  As electric vehicle (EV) technologies become mature, EV has been rapidly
adopted in modern transportation systems, and is expected to provide future
autonomous mobility-on-demand (AMoD) service with economic and societal
benefits. However, EVs require frequent recharges due to their limited and
unpredictable cruising ranges, and they have to be managed efficiently given
the dynamic charging process. It is urgent and challenging to investigate a
computationally efficient algorithm that provide EV AMoD system performance
guarantees under model uncertainties, instead of using heuristic demand or
charging models. To accomplish this goal, this work designs a data-driven
distributionally robust optimization approach for vehicle supply-demand ratio
and charging station utilization balancing, while minimizing the worst-case
expected cost considering both passenger mobility demand uncertainties and EV
supply uncertainties. We then derive an equivalent computationally tractable
form for solving the distributionally robust problem in a computationally
efficient way under ellipsoid uncertainty sets constructed from data. Based on
E-taxi system data of Shenzhen city, we show that the average total balancing
cost is reduced by 14.49%, the average unfairness of supply-demand ratio and
utilization is reduced by 15.78% and 34.51% respectively with the
distributionally robust vehicle balancing method, compared with solutions which
do not consider model uncertainties.
