Innovations and Energy Implications from Connected/Automated Vehicles and New Mobility Technologies

Transcription

1 Innovations and Energy Implications from Connected/Automated Vehicles and New Mobility Technologies Jeff Gonder, Group Manager Mobility, Behavior and Advanced Powertrains NREL Transportation & Hydrogen Systems Center October 2018

2 NREL is Part of the US DOE s National Lab System 2

3 Scope of NREL Mission Sustainable Energy Energy Transportation EfficiencyProductivity Vehicle & Mobility Technologies Electrification Hydrogen Renewable Electricity Residential Buildings Solar Commercial Buildings Water: Marine Hydrokinetics Manufacturing Geothermal Biofuels Wind Systems Integration Grid Integration of Clean Energy Distributed Energy Systems Batteries and Thermal Storage Partners Private Industry Federal Agencies State/Local Government International Energy Analysis 3

4 Advanced Fueling Infrastructure ENERGY EFFICIENT MOBILITY SYSTEMS PROGRAM INVESTIGATES Connected & Automated Vehicles Advanced R&D Projects MOBILITY ENERGY PRODUCTIVITY THROUGH FIVE EEMS CONSORTIUM ACTIVITY AREAS SMART MOBILITY LAB Smart Mobility Mobility Decision ScienceLab Consortium Urban Science Living Labs Core Evaluation & Multi-Modal Simulation Tools Transport HPC4Mobility & Big Transportation Data Analytics

5 Advanced Fueling Infrastructure Connected & Automated Vehicles (CAVs) Urban Science Click to SMART edit MOBILITY Master title style LAB CONSORTIUM 7 labs, 30+ projects, 65 researchers, $34M* over 3 years. Mobility Decision Science Multi-Modal Transport *Based on anticipated funding

6 Wide Range of National-Level CAVs Impacts Scenarios Partial automation: +/- 10%-15% Full automation: -60% / +200% Ride-sharing: Reduction of up to 12% (No fuel switching or electrification included) Stephens, T.S.; Gonder, J.; Chen, Y.; Lin, Z.; Liu, C.; Gohlke, D. Estimated Bounds and Important Factors for Fuel Use and Consumer Costs of Connected and Automated Vehicles. NREL Technical Report, TP , Nov

7 Upper Bound Scenario Details 7

8 Lower Bound Scenario Details 8

9 Truck Platooning Testing and Analysis Max team savings from multiple scenarios 7% savings for 2 truck platoon team 13% savings for 3 truck platoon team Evaluated question on true baseline of trucks in traffic today 2% individual truck savings following compact SUV 5-9% trailing truck savings at over 140 behind tractor trailer Platooning opportunity analysis Analyzed 210M miles of telematics data >55% of miles platoonable based on speed and partner presence constraints 9

10 Green Routing Methodology Refinement & Validation Energy Estimation Model Refinement & Validation Idaho National Lab collected data with multiple types of vehicles over alternate routes NREL customized energy estimation model sensitive to anticipated segment speeds, grades and turns o Trained by large-scale simulation of validated FASTSim model over TSDC drive cycles, then applied pre-trip Showed conventional vehicle energy estimation model correctly identified the greener route in all of the onroad tests Inbound highway 10

11 ARPA-E NEXTCAR Project with GM & CMU Info-Rich Vehicle Dynamics and Powertrain Controls Eco-Approach o Maximize the kinetic energy recovery through the use of preview information by coordinating vehicle speed control and various powertrain fuel-saving features (DFCO, AFM, gear selection, stop/start, etc.) Eco-Departure o Optimize vehicle departing acceleration profile and powertrain control calibration to maximize efficiency Eco-Cruise o Optimize powertrain operation to maximize efficiency based on look-ahead road grade and traffic conditions Eco-Routing o Select route that minimizes fuel consumption based on vehicle-specific powertrain characteristics without compromising travel time GM = General Motors; CMU = Carnegie Mellon University DFCO = deceleration fuel cut off; AFM = active fuel management 11

12 Automated Mobility District (AMD) Modeling and Analysis An AMD is a campus-sized implementation of connected/automated vehicle technology to realize the full benefits of a fully electric automated mobility service within a confined region or district. 12

13 EV Charging Infrastructure Analyses Using EVI-Pro Travel-data-informed infrastructure placement For conventional and ride-share operating models Comparison with existing /candidate infrastructure locations INRIX Columbus trip data RideAustin trip destinations 13

14 Mobility Energy Productivity (MEP) Metric Quantify Mobility Benefits Relative to Energy Costs A first-of-its-kind, high-resolution, comprehensive accessibility metric that considers energy dependency. The MEP Metric measures the fundamental quality of transportation networks to connect people with goods, services, and employment that define a high-quality of life. Quantifies opportunities reachable within travel time thresholds by different modes, weighted by energy, by cost and by frequency of trip purpose. Have prototyped in multiple cities and is scalable Can compare Two locations (Topeka, Kansas with Chicago) Two planning strategies (roadway extension vs. transit expansion) Two technologies (EV penetration, AV penetration) Driving All Modes Except Driving MEP Metric for Columbus, OH Preliminary Analysis 14

15 Questions? For more information: Jeff Gonder National Renewable Energy Laboratory phone: NREL Transportation Research Website: NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

16 Appendix NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

17 CAV Energy Impacts: Bookending Analyses Potential connected and automated vehicle (CAV) features could have dramatic energy impacts Positive Energy Outcomes Negative Energy Outcomes Enabling electrification Higher occupancy Less hunting for parking Lightweighting & powertrain/vehicle size optimization More travel Faster travel Full cycle smoothing Travel by underserved Efficient routing Efficient driving Platooning Fuel Intensity 0 Energy Intensity Use 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. doi: / _13 17

18 On-Road Data Analysis: Evaluating Automation Impacts on Vehicle Operation and Fuel Consumption Volvo Car Corp (VCC) provided NREL access to a large set of on-road vehicle operating data in adaptive cruise control (ACC) and manually driven (non-acc) modes Developed methodology to assess ACC (partial automation) impacts, with intent to repeat on higher-level vehicle automation under Drive Me From the data NREL derived 17K segments ( 0.5 km in length) of ACC operation and 61K segments of non-acc operation over the test route designated for Drive Me ACC segments showed (statistically significant) smoother overall driving Also examined ACC vs. non-acc fuel consumption differences found to vary with traffic speed and road grade. Segments of contiguous ACC operation on the Drive Me test route Non-ACC segments acceleration standard deviation is significantly higher than for ACC segments 18

19 Weighting Differences by Overall Travel on the Network Speed Bins (kmph) In some conditions ACC fuel use >10% lower, in others no difference Applied methodology to estimate volume of travel in different speed and grade conditions experienced on the road network Calculated overall ACC impact by weighting the relative ACC vs. non-acc fuel consumption rates in each driving condition by the amount of driving that occurs in each condition Overall: 5%-6% lower ACC vehicle fuel consumption (in mostly non-acc traffic) VKT (unit: million) (0, 10] (10, 20] (20, 30] (30, 40] (40, 50] (50, 60] (60, 70] (70, 80] (80, 90] (90, 100] (100, 110] % Grade Bins (-5, -4] (-4, -3] (-3, -2] (-2, -1] (-1, 0] (0, 1] (1, 2] (2, 3] (3, 4] (4, 5] Zhu, L., J. Gonder, E. Bjarkvik, M. Pourabdollah and B. Lindenberg, An Automated Vehicle Fuel Economy Benefits Evaluation Framework Using Real-World Travel and Traffic Data. To be published in the IEEE Intelligent Transportation Systems Magazine Special Issue on Emerging Mobility Systems. 19

20 Bottom-Up Approach to Explore Nuanced Scenarios Quantify different CAV feature fuel economy impacts in different driving situations Consider the relative proportion of national VMT represented by each driving situation Aggregate weighted results for national-level impact, making A/B comparisons for fuel use with or without a given technology active 20