Shared autonomous electric vehicle service performance: Assessing the impact of charging infrastructure

Abstract

A B S T R A C TShared autonomous vehicles (SAVs) are the next major evolution in urban mobility. This technology has attracted much interest of car manufacturers aiming at playing a role as transportation network companies (TNCs) in order to gain benefits per kilometer and per ride. The majority of future SAVs will most probably be electric. It is therefore important to understand how limited vehicle range and the configuration of charging infrastructure will affect the performance of shared autonomous electric vehicle (SAEV) services. We aim to explore the impacts of charging station placement, charging type (rapid charging, battery swapping) as well as vehicle range onto service efficiency and customer experience in terms of service availability and response time. We perform an agent-based simulation of SAEVs across the Rouen Normandie metropolitan area in France. The simulation process features impact assessment by considering dynamic demand responsive to the network and traffic.Research results suggest that the performance of SAEVs is strongly correlated to the charging infrastructure. Importantly, faster charging infrastructure and optimized placement of charging locations in order to minimize distances between demand hubs and charging stations result in a higher performance. Further analysis indicates the importance of dispersing charging stations across the service area and how this affects service effectiveness. The results also underline that SAEV battery capacity has to be carefully selected to avoid the overlaps between demand and charging peak times. Finally, the simulation results show that by providing battery swapping infrastructure the performance indicators of SAEV service are significantly improved.

Exploring the challenges of electric vehicle adoption in final mile parcel delivery

“…EV batteries have a much lower energy density compared to the fuel of combustion engine vehicles (Keskin and Catay 2016and Vahidi and Sciarretta 2018in Vosooghi et al 2020. This point has been corroborated by Haddadian, Khodayar, and Shahidehpour (2015), who argue that battery technology is not close to the hypothetical boundary for energy density, and that further research is vital for producing lower cost, higher performance batteries.…”

“…Depot-based charging would require investment in charging stations, potentially, fast-charge stations to ensure that sufficient time is provided to charge vehicles between routes (Vosooghi et al 2020). This would combat potential waiting times at charging stations, and the security risks and driver inefficiencies (Morganti and Browne 2018).…”

An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles

“…However, as the critical issue of energy demand due to vehicle dispatching is not considered in these existing methods, they are not applicable to SAEVs. Another line of work that is orthogonal to our paper focuses on planning and investment decisions for SAEV systems, such as charging station positioning and vehicle battery sizing [20][21][22][23][24]. Among them, [21,25] built a multi-agent simulation framework to calculate the battery capacities and number of charging stations to satisfy the requirements of demanded trips, using the same trip data from the city of New York that we used in the present study.…”

Optimal planning of charging stations for an electric vehicle fleet in c ar‐sharing business

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