CO 2 Pilot From hybrid vehicles eco-driving to automated driving Vanessa Picron 15 e cycle de conférences : Utilisation rationnelle de l énergie et environnement March 18 th, 2014 I 1 May 28 th, 2013 March 18 th, 2014
Agenda Market analysis Affordable hybrid CO2Pilot concept From eco-driving to automated driving Conclusion March 18 th, 2014 I 2
Agenda Market analysis Affordable hybrid CO2Pilot concept From eco-driving to automated driving Conclusion March 18 th, 2014 I 3
CO2 worldwide market driver 2020 5 l/100 km (117g) 4,5l/100km (106g) (Passenger Cars only ) China 2020(cars only):106 2025 54.5 mpg equivalent NEDC 3.9 l/100 km (93g) 20202021 95g CO 2 /km 4.0 l/100 km March 18 th, 2014 I 4
Hybridization as a key solution for CO 2 reduction Energy (kj) Steady state Idle Accel Decel (losses) Decel (potential regen) From wheel to powertrain 1345-2815 384 1522 Source Valeo - Based on NEDC, gasoline engine on sedan vehicle 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 Breakdown of decel energy Pumping losses 22% Friction losses 9% Rolling friction 9% Braking energy 49% Air resistance 11% March 18 th, 2014 I 5
Hybridization variety of solutions Electric motor on Combustion Engine (Buick LaCrosse) Electric motor in transmission (Toyota PRIUS) Electric motor on the rear-axle (PSA 3008 HY4) March 18 th, 2014 I 6
Hybridization market: Worldwide Trend Vehicles <6T, Oil barrel $120 2020, Li-Ion Battery 300 /kwh 2020 Source: 2013 Valeo Powertrain Forecast Internal Combustion Engine Growth of Stop-Start FULL as niche, then growth MILD take-off Emergence of PHEV Stop-Start 98.2 % ICE MILD FULL PHEV EREV BEV 1.8 % Trends 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, growing 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 March 18 th, 2014 I 7
Hybridization market: Europe Trend Vehicles <6T, Oil barrel $120 2020, Li-Ion Battery 300 /kwh 2020 Source: 2013 Valeo Powertrain Forecast Internal Combustion Engine Fast growth of Stop-Start Emergence of Electric Growth of MILD / FULL Rising importance of PHEV MILD No real EREV/BEV take off 97.0 % ICE Stop-Start FULL PHEV EREV BEV 3.0 % Trends BEV/FCEV: lower forecast than in the past (A / B / C + LCV), EREV remaining a niche FULL / PHEV: growing significance, with higher weight of PHEV in sales MILD: somewhat postponed take off expected in 2018 Stop-Start: becoming standard within the next 6 years, almost 0% conventional engines in 2023 Significant Hybrid growth expected before 2020 to reach 95g (expected 103g 2020, 88g 2023) March 18 th, 2014 I 8
Agenda Market analysis Affordable hybrid CO2Pilot concept From eco-driving to automated driving Conclusion March 18 th, 2014 I 9
Targets and optimization levers Target: Improve Hybrid powertrain cost affordability Define the best cost vs. CO2 benefits ratio through Components optimization Standardization Implementation Integration Sizing & technological choices Generic components & 48V network Advanced operation functions Location flexibility & low intrusivity March 18 th, 2014 I 10
140 120 100 80 60 40 20 0 0 200 400 600 800 1000 1200 TIME (s) System assessment through simulation Architecture study (e-machine location) Gearbox EM EM Starter/Alternator ICE DC/DC LV Battery EDLC Electric motor & battery (Technology, Power, Voltage, Capacity) Mission profile (NEDC, WLTC, Artemis Urban) HV Bus HEV simulation platform Supervisor model Vehicle & driver model Traction model Supervision & control Energy Management - Operating modes - Energy storage Fuel consumption - CO2 saving - Cost / gco2 VEHICLE SPEED (km/h) Vehicle platform (Engine displacement, segment) Optimized system March 18 th, 2014 I 11
Architecture study DC/DC MH1 Gearbox ICE EM LV Battery EDLC HV Bus Electric Motor directly on the crankshaft of the engine MH2 Gearbox EM EM Starter/Alternator ICE DC/DC LV Battery EDLC HV Bus Electric Motor between engine and gearbox with an additional clutch MH3 EM Gearbox EM Starter/Alternator ICE DC/DC LV Battery EDLC HV Bus Electric Motor behind the gearbox through a disconnect clutch Less intrusive system is with belt-driven machine March 18 th, 2014 I 12
Hybrid architecture assessment Simulation results on NEDC cycle B segment vehicle with Turbo Gasoline DI engine Optimal control / EDLC battery storage / Optimum size for each architecture MH1 4kW CP CO2 emissions s benefit (%) MH1 MH2 MH3 CP : Claw Pole MR : Mixed Rotor PM : Permanent Magnet PM P : Permanent Magnet Pancake OEM on cost wo integration overcost ( ) MH1 6kW CP MH1 8kW MR MH1 14kW MR MH2 8kW MR 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 Best cost to value with a 6-8 kw BSG motor March 18 th, 2014 I 13
Vehicle implementation BSG e-machine Engine & Powertrain Control Unit 48V i-bsg Inverter DC/DC converter Energy storage Demonstrator BSG implementation on 1.6L Turbo GDI Manual Trans. March 18 th, 2014 I 14
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 mode Torque split management Driver request Torque request Conventional Electric Torque assist Generation Overboost Overboost request Thermal engine Electric machine March 18 th, 2014 I 15
CO2 benefits Simulation results on NEDC cycle B segment vehicle with Turbo Gasoline DI engine MH1 architecture / Real time control Additional benefits can be reached using predictive control March 18 th, 2014 I 16
Agenda Market analysis Affordable hybrid CO2Pilot concept From eco-driving to automated driving Conclusion March 18 th, 2014 I 17
Context and principle Prediction of coming torque demand profile allows optimizing 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 Zero Emission Vehicle phases (low speed limitation, traffic jam) Application example: Preconditioning before downhill area: Anticipation of available energy during regenerative phase. Battery SOC High SOC Preconditioning Free energy area Wasted free energy Conventional CO2Pilot Low SOC Optimal preconditioning March 18 th, 2014 I 18
Road profile prediction - Driving Assistance Data Fusion Digital Map Embedded Sensors Telecommunication Next 5km At 50m Data fusion in ADAS ECU or front camera to predict oncoming driving profile from short term / dynamic events and mid/long term areas March 18 th, 2014 I 19
Traffic Sign Recognition Traffic Sign recognition by camera Image provided by SpeedVue camera Sub signs detection (trucks only etc) Fusion with GPS location/ speed limit information Traffic Sign identification in Navigation database Situational awareness with line identification March 18 th, 2014 I 20
Car2X Cooperative Traffic Lights Communication : 802.11p Use Cases : Green Light Speed Advisory Automatic regenerative braking system Cooperative Traffic Light Traffic Light data reception GLOSA speedometer March 18 th, 2014 I 21
CO2 assessment 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 Acceleration situation Deceleration situation Slope situation Short time situation Mountain situation Extra urban to urban situation Urban Extra to urban situation Long time situation Key impacts on data fusion Example on WLTC cycle For low energy storage, high detection precision and events detection are required For high energy storage, the detection precision needs decrease from events to areas Prédiction horizon time (Mild hybrid) (Full hybrid) Detection precision needs Energy storage March 18 th, 2014 I 22
CO2 assessment using predictive optimal ctrl CO2 benefits on homologation cycle using predictive optimal control Speed (k km/h) Benefits depend on: CO2 benefits on real usage using predictive optimal control 100 Cergy 50 Benefits highly depend on mission profile but increase fuel economies robustness in real life - Hybridization architecture - E-Machine power - Stocker size - Cycle Bobigny 0 0 1000 2000 3000 4000 5000 6000 7000 8000 tim e (s ) Expected fuel benefits with full prediction Real usage MH1 (belt driven) Up to 3% MH3 (on axle) Up to 5% Major benefits with low storage device come from regenerative braking optimisation further improvement by automated deceleration March 18 th, 2014 I 23
CO2 assessments / eco-driving results Principle Automatic intervention on vehicle command to realize eco-driving Driver acceptance taken into account Vehicle speed Conventional CO2Pilot CO2Pilot deceleration Injection cut-off Optimal deceleration start Deceleration control with electric machine Driver deceleration Injection cut-off Stop event Additional fuel benefits by activating automated regenerative braking through injection cut off control Automated deceleration Roundabout x 4 Real usage Fuel benefits Up to 10% Impact on driving time <3% March 18 th, 2014 I 24
Agenda Market analysis Affordable hybrid CO2Pilot concept From eco-driving to automated driving Conclusion March 18 th, 2014 I 25
Towards fuel efficient automated driving The driver is an important factor on the fuel efficiency Driver coaching systems are the first step Full potential will be achieved with automated energy efficient vehicle control Traffic flow anticipation Green wave Optimized powertrain operation (engine speed & load, cut off, regenerative braking) March 18 th, 2014 I 26
Race towards Automated Driving Daimler (Aug 13) BMW (since 11) Audi (Jan 14) Volvo (Nov 13) Berta Benz drive Country and Urban roads Motorway Pilot (with Lane Change) Traffic Jam Pilot Valet Parking Mixed roads 100 vehicles in 2017 GM (Apr 13) Ford (Dec 13) Toyota (Oct 13) Nissan (Nov 13) Motorway Pilot (ACC + Lane Keeping) Autopilot capabilities, such as vehicle platooning Motorway Pilot (ACC + Lane Keeping + V2V) Motorway Pilot (ACC + Lane Keeping) Renault (Feb 14) Traffic Jam Pilot (ACC + Lane Keeping) Images: OEMs March 18th, 2014 I 27
Automated driving Automated driving will leverage experience from automated parking and low speed control to extend to motorway and urban driving Low acceptance High acceptance Automated Parking Emergency Braking Traffic Jam Pilot Temporary Autopilot Source: Intuitive Driving workshops 2012 March 18 th, 2014 I 28
Automated car classification THE CON NNECTED CAR SIMPLE ASSISTED the car partially takes over the trajectory tasks (braking, accelerating or steering) under driver supervision PARTIALLY AUTOMATED the car partially takes over the trajectory tasks (braking, accelerating &/or steering) under driver supervision Legal framework to be adapted CONDITIONALLY AUTOMATED the car completely takes over the trajectory tasks (braking, accelerating and steering) for a limited duration in a given situation HIGHLY AUTOMATED the car completely takes over the trajectory tasks (braking, accelerating and braking) for a longer duration in given situations FULLY AUTOMATED the car completely takes over the trajectory tasks (braking, accelerating and braking) for the entire trip LEVEL1 LEVEL 2 LEVEL 3 LEVEL 4 LEVEL5 March 18 th, 2014 I 29
Intuitive Driving for safe and connected mobility 1 Automated Car Park4U Remote 360 Vue Intuitive Driving 2 InSync InTouch Connected Car Lane keeping Cocoon detection & fusion, ego localization, System & decision, Automated driving functions 3 eskin Intuitive Controls Car2X Intuitive Driving for safe and connected mobility while reducing CO2 emissions Display, control & connect March 18 th, 2014 I 30
Agenda Market analysis Affordable hybrid CO2Pilot concept From eco-driving to automated driving Conclusion March 18 th, 2014 I 31
Conclusion Hybrid powertrain affordability can be improved through Components optimization & standardization Advanced operation functions CO2Pilot & ADAS systems 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 System understanding & optimization Cost to CO2 optimization New value created through system approach March 18 th, 2014 I 32
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