A bi-decision model for electric vehicle dispatch and battery swapping station charging schedule problem.

The blooming population and advanced technology of electric vehicles (EVs) have promoted the wide studies on battery swapping station (BSS) models. However, current BSS researches only focus on isolated decision models, such as the dispatching model or charging schedule model, which cannot represent the realistic situation and obtain the optimal solution for both the EV drivers and BSS operators. In this paper, a bi-decision model for EV dispatch and BSS charging schedule problem is proposed to minimize the average extra time (ET) through the assigned BSS for EVs, and optimize the electricity cost, charging damage to batteries, and power load variance for BSSs, where the solution in the first decision is the pre-defined condition of the second decision model. Knowing that two models are both Non-deterministic Polynomial-time hard (NP-hard) problems, two types of evolutionary algorithms are proposed. In the first model, an adaptive tabu search (ATS) algorithm is proposed by formatting the EVs' ET, the number of batteries, and queuing EVs at BSSs. In the second model, a multi-objective particle swarm optimization (MOPSO) algorithm is proposed to obtain the Pareto set of the complicated scheduling problem. Experiments are carried out to investigate the viability of the bi-decision model by comparing it with rule-based strategies, such as nearest-in-range. Also, the waiting times in the first decision and the scheduling results are illustrated in the Gantt charts. Lastly, a comprehensive comparison between the proposed ATS algorithm and the MOPSO algorithm is presented to show the effectiveness and competitiveness.