Multi-day scenario analysis for battery electric vehicle feasibility assessment and charging infrastructure planning

Abstract

Multi-day activity-travel patterns help create potential vehicle usage profiles that contain vehicle operations and battery status under different scenarios with varying location-based charging opportunities, based on travel needs and charging availability/behaviors. Utilizing a multi-day data sampling method, analyses of scenarios are designed to provide insights on bounds of potential BEV market under different charging opportunities, including level 2 activity charging and level 3 trip charging. Single-day data results tend to overestimate travelers' BEV feasibility assuming that multi-day sample data provides accurate estimations. Facility utilization can be improved without affecting travelers' charging demand under correct pricing scheme for most cost-sensitive users. Smart grid charging strategy can greatly reduce the total number of operating chargers during the same time in a day, and BEV users' charging behaviors have minor impact on this improvement. Our numerical results indicate that an appropriate number of chargers installed in shopping and leisure locations should be more profitable and have higher charger utilization rate since those chargers help cover BEV users' trips.