Battery Electric Vehicles: Range Optimization and Diversification for the U.S. Drivers

The driving range of battery electric vehicles (BEV) is often determined by the constraint of vehicle price competitiveness or similarity to conventional vehicles. These two directions are mutually conflicting, which suggests the need for range optimization with respect to individual consumer's charging availability, driving patterns, and range anxiety. This study proposes a coherent framework to optimize the driving range by minimizing the range-related costs, including upfront battery cost, charging availability, access to a backup vehicle, and the costs and refueling hassle of the backup vehicle. This optimization method is applied to a sample of 36664 vehicles, derived from the 2009 National Household Travel Survey data and used to represent the U.S. new car drivers. Results include: 1) the distribution of non-optimality loss, which is caused by marketing only one range type of BEVs and indicates the significance range optimization and diversification; 2) the mean and standard deviation of the optimal BEV ranges; 3) the sample distribution of optimal BEV ranges; 4) the approval ratings for a selected range types; and 5) the sensitivity analysis of the approval ratings with respect to energy prices, battery costs, and charging availability.