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Abstract A novel mobile charging service that utilizes vehicle-to-vehicle (V2V) charging technology has recently been proposed as a supplement to fixed charging infrastructure (CI), enabling electric vehicles (EVs) to exchange electricity. This study formulates a vehicle routing problem (VRP) of vehicle-to-vehicle (V2V) charging, optimizing the routing of discharging vehicles (DVs) to service recharging vehicles (RVs) while taking into account their willingness to join the V2V charging platform. A mixed integer linear programming (MILP) model is established to optimize the VRP-V2V (i.e. the VRP of V2V charging), which is known to be NP-hard. To solve large-scale instances for real-world applications, we propose an adaptive large neighbourhood search (ALNS) algorithm, which, when combined with the structure of the VRP-V2V problem, utilizes four local search procedures to enhance solution quality following destroy-and-repair operators. Results indicate that the proposed ALNS algorithm outperforms the optimization solver CPLEX in small-scale instances, and can solve large-scale instances that are unfeasible using the CPLEX solver. In a numerical analysis of Changsha's large-scale network, we demonstrate that the V2V platform can save an average of 33.1% on the charging cost of RVs, hence raising customer satisfaction with charging services and reducing range anxiety. The platform's profitability is also increased by using V2V charging in areas lacking fixed CI.
Ren et al. (2024) studied this question.