Optimal battery replenishment and vehicle-charger assignment for dynamic electric demand responsive transport service

Electrifying electric vehicle fleet for demand-responsive transport (DRT) service needs to eciently 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 the DRT service. A new battery replenishment model is proposed to obtain a day-ahead charging schedule for each vehicle based on the historical driving patterns of vehicles.
Then an online vehicle-charger assignment model is developed to determine where to charge by considering queuing delays at the level of chargers. An ecient 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 dial-aride service case study in Luxembourg and compared with the nearest charging station charging policy and first-come-first-served minimum charging delay policy under dierent 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.