Vehicular Ad-hoc Networks (VANETs) provide key support for the achievement of intelligent, safe, and efficient driverless transportation systems through real-time communication between vehicles and vehicles, and vehicles and road infrastructure. This paper investigates a joint optimization problem of electric vehicles (EVs) charging management and resource allocation based on VANETs. EV charging requires significantly more time than refueling conventional vehicles, a key factor behind people's reluctance to transition from internal combustion engine vehicles to EVs. Previous works have primarily concentrated on fully-charged vehicles and random matching, which does not solve the problems of vehicle charging delays and long customer waiting times. Considering these factors, we propose a distributed multi-level charging strategy and level-by-level matching method. Specifically, EVs and passengers are categorized into classes based on battery power and target mileage. Vehicles are then allocated to customers in the same or lower levels. Furthermore, the Attentive Temporal Convolutional Networks-Long Short Term Memory (ATCN-LSTM) model is leveraged to predict historical traffic data, supporting anticipatory decision-making. Subsequently, we develop a hierarchical charging and rebalancing joint optimization framework that incorporates charging facility planning. Experimental results obtained under various model parameters exhibit the method's commendable performance, as evidenced by metrics such as operating cost, system response time, and vehicle utilization.

中文翻译：

---

基于 VANET 的电动汽车充调度优化


车辆自组网 （VANET） 通过车与车、车与道路基础设施之间的实时通信，为实现智能、安全、高效的无人驾驶交通系统提供关键支撑。本文研究了基于 VANET 的电动汽车 （EV） 充电管理和资源分配的联合优化问题。电动汽车充电比传统汽车需要更多的时间，这是人们不愿意从内燃机汽车过渡到电动汽车的关键因素。以前的工作主要集中在充满电的车辆和随机匹配上，并没有解决车辆充电延迟和客户等待时间长的问题。考虑到这些因素，我们提出了一种分布式多级充电策略和逐级匹配方法。具体来说，电动汽车和乘客根据电池电量和目标里程分为几类。然后，车辆将分配给相同或更低级别的客户。此外，利用注意力时间卷积网络-长短期记忆 （ATCN-LSTM） 模型来预测历史流量数据，支持预期决策。随后，我们开发了一个分层充电和再平衡联合优化框架，其中包括充电设施规划。在各种模型参数下获得的实验结果显示了该方法值得称道的性能，运营成本、系统响应时间和车辆利用率等指标证明了这一点。