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This paper compares two prevalent charging strategies for electric vehicles,plug-in charging and battery swapping, to investigate which charging strategyis superior for electric autonomous mobility-on-demand (AMoD) systems. To thisend, we use a queueing-theoretic model to characterize the vehicle waiting timeat charging stations and battery swapping stations, respectively. The model isintegrated into an economic analysis of the electric AMoD system operated by atransportation network company (TNC), where the incentives of passengers, thecharging/operating shift of TNC vehicles, the operational decisions of theplatform, and the planning decisions of the government are captured. Overall, abi-level optimization framework is proposed for charging infrastructureplanning of the electric AMoD system. Based on the proposed framework, wecompare the socio-economic performance of plug-in charging and batteryswapping, and investigate how this comparison depends on the evolving chargingtechnologies (such as charging speed, battery capacity, and infrastructurecost). At the planning level, we find that when choosing plug-in charging,increased charging speed leads to a transformation of infrastructure fromsparsely distributed large stations to densely distributed small stations,while enlarged battery capacity transforms the infrastructure from denselydistributed small stations to sparsely distributed large stations. On the otherhand, when choosing battery swapping, both increased charging speed andenlarged battery capacity will lead to a smaller number of battery swappingstations. At the operational level, we find that improved charging speed leadsto increased TNC profit when choosing plug-in charging, whereas improvedcharging speed may lead to smaller TNC profit under battery swapping. The aboveinsights are validated through realistic numerical studies.

Charging Autonomous Electric Vehicle Fleet for Mobility-on-Demand Services: Plug in or Swap out?