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Electric vehicles (EVs) are being rapidly adopted due to their economic andsocietal benefits. Autonomous mobility-on-demand (AMoD) systems also embracethis trend. However, the long charging time and high recharging frequency ofEVs pose challenges to efficiently managing EV AMoD systems. The complicateddynamic charging and mobility process of EV AMoD systems makes the demand andsupply uncertainties significant when designing vehicle balancing algorithms.In this work, we design a data-driven distributionally robust optimization(DRO) approach to balance EVs for both the mobility service and the chargingprocess. The optimization goal is to minimize the worst-case expected costunder both passenger mobility demand uncertainties and EV supply uncertainties.We then propose a novel distributional uncertainty sets construction algorithmthat guarantees the produced parameters are contained in desired confidenceregions with a given probability. To solve the proposed DRO AMoD EV balancingproblem, we derive an equivalent computationally tractable convex optimizationproblem. Based on real-world EV data of a taxi system, we show that with oursolution the average total balancing cost is reduced by 14.49%, and the averagemobility fairness and charging fairness are improved by 15.78% and 34.51%,respectively, compared to solutions that do not consider uncertainties.

Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems under Demand and Supply Uncertainties