Impacts of Driving Patterns on the Life Cycle Performance of Plug-in Hybrid Electric Vehicles Leon Raykin Supervisors Heather L. MacLean Matthew J. Roorda MASc Presentation September 7, 2011

Light-Duty Vehicle (LDV) Fleet 2 Sources: Ribeiro et al. (2007); IEA (2010) Transportation fuels used by LDV Fleet Global petroleum energy use Energy-related greenhouse gas (GHG) emissions

Alternative Vehicle Options ICE Wheels Fuel Tank ICE: Internal Combustion Engine Internal Combustion Engine Vehicle (ICEV) Electric Motor ICE Wheels Battery Fuel Tank Parallel Hybrid Configuration Hybrid Electric Vehicle (HEV) Plug-In Hybrid Electric Vehicle (PHEV) Series Hybrid Configuration Electric Motor Battery Wheels ICE Fuel Tank 3

Driving Patterns Driving ConditionsDriving Distance Between Recharging electric-vehicle-charging-equipment 4

Driving Distance (Between Recharging) 5 ICEV HEV PHEV Electric Mode HEV Mode Adapted from Shiau et al. (2009)

Driving Cycles Used to represent driving patterns  Certification driving cycles represent fleet- average driving Exhibit significant regional variation  Specific regional driving cycles can be estimated using travel demand models 6

Life Cycle Assessment Technique for evaluating the environmental performance of a product or process over all stages of its life Life cycle assessment of transportation fuels/vehicles is known as a Well-To-Wheel (WTW) analysis 7

WTW Analysis Well-To-Tank (WTT) Tank-To-Wheel (TTW)

Objectives Paper 1  Apply a travel demand modeling approach to estimate driving cycles for regional driving patterns  Examine impacts of driving patterns on TTW energy use of PHEVs Paper 2  Evaluate implications of driving patterns on the WTW environmental performance of PHEVs:  Total, fossil, and petroleum energy use (MJ/km)  GHG emissions (g CO 2 -eq/km) 9

Paper 1 – Travel Demand Modeling Approach

Travel Demand Modeling Approach - Methods Lake Ontario Max speed, length and congestion Origin- destination demands 1. Travel Demand Data 2. Traffic Assign- ment Driving Cycles 3. Vehicle Motion Modeling PHEVs HEV ICEV PHEVs HEV ICEV 4. Vehicle Selection TTW results 4. Vehicle Simulation Petroleum and electricity use 1.Transportation Tomorrow Survey 2.Emme 3 3.CALMOB6 4.Autonomie Tools Used 11

1 & 2. Travel Demand Modeling Range of driving patterns  Different commute orientations  Constant commute duration 12 Lake Ontario Downtown Toronto Increasing Distance and Speed Increasing Congestion SuburbanHighway City

3. Vehicle Motion Modeling Congestion Complete Stop Partial Stop Reduced Cruise City Driving Cycle Highway Driving Cycle Free Flow 13

4. Vehicle Selection & Simulation Two PHEV designs One HEV One ICEV Vehicles normalized:  Body and tire specifications  Acceleration performance

TTW Petroleum Energy Use Results looks-at-mpg-myths-little-impact-from-tire-pressure/ 15

Average TTW Petroleum Energy Use 16 City Suburban Highway Increasing Distance and Speed Increasing Congestion

TTW Petroleum Savings Relative to ICEV 17 Increasing Distance and Speed Increasing Congestion City Suburban Highway

Paper 1 Summary Applied a travel demand modeling approach to estimate driving cycles for specific regional driving patterns Examined TTW energy use of vehicles for a wide range of driving patterns  Trends in TTW energy use were generally as expected  Both driving distance and driving conditions affect TTW petroleum energy use of PHEVs  Driving patterns have opposite effects on TTW petroleum energy use of PHEVs/HEVs and ICEVs

Paper 2 – WTW Analysis

Gasoline WTW Analysis Methods 20 TTW petroleum & electricity use Hydroelectric Natural Gas Coal Ontario Mix Hydroelectric Natural Gas Coal Ontario Mix Electricity Scenario Selection Energy use and GHG emissions Life Cycle Inventory Analysis WTT Inventory Liquid Fuel Selection WTW Results GREET & Misc. Tools Used

WTW Energy Use Results 21

WTW Total Energy Use 22 Increasing Distance and Speed City Suburban Highway Increasing Congestion -Electric Propulsion -Gasoline Propulsion

WTW Fossil Energy Use 23 Increasing Distance and Speed City Suburban Highway Increasing Congestion

WTW Petroleum Energy Use Increasing Distance and Speed City Suburban Highway Increasing Congestion 24

WTW GHG Emissions Results 25

WTW GHG Emissions 26 Increasing Distance and Speed City Suburban Highway Increasing Congestion

WTT vs TTW 27

Paper 2 Summary Driving patterns substantially affect the WTW performance of PHEVs City favorable over highway for WTW performance of PHEVs Extent to which driving patterns affect WTW performance depends on electricity supply When charging from coal, PHEVs only result in WTW (non-petroleum) energy use and GHG emissions reductions relative to ICEVs due to differences in vehicle fuel efficiency 28

Future Research Directions Further calibration and evaluation of the vehicle motion model Application to other trips and jurisdictions Evaluation of additional metrics and lifecycle activities PHEV scenario analyses using Transportation Tomorrow Survey microdata 29

Conclusions Paper 1  Applied a travel demand modeling approach for estimating driving cycles for regional driving patterns  Evaluated impacts of regional driving patterns on TTW energy use of PHEVs  General trends in TTW energy use were as expected Paper 2  Demonstrated that driving patterns and the electricity generation supply interact to substantially affect the WTW energy use and GHG emissions of PHEVs  Jurisdictions characterized as having favorable electricity generation supply and frequent traffic congestion should be most willing to support PHEVs on basis of energy use and GHG emissions benefits 30

Acknowledgements Heather L. MacLean and Matthew J. Roorda UofT Graduate Students David Checkel and Dan Handford (University of Alberta) Matthew Stevens (CrossChasm)

Questions? 32