The rapid shift from internal combustion engine vehicles to battery-powered electric vehicles (EVs) presents considerable challenges, such as limited charging points (CPs), unpredictable wait times, and difficulty selecting appropriate CPs. To address these challenges, we propose a novel end-to-end framework called Stable Matching EV Charging Assignment (SMEVCA) that efficiently assigns charge-seeking EVs to CPs with assistance from roadside units (RSUs). The proposed framework operates within a subscription-based model, ensuring that the subscribed EVs complete their charging within a predefined time limit enforced by a service level agreement (SLA). The framework SMEVCA employs a stable, fast, and efficient EV-CP assignment formulated as a one-to-many matching game with preferences. The matching process identifies the preferred coalition (a subset of EVs assigned to the CPs) using two strategies: (1) Preferred Coalition Greedy (PCG) that offers an efficient, locally optimal heuristic solution and (2) Preferred Coalition Dynamic (PCD) that is more computation-intensive but delivers a globally optimal coalition. Extensive simulations reveal that PCG and PCD achieve a gain of 14.6% and 20.8% over random elimination for in-network charge transferred with only 3% and 0.1% EVs unserved within the RSUs vicinity.

翻译：从内燃机汽车向电池驱动电动汽车的快速转型带来了诸多挑战，例如充电桩数量有限、等待时间难以预测以及选择合适的充电桩困难等。为应对这些挑战，我们提出了一种名为稳定匹配电动汽车充电分配的新型端到端框架，该框架在路边单元的辅助下，将寻求充电的电动汽车高效地分配至充电桩。所提出的框架在订阅模式下运行，确保已订阅的电动汽车在服务等级协议规定的时间限制内完成充电。SMEVCA框架采用一种稳定、快速且高效的电动汽车-充电桩分配方案，该方案被建模为带偏好的一对多匹配博弈。匹配过程通过两种策略识别首选联盟：首选联盟贪婪算法，提供一种高效的局部最优启发式解；以及首选联盟动态算法，其计算强度更大但能提供全局最优联盟。大量仿真结果表明，在网络内传输的充电量方面，PCG和PCD相较于随机淘汰策略分别实现了14.6%和20.8%的性能增益，且在RSU覆盖范围内仅有3%和0.1%的电动汽车未得到服务。