Optimal queueing-based vehicle charging scheduling and assignment for dynamic electric ridepooling service

Electrifying electric vehicle fleet for ride-hailing service needs to efficiently manage the daily charging operation to minimize charging waiting times and cost under stochastic customer demand. Given capacitated charging facilities and varying charging prices, the issue of determining where, when, and how much to charge for each vehicle is a challenging issue for successful deployment of e-fleet. In this study, a new two-stage solution approach is proposed to handle dynamic vehicle charging scheduling to minimize the costs of daily charging operations of pooled ride-hailing service. A new vehicle charging scheduling model is proposed to obtain a day-ahead charging schedule for each vehicle based on the historical driving patterns of vehicles. Then an online queueing-based vehicle–charger assignment model is developed to determine where to charge by considering queueing delays at the level of chargers. An efficient Lagrangian relaxation algorithm is proposed to solve the large-scale vehicle-charger assignment problem with small optimality gaps. The approach is applied to a realistic dynamic microtransit service case study in Luxembourg and compared with the nearest charging station charging policy and first-come-first-served minimum charging delay policy under different charging infrastructure scenarios. The computational results show that the approach can achieve significant savings for the operator in terms of charging waiting times, charging times, and charged energy costs.