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Range limitation is a significant obstacle to market acceptance of battery electric vehicles (BEVs). Range anxiety is exacerbated when drivers could not reliably predict the remaining battery range or when their journeys were unexpectedly extended. This paper quantifies the impact of reliable range estimation on BEV feasibility using GPS-tracked travel survey data, collected over an 18-month period (from November 2004 to April 2006) in the Seattle metropolitan area. BEV feasibility is quantified as the number of days when travel adaption is needed if a driver replaces a conventional gasoline vehicle (CGV) with a BEV. The distribution of BEV range is estimated based on the real-world fuel efficiency data. A driver is assumed to choose between using a BEV or a substitute gasoline vehicle, based on the cumulative prospect theory (CPT). BEV is considered feasible for a particular driver if he/she needs to use a substitute vehicle on less than 0.5% of the travel days. By varying the values of some CPT parameter, the percentage of BEV feasible vehicles could change from less than 5% to 25%. The numerical results also show that with a 50% reduction in the standard deviation and 50% increase in the mean of the BEV range distribution BEV feasibility increases from less than 5% of the sampled drivers to 30%. [ABSTRACT FROM AUTHOR]

Data from the New South Wales (NSW) Household Travel Survey (2014/15) was analyzed to determine the trip-by-trip range of automobile travel in NSW. The results show that 88% of trips were less than 30 km, which could readily be provided by electric vehicles, consuming a total of 18 GWh in electrical energy per weekday. Even if all electric vehicles were recharged from non-renewable coal-fired power plants, the greater efficiency of electric vehicles would result in a reduction of greenhouse gas emissions across NSW by 18% carbon dioxide equivalent (CO2(eq)). Additionally, we mapped the average state of charge distribution of the electric vehicles at key times during the day, indicating the maximum net load (for recharging) and/or available energy (for vehicle-to-grid services) across NSW. The results are consistent with other international studies and demonstrate the potential for wide scale electric vehicles adoption in Australia. [ABSTRACT FROM AUTHOR]

Consumer willingness to pay for electric vehicles (EVs) is severely limited by their driving range. The expansion of a charging network could alleviate this dilemma. This paper focuses on determining whether the manufacturer or the dealer is more suitable to extend charging network. In scenario 1 (wholesale price is exogenous), M-Investing (the manufacturer invests on charging stations) better facilitates EV adoption at the early stage of EV market. By contrast, D-Investing (the dealer invests on charging stations) better facilitates the EV adoption when the market becomes mature. However, neither of the two investors have an incentive to offer building investment. In scenario 2 (wholesale price is a decision made by the manufacturer), M-Investing is consistently better than D-Investing in terms of facilitating EV adoption. The manufacturer is voluntary even with high building costs. In addition, we address two subsidy policies: producer subsidy and consumer subsidy, to determine which is more effective in facilitating EV adoption in M-Investing and D-Investing, respectively. Moreover, we extend our model by allowing cost sharing between the manufacturer and the dealer. We observe some cases in which the profit and the EV adoption level are improved. [ABSTRACT FROM AUTHOR]