Connected/Automated and Autonomous ...
Connected/Automated and Autonomous Vehicles (CAVs) Patrick B. Davis, Director
Vehicle Technologies Office Energy Efficiency and Renewable Energy U.S. Department of Energy (DOE) July, 2014
Oil Dependency is Dominated by On-Road Vehicles Transportation is responsible for 2/3 of U.S. petroleum usage On-Road vehicles responsible for 80% of transportation petroleum usage >240M Vehicles on the road
Economic security, energy security, and environmental stewardship Changing energy landscape • Natural gas • Electrification • Fuel Economy Standards
The Cost of Oil is Not Just Monetary
2
Overview and Outline: EERE in a CAV Context 2) 1) Existing EERE Capabilities
Leveraging existing expertise for CAVs •
EERE Existing mobility technology RDD&D
3
• •
3) Expanding expertise for future priorities
Collaboration Data infrastructure (hardware, software) Controls (diagnostics, sensor development) Systems modeling (vehicle testing, data collection and analysis)
Wealth of ongoing activity in the CAV space (U.S. DOT, private sector)
EERE Existing Capabilities Technology Offices Vehicles • • • •
Efficiency Improvement Fuel Diversification Domestic & Renewable Reduced GHG
Bioenergy
Hydrogen and Fuel Cells
National goals •& Reduce GHG emissions in the range of 17% by 2 Reduce net oil imports by 50% by 2020 * • Standards Achieve CAFE Standards 54.5 mpg by 2025 •
*Major Administration Goals
4
EERE Existing Capabilities: Current RDD&D Focus • EERE is DOE’s primary applied research office • Research, Development, Demonstration, and Deployment – – – – – – – –
5
Vehicle Electrification Materials Lightweighting Advanced Combustion Drop-in Biofuels Fuel Cell Technology Hydrogen Infrastructure Deployment (e.g., Clean Cities) Grid Systems Integration
Early EERE CAV R&D efforts tie existing expertise Recognizing Key to CAV energy needs Current EERE R&D Energy-Related Needs for Connected Mobility
Existing EERE Core Expertise •
Transportation energy system analysis
•
Vehicle communications and data collection
•
Alternative fuel technologies and systems
•
•
•
•
6
Co-optimization of safety and efficiency • Vehicle re-design (potential significant lightweighting) Interoperability across connected mobility communication systems Alternative fuels enabling potential: enhanced ROI and adoption? Predicting vehicle use systems response • Rebound effect(s) • Increased mobility access
Efforts at the Nexus of Energy and Mobility •
•
Foundational studies on potential energy effects • Synergistic gains from safety initiatives • Fuel efficiency algorithms • Vehicle redesign (lightweighting and aerodynamics) Deeper research in opportunity areas • Advanced vehicle design • Novel diagnostics, controls, and sensor development
Synergies Between Connected Mobility & Energy
7
Autonomy
• Efficient driving • Platooning • Assisted parking
Safety & Collision Avoidance
• Reduced idling • Significant light weighting • Enhanced aerodynamics
Multimodal Transportation
• Lowest carbon trip planning • Automated carpooling
V2X
• Vehicle-to-Vehicle (V2V) • Traffic signal management (V2I) • Grid system integration (V2G)
Data as a Service
• Big data analytics • Efficient routing • Optimizing corridor efficiency
Foundational studies estimate ranges of energy effects Positive Energy Outcomes
Negative Energy Outcomes
Enabling electrification Higher occupancy Lightweighting & powertrain/vehicle size optimization
Less hunting for parking
More travel Faster travel
Full cycle smoothing Travel by underserved Efficient routing Efficient driving Platooning
-1
-0.8
-0.6
-0.4 Use Intensity
-0.2
0
0.2
Energy Intensity
0.4
0.6
0.8
1
Fuel Intensity
Brown, A.; Gonder, J.: Repac, B. (2014). “An Analysis of Possible Energy Impacts of Automated Vehicles.” Chapter 5, Societal and Environmental Impacts. Meyer, G., ed. Lecture Notes in Mobility: Road Vehicle Automation. Berlin: Springer.
8
Potential combined fuel demand increases/decreases by factor category
Foundational studies convey energy effect uncertainty 1 1.1 0
-16%
+30% -0.75
0 -0.75
-1 Use Intensity
Energy Intensity
Fuel Intensity
• Energy impacts can be dramatic – Potential for large improvements in energy and fuel intensity – Increased use intensity may counteract
• Significant uncertainty exists – Total combined impacts from >90% savings to >150% increase in energy use—further research warranted 9
Current efforts inform expanding EERE expertise for future priorities Current EERE R&D Efforts (Foundational studies, deeper research) • •
DOE prioritizing CAV “layers” in which to participate Coordinating across agencies to leverage funding Example:
Possible Future Research •
• •
10
Refine foundation studies on energy impacts • Understanding and reducing uncertainties • Better system interaction modeling • Further energy-focused data collection and analysis Increased collaboration with USDOT • Engage with UMTRI, RITA, NHTSA, ITS America, Non-Profits Continued leveraging of existing expertise • Hardware, software, physical & data infrastructure(s), cyber security • Diagnostics, controls, and sensor development • Systems modeling and vehicle testing • Data collection and analysis
Modeling and Simulation 1. Individual connected vehicle and fleet energy optimization 2. City/corridor connected traffic systems 3. Leveraging model observations for Federal policy implications
Ex 1. Modeling and simulation to optimize connected vehicle/fleet energy use Energy Impact of "Efficient Driving" for Advanced Powertrains Benefits of Connected Route-Based Energy Management for LightDuty Electrified Vehicles Benefits of Connected Route- and Duty-Based Energy Management for P&D PHEV Truck
Virtual Proving Grounds for Development and Evaluation of Energy Efficient CAVs
Energy Impact of CAVs in the context of an Entire Transportation System Benefits of Vehicle Electrification/Automation in the Context of a Large Metro Area Behavior Changes and Adoption under various policies / technologies
Single Vehicle
Small Network of CAVs and ITS 11
Entire Urban Area
Ex 2. Combining models/simulations for city/corridor energy use
Market Penetration / Fleet Definition
ansportation Simulation
Vehicle Energy Consumption
efine network, control & demand 25 20 15 10
25
5 0
20 0
100
200
300
15
400
500
10
40
5 0
0
100 30 200
300
400
500
20 10 0
0
500
1000
1500
2000
2500
Vehicle speed/grade profiles extraction/generatio consumption of n the transportation
Energy network from corridor (~100,000 vehicles) to entire cities (>10M vehicles) 12
Ex 3. Assessing model observations for policy development
Choic e
POLARIS+ Vehicle Ownership Model Level 3
Level 2 Combined Function automation
Level 0 No automationModel
13
Limited self-driving automation
Level4 Full Self-Driving Automation
changes of traveler’s behavior in the context of automation level
VMT
Market Adoption
Choic e
Polic y
Level 1 Function specific automation
Aggrega te
Mode Choice/Shift
Regional or National
Vehicle Ownership Model: Market adoption dynamics based on household characteristics (eg. demographic, existing vehicles ownership, income)
Patrick Davis [email protected]
Vehicle Technologies Office Energy Efficiency and Renewable Energy U.S. Department of Energy (DOE) July, 2014
Oil Dependency is Dominated by On-Road Vehicles Transportation is responsible for 2/3 of U.S. petroleum usage On-Road vehicles responsible for 80% of transportation petroleum usage >240M Vehicles on the road
Economic security, energy security, and environmental stewardship Changing energy landscape • Natural gas • Electrification • Fuel Economy Standards
The Cost of Oil is Not Just Monetary
2
Overview and Outline: EERE in a CAV Context 2) 1) Existing EERE Capabilities
Leveraging existing expertise for CAVs •
EERE Existing mobility technology RDD&D
3
• •
3) Expanding expertise for future priorities
Collaboration Data infrastructure (hardware, software) Controls (diagnostics, sensor development) Systems modeling (vehicle testing, data collection and analysis)
Wealth of ongoing activity in the CAV space (U.S. DOT, private sector)
EERE Existing Capabilities Technology Offices Vehicles • • • •
Efficiency Improvement Fuel Diversification Domestic & Renewable Reduced GHG
Bioenergy
Hydrogen and Fuel Cells
National goals •& Reduce GHG emissions in the range of 17% by 2 Reduce net oil imports by 50% by 2020 * • Standards Achieve CAFE Standards 54.5 mpg by 2025 •
*Major Administration Goals
4
EERE Existing Capabilities: Current RDD&D Focus • EERE is DOE’s primary applied research office • Research, Development, Demonstration, and Deployment – – – – – – – –
5
Vehicle Electrification Materials Lightweighting Advanced Combustion Drop-in Biofuels Fuel Cell Technology Hydrogen Infrastructure Deployment (e.g., Clean Cities) Grid Systems Integration
Early EERE CAV R&D efforts tie existing expertise Recognizing Key to CAV energy needs Current EERE R&D Energy-Related Needs for Connected Mobility
Existing EERE Core Expertise •
Transportation energy system analysis
•
Vehicle communications and data collection
•
Alternative fuel technologies and systems
•
•
•
•
6
Co-optimization of safety and efficiency • Vehicle re-design (potential significant lightweighting) Interoperability across connected mobility communication systems Alternative fuels enabling potential: enhanced ROI and adoption? Predicting vehicle use systems response • Rebound effect(s) • Increased mobility access
Efforts at the Nexus of Energy and Mobility •
•
Foundational studies on potential energy effects • Synergistic gains from safety initiatives • Fuel efficiency algorithms • Vehicle redesign (lightweighting and aerodynamics) Deeper research in opportunity areas • Advanced vehicle design • Novel diagnostics, controls, and sensor development
Synergies Between Connected Mobility & Energy
7
Autonomy
• Efficient driving • Platooning • Assisted parking
Safety & Collision Avoidance
• Reduced idling • Significant light weighting • Enhanced aerodynamics
Multimodal Transportation
• Lowest carbon trip planning • Automated carpooling
V2X
• Vehicle-to-Vehicle (V2V) • Traffic signal management (V2I) • Grid system integration (V2G)
Data as a Service
• Big data analytics • Efficient routing • Optimizing corridor efficiency
Foundational studies estimate ranges of energy effects Positive Energy Outcomes
Negative Energy Outcomes
Enabling electrification Higher occupancy Lightweighting & powertrain/vehicle size optimization
Less hunting for parking
More travel Faster travel
Full cycle smoothing Travel by underserved Efficient routing Efficient driving Platooning
-1
-0.8
-0.6
-0.4 Use Intensity
-0.2
0
0.2
Energy Intensity
0.4
0.6
0.8
1
Fuel Intensity
Brown, A.; Gonder, J.: Repac, B. (2014). “An Analysis of Possible Energy Impacts of Automated Vehicles.” Chapter 5, Societal and Environmental Impacts. Meyer, G., ed. Lecture Notes in Mobility: Road Vehicle Automation. Berlin: Springer.
8
Potential combined fuel demand increases/decreases by factor category
Foundational studies convey energy effect uncertainty 1 1.1 0
-16%
+30% -0.75
0 -0.75
-1 Use Intensity
Energy Intensity
Fuel Intensity
• Energy impacts can be dramatic – Potential for large improvements in energy and fuel intensity – Increased use intensity may counteract
• Significant uncertainty exists – Total combined impacts from >90% savings to >150% increase in energy use—further research warranted 9
Current efforts inform expanding EERE expertise for future priorities Current EERE R&D Efforts (Foundational studies, deeper research) • •
DOE prioritizing CAV “layers” in which to participate Coordinating across agencies to leverage funding Example:
Possible Future Research •
• •
10
Refine foundation studies on energy impacts • Understanding and reducing uncertainties • Better system interaction modeling • Further energy-focused data collection and analysis Increased collaboration with USDOT • Engage with UMTRI, RITA, NHTSA, ITS America, Non-Profits Continued leveraging of existing expertise • Hardware, software, physical & data infrastructure(s), cyber security • Diagnostics, controls, and sensor development • Systems modeling and vehicle testing • Data collection and analysis
Modeling and Simulation 1. Individual connected vehicle and fleet energy optimization 2. City/corridor connected traffic systems 3. Leveraging model observations for Federal policy implications
Ex 1. Modeling and simulation to optimize connected vehicle/fleet energy use Energy Impact of "Efficient Driving" for Advanced Powertrains Benefits of Connected Route-Based Energy Management for LightDuty Electrified Vehicles Benefits of Connected Route- and Duty-Based Energy Management for P&D PHEV Truck
Virtual Proving Grounds for Development and Evaluation of Energy Efficient CAVs
Energy Impact of CAVs in the context of an Entire Transportation System Benefits of Vehicle Electrification/Automation in the Context of a Large Metro Area Behavior Changes and Adoption under various policies / technologies
Single Vehicle
Small Network of CAVs and ITS 11
Entire Urban Area
Ex 2. Combining models/simulations for city/corridor energy use
Market Penetration / Fleet Definition
ansportation Simulation
Vehicle Energy Consumption
efine network, control & demand 25 20 15 10
25
5 0
20 0
100
200
300
15
400
500
10
40
5 0
0
100 30 200
300
400
500
20 10 0
0
500
1000
1500
2000
2500
Vehicle speed/grade profiles extraction/generatio consumption of n the transportation
Energy network from corridor (~100,000 vehicles) to entire cities (>10M vehicles) 12
Ex 3. Assessing model observations for policy development
Choic e
POLARIS+ Vehicle Ownership Model Level 3
Level 2 Combined Function automation
Level 0 No automationModel
13
Limited self-driving automation
Level4 Full Self-Driving Automation
changes of traveler’s behavior in the context of automation level
VMT
Market Adoption
Choic e
Polic y
Level 1 Function specific automation
Aggrega te
Mode Choice/Shift
Regional or National
Vehicle Ownership Model: Market adoption dynamics based on household characteristics (eg. demographic, existing vehicles ownership, income)
Patrick Davis [email protected]
