California Statewide Charging Assessment Model for Plug-in Electric Vehicles: Learning from Statewide Travel Surveys

Electric vehicle travel and charging was simulated using gasoline vehicle travel information from approximately 15,000 households in the CalTrans 2001 California Statewide Travel Survey. Ranges of 60, 80, and 100 miles were simulated to investigate the travel that could not be completed with home charging alone. Different types of chargers including workplace level 1 and level 2 chargers, level 2 public chargers, and DC quick chargers were then posited to determine the effect of each charging type on electric vehicle miles traveled (eVMT). If all statewide vehicle were 80 mile range battery electric vehicle (BEVs) and began the day with a full charge, 71% of miles (95% of home-based tours) are possible with home charging alone. Travel that requires some charging accounts for a corresponding 29% of miles (5% of tours). Workplace charging can enable about 7% more eVMT, public level 2 at stops greater than 1.5 hours could provide an additional 4% of eVMT, and quick charging could provide an additional 12% of eVMT. 6% of eVMT (0.6% of tours) would be difficult to complete in an 80 mile range BEV. 200 DC fast locations could provide an initial network to serve most Californians with the number of chargers growing past 200 to handle congestion at charging areas. Scenarios for plug-in hybrid electric vehicles (PHEVs) show that for a 30 mile range PHEV, 61% of miles could be completed with home charging alone.

[1]  Jonn Axsen,et al.  Anticipating plug-in hybrid vehicle energy impacts in California: Constructing consumer-informed recharge profiles , 2010 .

[2]  Constantinos Antoniou,et al.  Spatial Exploration of Effective Electric Vehicle Infrastructure Location , 2012 .

[3]  Jamie Davies,et al.  DC Fast as the Only Public Charging Option? Scenario Testing from GPS-Tracked Vehicles , 2012 .

[4]  A. Schroeder,et al.  The economics of fast charging infrastructure for electric vehicles , 2012 .

[5]  Christopher Yang,et al.  Determining marginal electricity for near-term plug-in and fuel cell vehicle demands in California: Impacts on vehicle greenhouse gas emissions , 2009 .

[6]  Diego Klabjan,et al.  An agent-based decision support system for electric vehicle charging infrastructure deployment , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[7]  Jeremy J. Michalek,et al.  Cost-effectiveness of plug-in hybrid electric vehicle battery capacity and charging infrastructure investment for reducing US gasoline consumption , 2013 .

[8]  Christopher Yang,et al.  Plug-in Hybrid Vehicle GHG Impacts in California: Integrating Consumer-Informed Recharge Profiles with an Electricity-Dispatch Model , 2011 .

[9]  Stanton W. Hadley,et al.  Potential Impacts of Plug-in Hybrid Electric Vehicles on Regional Power Generation , 2009 .

[10]  Jee Eun Kang,et al.  An activity-based assessment of the potential impacts of plug-in hybrid electric vehicles on energy and emissions using 1-day travel data , 2009 .

[11]  P. Hines,et al.  Spatial Analysis of Travel Demand and Accessibility in Vermont: Where will EVs work? , 2012 .

[12]  Tomohiko Ikeya,et al.  A Design system for layout of Charging infrastructure for Electric Vechicle , 2012 .

[13]  Budhendra L. Bhaduri,et al.  A Multi Agent-Based Framework for Simulating Household PHEV Distribution and Electric Distribution Network Impact , 2010 .