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

March 18 th, 2014 I 18

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

March 18 th, 2014 I 21

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