through
Targets and optimization levers
March 18 th, 2014 I 10
Componentsoptimization
Sizing & technological choices
Standardization
Generic components & 48V network
Implementation
Advanced operation functions
Integration
Location flexibility & low intrusivity
System assessment through simulation
� Energy Management
- Operating modes
- Energy storage
� Fuel consumption
- CO2 saving
Gearbox ICE
EMEDLC
HV
Bus
EMStarter/Alternator
LV Battery
DC/DC
Architecture study (e-Machine location)
Electric motor & battery(Technology, Power, Voltage, Capacity) Supervisor
model
Vehicle & driver model
HEV simulation platform
March 18 th, 2014 I 11
- CO2 saving
- Cost / gCO2
0
20
40
60
80
100
120
140
0 200 400 600 800 1000 1200
TIME (s)
VE
HIC
LE
SP
EE
D (
km/h
)
Mission profile(NEDC, WLTC, Artemis Urban)
Vehicle platform(Engine displacement, segment)
Traction model
Supervision & control
���� Optimized system
Architecture study
Gearbox ICE
HV
Bus
EMStarter/Alternator
LV Battery
DC/DC
MH
2
Electric Motor between engine and gearbox with
Gearbox ICE
EM EDLC
LV Battery
HV
Bus
DC/DC
Electric Motor directly on the crankshaft of the engineM
H1
March 18 th, 2014 I 12
Less intrusive system is with belt-driven machine
EM
EDLC HV
BusM
H2
engine and gearbox with an additional clutch
Gearbox ICE
EM
EDLC HV
Bus
EMStarter/Alternator
LV Battery
DC/DC
Electric Motor behind the gearbox through a disconnect clutch M
H3
Hybrid architecture assessmentSimulation results on NEDC cycle
B segment vehicle with Turbo Gasoline DI engineOptimal control / EDLC battery storage / Optimum si ze for each architecture
CO
2 e
mis
sio
ns
be
ne
fit
(%)
MH1 4kW CP
MH1 6kW CP
MH1 8kW MR
MH1 14kW MR
MH2 8kW MR
MH2 14kW PM
MH1
MH2MH3
March 18 th, 2014 I 13
Best cost to value with a 6-8 kW BSG motor
CO
2 e
mis
sio
ns
be
ne
fit
(%)
OEM on cost wo integration overcost (€)
MH2 14kW PM
MH2 14kW MR
MH2 14kW PM P
MH2 20kW PM P
MH3 8kW MR
MH3 14kW MR
MH3 14kW PM
MH3 20kW PM
CP : Claw Pole
MR : Mixed Rotor
PM : Permanent Magnet
PM P : Permanent Magnet Pancake
Vehicle implementation
BSGe-machine
Engine & PowertrainControl Unit
March 18 th, 2014 I 14
Inverter Energy storageDC/DC converter
Demonstrator ���� BSG implementation on 1.6L Turbo GDI Manual Trans.
48V i-BSG
Operating modes
Extended Stop / Start (even with manual gearbox), coasting
Electric mode: running and take off (even with belt driven system)
Generation mode & regenerative braking
Torque assist / Overboost
Operation modeTorque split
March 18 th, 2014 I 15
Torque
request
Conventional Electric Torque
assist
Generation Overboost
Driver request
Overboost request
Thermal engine
Electric machine
Torque split management
CO2 benefitsSimulation results on NEDC cycle
B segment vehicle with Turbo Gasoline DI engineMH1 architecture / Real time control
March 18 th, 2014 I 16
Additional benefits can be reached using predictive control
Agenda
Affordable hybrid
CO2Pilot concept
Market analysis
March 18 th, 2014 I 17
CO2Pilot concept
Conclusion
From eco-driving to automated driving
Context and principle
Prediction of coming torque demand profile allows o ptimizing energetic control to increase CO2 benefits
Avoid to overflow the battery and waste braking energy
Avoid to underflow the battery and waste EV mode phase
Data fusion of driving assistance pieces (cameras, telematics& GPS) allows anticipating road profile events
Deceleration phases (roundabout, traffic light, intersection...)
Downhill areas
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Downhill areas
“Zero Emission Vehicle” phases (low speed limitation, traffic jam)
Application example:Preconditioning before downhill area:Anticipation of available energy duringregenerative phase.
High SOC
Low SOC
Battery SOC Wasted free
energy
Free energy
area
Pre-
conditioning
Optimal
preconditioning
Conventional
CO2Pilot
Digital Map Embedded Sensors
Road profile prediction - Driving Assistance Data Fusion
Telecommunication
March 18 th, 2014 I 19
At 50mNext 5km
Data fusion in ADAS ECU or front camerato predict oncoming driving profile
from short term / dynamic events and mid/long term areas
Image provided by SpeedVue™ cameraTraffic Sign recognition by camera
Sub signs detection (trucks only etc)
Fusion with GPS location/
Traffic Sign Recognition
March 18 th, 2014 I 20
Fusion with GPS location/ speed limit information
Traffic Sign identification in Navigation database
Situational awareness with line identification
Communication : 802.11p
Use Cases :
Green Light Speed AdvisoryAutomatic regenerative braking system
Car2X – Cooperative Traffic Lights
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Traffic Light data reception
GLOSA speedometerCooperative Traffic Light
Key benefits
Avoid storage saturation during deceleration on MH1 & MH3 (Small energy storage )
Anticipation of areas for electric mode & optimal generation mode on Full & MH3
Driving situations
Deceleration situation
Slopesituation
Extra urban to urban situation
Urban Extra to urbansituation
Mountainsituation
Acceleration situation
Short time situation Long time situation
CO2 assessment
March 18 th, 2014 I 22
Key impacts on data fusion
For low energy storage, high detection precisionand events detection are required
For high energy storage, the detection precision needs decrease from events to areas
Short time situation Long time situation
Detection precision
needs
Energy storage
Prédiction horizon
time
Example on WLTC cycle
(Mild hybrid) (Full hybrid)
CO2 assessment using predictive optimal ctrl
CO2 benefits on homologation cycle using predictive optimal control
Benefits depend on:
CO2 benefits on real usage using predictive optimal control
50
100
Spe
ed (
km/h
)
Cergy Bobigny
- Hybridization architecture- E-Machine power- Stocker size- Cycle
March 18 th, 2014 I 23
Benefits highly depend on mission profile but increase fuel economiesrobustness in real life
0 1000 2000 3000 4000 5000 6000 7000 80000
50
tim e (s )
Spe
ed (
km/h
)
Major benefits with low storage device come from re generative braking optimisation ���� further improvement by automated deceleration
Expected fuel benefitswith full prediction
Real usage
MH1 (belt driven) Up to 3%
MH3 (on axle) Up to 5%
Principle
Automatic intervention on vehicle command to realize eco-driving
Driver acceptance taken into account
Vehicle
speed
Stop
event
Driver
deceleration
Injection
cut-off
CO2Pilot
decelerationOptimal
deceleration start
Injection
cut-off
Deceleration control
with electric machine
Conventional
CO2Pilot
CO2 assessments / eco-driving results
March 18 th, 2014 I 24
Additional fuel benefits by activating automatedregenerative braking through injection cut off cont rol
Real usage
Fuel benefits Up to 10%
Impact on driving time <3%
Roundabout
Automated deceleration
x 4
Affordable hybrid
CO2Pilot concept
Market analysis
Agenda
March 18 th, 2014 I 25
CO2Pilot concept
Conclusion
From eco-driving to automated driving
Towards fuel efficient automated driving
The driver is an important factor on the fuel effic iency
Driver coaching systems are the first step
Full potential will be achieved with automated ener gy efficient vehicle control
Traffic flow anticipation Green wave
March 18 th, 2014 I 26
Green waveOptimized powertrain operation (engine speed & load, cut off, regenerative braking)
Audi (Jan ‘14)Traffic Jam PilotValet Parking
Toyota (Oct ‘13)GM (Apr ‘13) Nissan (Nov ‘13)
Volvo (Nov ‘13)Mixed roads 100 vehicles in 2017
Daimler (Aug ‘13)Berta Benz driveCountry and Urban roads
BMW (since ‘11)Motorway Pilot (with Lane Change)
Ford (Dec ‘13)
Race towards Automated Driving
March 18 th, 2014 I 27
Toyota (Oct ‘13)Motorway Pilot (ACC + Lane Keeping + V2V)
GM (Apr ‘13)Motorway Pilot (ACC + Lane Keeping)
Nissan (Nov ‘13)Motorway Pilot (ACC + Lane Keeping)
Ford (Dec ‘13)“Autopilot capabilities, such as vehicle platooning”
Renault (Feb ‘14)Traffic Jam Pilot(ACC + Lane Keeping)
Images: OEMs
Automated driving
Automated Parking
Low acceptance High acceptance
Automated driving will leverage experience Automated driving will leverage experience from automated parking and low speed controlfrom automated parking and low speed control
to extend to motorway and urban drivingto extend to motorway and urban driving
March 18 th, 2014 I 28
Emergency Braking
Parking
TemporaryAutopilot
Traffic JamPilot
Source: Intuitive Driving workshops 2012
Automated car classification
TH
E C
ON
NE
CT
ED
CA
R
SIMPLEASSISTED
HIGHLY AUTOMATED
the car completely takes over the
the car partially takes over the trajectory tasks
CONDITIONALLY
AUTOMATED
the car completely takes over the
FULLY AUTOMATED
the car completely takes over the
Legal frameworkto be adapted
PARTIALLYAUTOMATED
the car partially takes over the trajectory tasks
March 18 th, 2014 I 29
TH
E C
ON
NE
CT
ED
CA
R
takes over the trajectorytasks (braking, accelerating and braking) for a longer duration in given situations
trajectory tasks (braking, accelerating or steering) under driver supervision
takes over the trajectory tasks (braking, accelerating and steering) for a limited duration in a given situation
LEVEL1 LEVEL 4LEVEL 3
takes over the trajectorytasks (braking, accelerating and braking) for the entire trip
LEVEL5
trajectory tasks (braking, accelerating &/or steering) under driver supervision
LEVEL 2
Intuitive Driving for safe and connected mobility
Intuitive Driving
2
InSync
InTouch
Connected Car
Car2X
Automated CarAutomated Car1
Park4U Remote ®360 Vue®
Lane
March 18 th, 2014 I 30
Intuitive Drivingfor safe and connected mobility while reducing CO2 emissions
3
eSkin
Display, control & connect
IntuitiveControls
Lane keeping
Cocoon detection & fusion,
ego localization,
System & decision,
Automated driving functions
Agenda
Affordable hybrid
CO2Pilot concept
Market analysis
March 18 th, 2014 I 31
CO2Pilot concept
Conclusion
From eco-driving to automated driving
Hybrid powertrain affordability can be improved through
Components optimization & standardization
Advanced operation functions
CO2Pilot & ADAS systems
ADAS systems can anticipate road profile and
System understanding &
optimization
Conclusion
March 18 th, 2014 I 32
���� Cost to CO2 optimization���� New value created through system approach
ADAS systems can anticipate road profile and further improve powertrain supervision
First step fuel economy can be provided through optimized predictive control or driver coaching
Full potential will be achieved with energy efficient automated vehicle
March 18 th, 2014 I 33
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