ARPA-E NEXTCAR Project: Energy Efficiencies for Connected Vehicles Christopher Flores Director, Advanced Technology Sensys Networks

Webinar Sponsored by

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.

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

Arterial Driving

V2V/V2I communication

Cloud connectivity

Highway Driving

V2V communication Cloud connectivity

Eco Routing

Cloud connectivity

Scenarios

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

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.

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

Live Oak Demo

• 3 vehicle platoon demo • V2I to safely stop at red lights • V2V to allow compact platoon formation

Scenario:

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

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

Q&A Thank You

1

Demonstrating

Eco-Drive in

Real-World

Environments

Dr. Kanok

Boriboonsomsin

Dr. Aravind Kailas

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

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

4

W Harry Bridges Blvd1

Alameda St

Del Amo Blvd

2

Wilmington Blvd Del

Amo Blvd

3

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

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

Eco-Drive has the potential to reduce inefficiencies at intersections, especially for heavy duty trucks

6

GPS Map Radar

EAD

ECU

DVI

1

Live SPaT StreamsMap9

7

2

3

4

Connected

truck

Cloud server

City TMC

……Connected

traffic signals

8

8 8 8 8

5 6

The connected architecture comprises onboard units (on the truck) and offboard units (traffic signals and cloud server)

7

Connected to 4G-LTE cellular network

Existing cellular communication technology can be used to enable connected intersections

8

Side viewTop view

Connected vehicle onboard units include sensing, communication, computing, and display devices

9

10

Early results reveal potential for energy savings with Eco-Drive, but additional analysis is needed as this is not straightforward

11

% 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

• 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

12

Quantifying and measuring the benefits of Eco-Drive in real-world trucking applications is challenging

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

13

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

14

Demonstrating

Eco-Drive in

Real-World

Environments

Dr. Kanok Boriboonsomsin

kanok@cert.ucr.edu

Dr. Aravind Kailas

aravind.kailas@volvo.com

mailto:kanok@cert.ucr.edumailto:aravind.kailas@volvo.com

Comparison of 4G/LTE and DSRC Latency in a Real-World Environment

Kun ZhouCalifornia PATHJune 17, 2020

Background

• Conducted under Caltrans Funded Project – Red Light Violation Warning (RLVW) over Cellular

Source: CICAS-V Concept of Operations Document

Average 3G and 4G Network Latency by Provider in the U.S. in 2018

Source: Statista

Objective

• To quantify point-to-point communication latency over DSRC and 4G/LTE in the California CV Test Bed in Palo Alto

Conceptual Message Flow

• The Server is located at PATH Headquarters• Same SAE J2735 message payloads are transmitted over DSRC and 4G/LTE

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

Vehicle-Side Simultaneous Data Collection

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

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

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

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

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