Congestion-aware charging management for ride-hailing systems with time-varying energy prices

Dynamic charging management of electrified ride-hailing services under a stochastic environment is a challenging research issue due to the interplay between vehicles' decisions for serving customers versus charging operations. Existing studies assume constant energy prices and uncapacitated charging stations or do not explicitly consider vehicle queueing at charging stations, resulting in over-optimistic charging infrastructure utilization. This work develops a mixed integer linear program to optimize the sequential decision problems of dynamic ride-hailing systems to maximize the operator's profit under time-varying energy prices. We tested the proposed method on different scenarios using 2019 NYC yellow taxi data. The results show that the proposed methodology can (i) increase the profit and service rate (+7.7% and +7.2%, respectively), (ii) reduce total electricity cost for charging by at least 52.6% compared with the best benchmark approach (100 EVs and 4000 customers/day), (iii) significantly reduce vehicle waiting time at charging stations under a heterogeneous charging station environment.