Highlights • A statistical model of heterogeneity of personal vehicle usage is demonstrated. • Three scenarios studied suggest an optimal average range less than 180 km. • Matching range to individual needs reduces investment in fast-charging facilities. • 240 million BEV with average range of 240 km require only 9000 locations. • Home charging for all vehicles is essential to minimizing infrastructure cost.

Abstract Usage data from fleets of instrumented vehicles in several US cities have been used in numerous studies of the prospective costs, benefits and customer acceptance of battery-electric vehicles (BEV). In turn, the results of these studies can be used to design policies and strategies that promote electrification of personal transportation. Any broader application of the results of these regional studies carries the implicit assumption that vehicle usage in the affected population is similar to that of population selected for the underlying usage study. Given this projection of behavior from one population onto others, replacement of the raw usage data with a statistical representation of the heterogeneity of vehicle usage should be equally valid while reducing elaborate studies of large data sets to just a few equations and spreadsheet calculations. We demonstrate this analytic approach in a study of the trade-off between increasing cost and convenience of incremental all-electric range (AER), and the cost of fast chargers needed whenever range is insufficient for a given day of travel. Three scenarios are considered: (1) A single range was assigned to all vehicles regardless of individual usage, (2) a common tolerance for the frequency of charging away from home was ascribed to all users and the range computed accordingly, and (3) the range that minimized the combined cost of batteries and electricity was computed for each vehicle. All three methods suggest optimal fleet-average range of approximately 175 km. However, both the frequency of visits to fast-charging stations and the energy drawn from those chargers is reduced by allowing users to choose the range that best matches their needs. For example, allowing a choice of range that results in a fleet average range of 200 km in place of a common fixed range of 200 km, the frequency of visits to fast chargers is reduced by 25% and the energy drawn from those chargers is reduced by over 50%. These results suggest that the total cost of universal BEV deployment can be reduced by allowing users to choose the BEV range that matches their needs.