arpa-e nextcar for connected vehicles christopher flores … · 2020. 6. 26. · arpa-e nextcar...
TRANSCRIPT
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ARPA-E NEXTCAR Project: Energy Efficiencies for Connected Vehicles Christopher Flores Director, Advanced Technology Sensys Networks
Webinar Sponsored by
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PREDICTIVE DATA-DRIVEN VEHICLE DYNAMICS AND POWERTRAIN CONTROL: FROM ECU TO THE CLOUD
UC Berkeley Department of Mechanical Engineering
ARPA-E NEXTCAR program April 2017 – April 2020
Hyundai Motor Group Sensys Networks Inc.
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Objective
• Demonstrate 20% reduction of energy consumption on a plug-in HEV
• Real-time control and planning with uncertain forecasts
• Co-optimize vehicle dynamics and powertrain controls
• Harness cloud computing forecasts, historical data coordination with infrastructure driving automation coordination with other vehicles
• Conduct market feedback
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Arterial Driving
V2V/V2I communication
Cloud connectivity
Highway Driving
V2V communication Cloud connectivity
Eco Routing
Cloud connectivity
Scenarios
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Connected infrastructure
Signal Phase and Timing (SPaT) for 8 intersections along Live Oak in Arcadia
(2.5 km)
Baseline vehicles 3 2017 Hyundai IONIQs PHEV
2 2017 Hyundai IONIQs HEV
L2 Automation
CAV instrumentation
RADAR Camera
8.9 kWh Li-ion
battery 1.6L
engine 44.5 kW
motor
6 speed dual-clutch
transmission
AVL PEMS
Fuel Flow Meter
dSPACE MicroAutoBox
Adlink MXC-6400
ETAS DAQ
Cohda MK5 OBU
Field System Setup
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Simulation System Setup •Hardware-In-the-Loop setup for both arterial and highway driving •Reproducible scenario for fair comparison of controller performance •Real vehicle for accurate powertrain measurement and vehicle dynamics
Y. Kim, S. Tay, J. Guanetti, F. Borrelli. (2018) Hardware-In-the-Loop for Connected Automated Vehicles Testing in Real Traffic. 14th International Symposium on Advanced Vehicle Control, AVEC’18. Beijing.
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Vehicle SW Architecture
Elevation mapsTraffic data Sensys Networks
DatorEco-route Eco-drive Eco-charge Learned models Model learning
CAN/ETH Gateway
Data logging
Motion control
Powertrain control
PerceptionLocalizationPrediction
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Live Oak Demo
• 3 vehicle platoon demo • V2I to safely stop at red lights • V2V to allow compact platoon formation
Scenario:
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Results
Technology MPGe improvement
Time penalty Mode Validation
Powertrain blending + highway eco-ACC
27.8% 0% Blended Simulations 13.9% 0% Blended Proving ground test
Predictive eco-
approach/departure at signalized intersections
27.3% 12.4% CD Simulations
31.0% 8.5% CD Road testing (Arcadia)
20.4% 11.9% CS Simulations
Eco-routing 14.9% 15.8% Blended Simulations (Bay
Area) 11.8% 20.0% Blended Road test (Bay Area)
Compact platooning Up to 15% 0% CD Proving ground test
Up to 10% 0% CS Proving ground test
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Key Lessons Learned
• PHEVs powertrain control can be greatly improved combining real-time optimization with historical and real-time data
• Highway ACC can yield substantial energy savings by speed profile shaping, compact platooning, and powertrain co-optimization
• Arterial eco-approach benefits significantly impacted by surrounding traffic
• Customers value convenience and safety greatly, they appreciate energy savings and are willing to compromise on travel time if there is a clear benefit
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Q&A Thank You
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1
Demonstrating
Eco-Drive in
Real-World
Environments
Dr. Kanok
Boriboonsomsin
Dr. Aravind Kailas
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CARB-SCAQMD-Volvo Group-Metro-UC Riverside Project
• How - CARB Zero Emission Drayage Truck Demonstration Low Carbon Transportation Greenhouse Gas
Reduction Fund
• What – SCAQMD-led Plug-in Hybrid Electric Vehicle (PHEV) Ultra project to
o design of advanced vehicle controls
o Eco-Drive aspect - explore synergies between HEV platforms and connected vehicles
Completed – demonstrated connected vehicle capability using a conventional diesel truck
Ongoing – applying this capability to a PHEV truck
2
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3
• How - CEC Alternative and Renewable Fuel and Vehicle Technology Program Grant
• What - POLA-led Advanced Yard Tractor Deployment & Eco-FRATIS* Drayage Truck Efficiency
Project
o advance zero/near-zero emission cargo-handling equipment & truck technology to reduce
emissions
o Eco-Drive aspect – provides real-time traffic signal data to truckers optimize
acceleration/deceleration of trucks
*FRATIS = Freight Advanced Traveler Information Systems
CEC-Port of Los Angeles-Metro-UC Riverside Project
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4
W Harry Bridges Blvd1
Alameda St
Del Amo Blvd
2
Wilmington Blvd Del
Amo Blvd
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15 Connected traffic lights +4
ITS deployment at the ports1
Pooling resources has resulted in the first connected vehicle corridor at the ports, spanning 6-8 miles
3
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5
Secured ServerSignal
Phase and Timing
Information
Sensing of Preceding
Vehicle
Vehicle Equipped with the Eco-Drive Application
Signal Phase and
Timing Information
Eco-Drive is a connected vehicle application where traffic signal data is used to design the best driving speed profiles
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Eco-Drive has the potential to reduce inefficiencies at intersections, especially for heavy duty trucks
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GPS Map Radar
EAD
ECU
DVI
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Live SPaT StreamsMap9
7
2
3
4
Connected
truck
Cloud server
City TMC
……Connected
traffic signals
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8 8 8 8
5 6
The connected architecture comprises onboard units (on the truck) and offboard units (traffic signals and cloud server)
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Connected to 4G-LTE cellular network
Existing cellular communication technology can be used to enable connected intersections
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Side viewTop view
Connected vehicle onboard units include sensing, communication, computing, and display devices
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10
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Early results reveal potential for energy savings with Eco-Drive, but additional analysis is needed as this is not straightforward
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% R
eduction in E
nerg
y C
onsum
ption
Alameda Wilmington
NB SB NB SB
-8
-7
-17
-12
• Will change with traffic
pattern
• May vary going NB and
SB
• May vary depending on
the street
• May result in less/no gains
for Eco-Drive also
Snapshot after 100 simulation runs for a random traffic pattern
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• Results are highly dependent on
o Traffic patterns
o Truck configuration
o Truck load
o % trucks on the road
o # connected traffic lights (along a corridor)
o # connected traffic light placement (along a corridor)
o Driver habits
o # connected trucks
o …
• Powertrain integration a must for HEV platforms to study benefits of Eco-Drive – otherwise, performance same as conventional diesel truck
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Quantifying and measuring the benefits of Eco-Drive in real-world trucking applications is challenging
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South Coast Air Quality Management District
Patricia Kwon, Joseph Impullitti, Joseph Lopat
Port of Los Angeles
Kerry Cartwright, Prashant Konareddy
Los Angeles County Metropolitan Transportation
Authority
Ed Alegre, Shrota Sharma
Los Angeles County Department of Public Work
Jane White, Pedro Cruz
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City of Carson
Reata Kulcsar, Gilbert Marquez
City of Los Angeles Department of Transportation
George Chen, Taesang Nam, Jonathan Hui
University of California at Riverside
Yuan-Pu Hsu, Alexander Vu, Francisco Caballero
Peng Hao, Ziran Wang, Guoyuan Wu, Matthew Barth
Volvo Group North America
Pascal Amar, Eddie Garmon, Sandeep Tanugula
Julie Wright, Lenny Levin, Kyle Palmeter
Manali Menaria, Steve Orens
It takes a village to build out the connected vehicle infrastructure and deploy connected vehicles in the real world
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14
Demonstrating
Eco-Drive in
Real-World
Environments
Dr. Kanok Boriboonsomsin
Dr. Aravind Kailas
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Comparison of 4G/LTE and DSRC Latency in a Real-World Environment
Kun ZhouCalifornia PATHJune 17, 2020
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Background
• Conducted under Caltrans Funded Project – Red Light Violation Warning (RLVW) over Cellular
Source: CICAS-V Concept of Operations Document
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Average 3G and 4G Network Latency by Provider in the U.S. in 2018
Source: Statista
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Objective
• To quantify point-to-point communication latency over DSRC and 4G/LTE in the California CV Test Bed in Palo Alto
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Conceptual Message Flow
• The Server is located at PATH Headquarters• Same SAE J2735 message payloads are transmitted over DSRC and 4G/LTE
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Server Identifying the Relevant Intersection w.r.t.the Location of a Connected Vehicle
• A connected vehicle is able to determine the MAP that the vehicle is traveling on and the IDs of its connecting intersections
• With 4G/LTE, the vehicle can send the ID of the current intersection and IDs of the connecting intersections to the server along with the BSM
• When the MAP of the current intersection is not available, the server sends the MAP of nearby intersections to the vehicle based on the proximity between vehicle location and intersection MAP reference point
BSM Current Intersection ID# of Received
MAPsIDs of Received
MAPs# Connecting Intersections
IDs of ConnectingIntersections
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Vehicle-Side Simultaneous Data Collection
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Filed Test Results
• Sample Size
• With DSRC, the vehicle receives SPaT messages from RSUs that are within the DSRC communication range, ranging from 0 to 4
• With 4G/LTE, the vehicle receives SPaT messages from the current and the connecting intersections
Communications Link Sample SizeDSRC 1,135,9164G/LTE 1,476,691Same SPaT Message Received on Both Links 716,018
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Comparison of Communication Latency over DSRC and 4G/LTE
0 100 200 300 400 500 600 700 800 900 1000
Communication Latency (milliseconds)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Empi
rical
Cum
ulat
ive
Dis
tribu
tion
Func
tion
DSRC (1,135,916 samples)
4G/LTE (1,476,691 samples)
F(60) = 98.7
F(100) = 95.4
ECDF – Empirical Cumulative Distribution FunctionCommunication latency = Message received time – Message created time
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Communication Latency Difference
Communication latency difference = Message received time over 4G/LTE – (Same) Message received time over DSRC
0 100 200 300 400 500 600 700 800 900 1000
Additional 4G/LTE Communication Latency than DSRC (milliseconds)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Empi
rical
Cum
ulat
ive
Dis
tribu
tion
Func
tion
SPaT (716,018 samples)F(85) = 95.8
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Summary of Communication Latency
• 5.9 GHz band spectrum is critical for safety applications that require reliable and short communication latency
• Existing 4G/LTE could support mobility applications
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Simultaneous In-Vehicle Display of V2I Information
ITESoCal_ITSCA Webinar 061720 Slide Number 1PREDICTIVE DATA-DRIVEN VEHICLE DYNAMICS AND POWERTRAIN CONTROL: FROM ECU TO THE CLOUDObjectiveScenariosField System SetupSimulation System SetupVehicle SW ArchitectureLive Oak DemoResultsKey Lessons LearnedSlide Number 16
2020-06-17 ITE SoCal ITS CA Webinar Kanok & Aravind v4_for postingComparison of 4G-LTE and DSRC LatencyComparison of 4G/LTE and DSRC Latency in a Real-World EnvironmentBackgroundAverage 3G and 4G Network Latency �by Provider in the U.S. in 2018ObjectiveConceptual Message FlowServer Identifying the Relevant Intersection w.r.t. the Location of a Connected VehicleVehicle-Side Simultaneous Data CollectionFiled Test ResultsComparison of Communication Latency �over DSRC and 4G/LTECommunication Latency DifferenceSummary of Communication LatencySimultaneous In-Vehicle Display of V2I Information