## 1 Introduction  
Growing concern for the environment and advancements in autonomous technology have increased the popularity of autonomous electric vehicles (AEVs) [1; 2; 3]. AEVs are a type of intelligent vehicle, considered to be a higher-level electric vehicle [4]. They are powered by batteries, making them an eco-friendly alternative to conventional fuel vehicles and helping to reduce pollution and resource depletion [5]. Moreover, equipped with an intelligent computer system, AEVs can operate autonomously, making them a potential replacement for human drivers in the future [6]. This feature enables AEVs to achieve distributed decision-making, mitigating the complexity of data processing and time delays caused by centralized distribution, and avoiding the impact of driver self-interest behavior on global optimization. However, as the battery charge gradually decreases during movement [7], pure path optimization is insufficient to guarantee arrival at a specific destination. **State-of-the-art** To ensure sufficient power consumption during travel to a specific destination, AEVs should consider path optimization with charging solutions [8]. Researchers have studied the charging guidance of electric vehicles from the information flow interaction between electric vehicles and charging station operators. For instance,Liu et al. [9] proposed a route-optimization model to reduce user travel time and charging costs based on real-time charging prices and traffic conditions. [PERSON] et al. [10] established a charging path-planning model to minimize the travel time cost and charging cost of electric vehicle (EV) users based on real-time traffic information. [PERSON] et al. [11] proposed a path-planning and charging navigation strategy for electric vehicles based on real-time traffic information. They developed a multi-objective optimization function that integrates road travel time, charging station load, and the number of incoming vehicles. They used an improved adaptive Dijkstra dynamic search algorithm to solve the problem and recommend the optimal driving and charging path. Considering the combination of transportation network and power network characteristics, [PERSON] et al. [12] proposed a novel optimal charging scheduling strategy based on transport system information, such as road length, vehicle velocity, and waiting time, and grid system information, such as load deviation and node voltage, which improves both the transport system efficiency and the grid system operation. Most studies have focused on planning charging paths for electric vehicles using the interaction between EVs and the transportation network, with very few studies examining path and charging optimization for electric vehicles considering traffic conditions and the dynamic operation of charging stations. **Motivation:** Dynamic optimization is crucial in adapting to time-varying traffic conditions and charging infrastructure status in complex transportation networks. To ensure the accuracy of dynamic optimization, real-time traffic and charging infrastructure service status information is essential [13]. Vehicle-to-everything (V2X) communication in vehicular ad hoc networks (VANETs) enables vehicles to exchange real-time information with other vehicles, roadside units (RSUs), and the power grid (charging infrastructure) via vehicle-to-vehicle (V2V), vehicle to roadside unit (V2R), and vehicle-to-grid (V2G) technologies. With the development of dynamic wireless power transmission technology, electronic coils can be installed on roads to create energy segments (ESs), which use electromagnetic induction to provide charging for electric vehicles. This means that AEVs can receive charging services not only from traditional charging stations (CSs) but also from ESs. Motivated by the realization of dynamic path optimization and charging selection, it is essential to establish a real-time communication mode to obtain real-time traffic conditions and charging infrastructure service status information.