  This paper presents a modeling and design optimization framework for an
Electric Autonomous Mobility-on-Demand system that allows for ride-pooling,
i.e., multiple users can be transported at the same time towards a similar
direction to decrease vehicle hours traveled by the fleet at the cost of
additional waiting time and delays caused by detours. In particular, we first
devise a multi-layer time-invariant network flow model that jointly captures
the position and state of charge of the vehicles. Second, we frame the
time-optimal operational problem of the fleet, including charging and
ride-pooling decisions as a mixed-integer linear program, whereby we jointly
optimize the placement of the charging infrastructure. Finally, we perform a
case-study using Manhattan taxi-data. Our results indicate that jointly
optimizing the charging infrastructure placement allows to decrease overall
energy consumption of the fleet and vehicle hours traveled by approximately 1%
compared to an heuristic placement. Most significantly, ride-pooling can
decrease such costs considerably more, and up to 45%. Finally, we investigate
the impact of the vehicle choice on the energy consumption of the fleet,
comparing a lightweight two-seater with a heavier four-seater, whereby our
results show that the former and latter designs are most convenient for low-
and high-demand areas, respectively.
