AVL List GmbH (Headquarters) Public Energy-Efficient Cooperative Adaptive Cruise Control Dr. Stephen J. Jones, AVL List GmbH [email protected]+43 664 850 9172 N. Wikström, A. Ferreira Parrilla, R. Patil, E. Kural, A. Massoner, AVL List GmbH A. Grauers, Chalmers University of Technology
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Energy-Efficient Cooperative Adaptive Cruise Control · Adaptive Cruise Control (ACC): Adapts speed based on distance to & speed of preceding vehicle, e.g. measured using on-board
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AVL List GmbH (Headquarters)
Public
Energy-Efficient Cooperative
Adaptive Cruise ControlDr. Stephen J. Jones, AVL List [email protected] +43 664 850 9172
N. Wikström, A. Ferreira Parrilla, R. Patil, E. Kural, A. Massoner, AVL List GmbH
A. Grauers, Chalmers University of Technology
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 2Public
Energy-Efficient CACCContents
1. Introduction to Predictive Energy Management
2. Traffic Light Assistant
3. Energy-Efficient Cooperative Adaptive Cruise Control
a) Problem Overview
b) Model Predictive Control
c) Results
4. Summary & Conclusion
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 3Public
Introduction4 Pillars of ADAS/AD Engineering Services
Trusted Engineering Service Provider & Development Partner at ADAS & Autonomous Driving with long term references at several OEMs
System Designsystem engineering,
use & test cases, architecture, component & function
specification
Tailored Control & SW Development
concept & series development customer features, modification/
adaptation
Advanced Predictive Functions improving vehicle attributes e.g. energy or fuel efficiency
Calibration, Testing & Validation
derivative integration, optimization & assessment, testing from lab, XiL to road
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 4Public
▪ Accident free driving active safety functions e.g. emergency braking, lane keeping assistant
▪ Driver relief & comfort functions e.g. parking assistant, adaptive cruise control
▪ Connectivitye.g. smart phone interaction, real time traffic information, V2X, cloud computing
▪ Fuel/energy efficiency e.g. EV driving range, Fuel saving by predictive functions & platooning
▪ Operating cost: Driver substitution as TCO argument at mainly transport & shared mobility business
Key importance
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 6Public
IntroductionPredictive Energy Management Leveraging ADAS Data
PREDICTIVE CHARGINGTRAFFIC LIGHT
ASSISTANT
PREDICTIVE THERMALMANAGEMENT
PREDICTIVE ADAPTIVE CRUISE CONTROL
ECOROUTING
COASTING ASSISTANT
Sources: www, AVL
PREDICTIVE GEARSHIFT
ADAS/ADHMI
CONNECTED
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 7Public
Energy-Efficient CACCContents
1. Introduction to Predictive Energy Management
2. Traffic Light Assistant
3. Energy-Efficient Cooperative Adaptive Cruise Control
a) Problem Overview
b) Model Predictive Control
c) Results
4. Summary & Conclusion
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 8Public
Market Example: TLA Audi
• Audi announces first V2I service in 10 US cities (as of 19/11/18) with Traffic Light information system. System available in 2017 on Q7, A4 & A4 Allroad.
• Current system: Real-time communication with city’s traffic management system, displays traffic light status (red/orange/green) & “time-to-green”.
• Future dvpt: integration of system with start/stop function, Green Light Optimized Speed Advisory (GLOSA), optimized navigation routing & other predictive services.
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 11Public
Market Example: TLAJaguar Land Rover
• GLOSA (Green Light Optimal Speed Advisory) system: V2X communication with traffic lights to advise driver of optimal approach speed to avoid having to stop at traffic light junctions.
• “Prevents drivers from racing to beat lights & improves air quality by reducing harsh acceleration or braking near lights.”
• Trials on Jaguar F-Pace.
• Works in conjunction with other ADAS: Adaptive Cruise Control, Intersection Collision Warning.
▪ Optimizes in real-time trade-off between energy efficiency, driver comfort & safety.
Velocity 𝒗
Acceleration 𝒂
Powertrain Model
Gear 𝑮
Road Inclination 𝜽
Ego Vehicle Preceding Vehicle
V2X (Vehicle to Anything)
On-Board Sensors
(e.g. radar)
Traffic Light
Vehicle-to-Vehicle (V2V)
Energy savings up to 30%*
Energy Consumption Map computed Online in real-time Optimization
Vehicle-to-Infrastructure (V2I)
…
Inertia, Drag, Rolling Res., Gravity
* Depends on test case, baseline, etc.
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 20Public
Energy-Efficient CACCContents
1. Introduction to Predictive Energy Management
2. Traffic Light Assistant
3. Energy-Efficient Cooperative Adaptive Cruise Control
a) Problem Overview
b) Model Predictive Control
c) Results
4. Summary & Conclusion
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 21Public
Energy-Efficient CACC – MPCIntroduction to Model Predictive Control (MPC)
𝑢
𝒖𝒐𝒑𝒕 = 𝑢 0 , 𝑢 1 ,… , 𝑢 𝐻𝑇𝑇
Optimal sequence of control inputs over prediction horizon 𝐻𝑇
* i.e. vehicle & driving environment
*
** vehicle states, traffic light information, etc.
**
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 22Public
Energy-Efficient CACC – MPCHybrid Model Predictive Control
▪ Hybrid* Model Predictive Control (MPC) dynamically incorporates descriptions of upcoming traffic & road conditions as constraints in receding horizon.
▪ Non-linear constraints like energy consumption, gear shifts, full load, & road attributes (e.g. gradient, curvature, speed limits) modelled.
▪ eHorizon & V2X used for better predictions of preceding traffic & infrastructure, including traffic lights, variable speed limits, delivery & bus stops.
Energy consumption mapincluding gear shifting
Acceleration limitsincluding road gradient
Road segmentation for topology, speed limits, etc.
*Note Hybrid here refers to modelling technique, not the powertrain type
1st2nd
3rd4th5th
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 29Public
Energy-Efficient CACCContents
1. Introduction to Predictive Energy Management
2. Traffic Light Assistant
3. Energy-Efficient Cooperative Adaptive Cruise Control
a) Problem Overview
b) Model Predictive Control
c) Results
4. Summary & Conclusion
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 31Public
Energy-Efficient CACC – Simulation ResultsGraz Route Simulation without Traffic
Energy savings: 25% without traffic,without an increase in travel time
Adjustable travel time & driver comfortability
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 32Public
Energy-Efficient CACC – Simulation ResultsGraz Route Simulation with Traffic
Energy savings: 16% with traffic,without an increase in travel time
Adjustable travel time & driver comfortability
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 33Public
▪ Virtual test environment for ADAS, including real communication units.▪ RT interaction / communication of traffic control infrastructure & cars.▪ Specific testbed setting for specialized application.▪ Testbed & Road testing with real vehicle & V2X units.
EECACC controlled test case achieves lower fuel consumption by end of the maneuver (up to 25% diesel consumption savings measured).
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 34Public
Energy-Efficient CACCContents
1. Introduction to Predictive Energy Management
2. Traffic Light Assistant
3. Energy-Efficient Cooperative Adaptive Cruise Control
a) Problem Overview
b) Model Predictive Control
c) Simulation Results
d) Testbed Results
4. Summary & Conclusion
Stephen Jones, N. Wikström, A. Ferreira Parrilla | DS | 28 February 2019 | 35Public
Energy-Efficient CACCSummary
▪ Increasing interest in V2X communications to intelligently connect conventional & electrified vehicles.
▪ V2X supported ADAS such as simple Traffic Light Assistants, now starting to be introduced in market.
▪ Efficiency, safety & convenience all benefit from optimized vehicle speed profiles.
▪ AVL’s Energy-Efficient Cooperative Adaptive Cruise Control (EECACC) reduces energy consumption by
up to 30%* in simulated city scenario, 25% on testbed.
▪ EECACC considers static layout, sizing & efficiency of powertrain, as well as dynamic state (e.g. SoC,
temperature) of powertrain, road topology, traffic, & traffic light signal phasing & timing (SPAT) data.
▪ Benefits of EECACC extended to other functions e.g. predictive hybrid powertrain mode selection.
* Like most predictive functions, the benefits depend on specific use case, baseline taken, etc.