ABSTRACT
A reliable-charging network is urgently demanded to support electrified ride-sourcing services due to their shorter dwell time, longer daily vehicle miles traveled, and concerns of sacrificing revenue for charging activities. We developed an integer programming (IP) model for the optimal allocation of charging stations and charging plugs to minimize the total investment costs and spatio-temporal varying drivers’ value of time (VOT) for charging activities. The trip chain data of the RideAustin ride-sourcing services have been used as a test case, based on which we estimated the charging needs of ride-sourcing EVs and identified candidate charging locations to fulfill the daily travel needs of ride-sourcing drivers. Through numerical study and sensitivity analyses, we analyze the impacts of different charger types, fleet sizes, government incentives, and VOT considerations on the optimal investment plans and system costs, and show the importance of considering ride-sourcing drivers’ VOT into charging infrastructure planning.
Acknowledgments
The efforts of Md Rakibul Alam were partially supported by the University of Central Florida ORC fellowship. We thank Tadeas Lobreis for his efforts on processing the input data from RideAustin for the purpose of this study.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1. We assume 20% SOC as a threshold to avoid excessive discharge, which may cause permanent battery capacity loss (BatteryUniversity Citation2021). In addition, ride-sourcing vehicles may need to maintain an energy buffer in case of emergency.
2. According to https://www.linkedin.com/pulse/what-you-need-know-ev-charging-station-maintenance-nolan-rutschilling/, Level 1/2 chargers have an expected lifespan of 10 years. However, DCFC requires more maintenance compared with Level 1/2 chargers. Therefore, we allocate initial capital costs to a 5-year planning horizon for a more conservative estimation.