## 2 Related Work  
There is a significant body of literature on the path-planning and charging scheduling problem. Recent works have explored the use of VANETs to transmit traffic information and aid electric vehicles in making travel decisions. For example, [PERSON] et al. proposed vehicle-to-everything communication based on VANETs to enable transmission between electric vehicles and mobile edge computing servers for V2V charging navigation [14]. [PERSON] et al. developed an intelligent power distribution system that used VANETs to enable communication among electric vehicles, RSUs, and a vehicle-traffic server, and proposed a globally optimal online electric-vehicle-charging strategy [15]. However, these centralized approaches can be computationally expensive, particularly in complex traffic networks. [PERSON] et al. proposed communication based on clustering vehicular ad hoc networks for electric vehicles (EV-VANET), in which a cluster leader collected and maintained information for cluster members and transmitted information between adjacent clusters and RSUs [16]. However, this work did not consider real-time information transmission. Recent research has focused on the path-planning and charging scheduling problem for electric vehicles, given their limited battery capacity. [PERSON] et al. studied en-route charging navigation in both deterministic and stochastic traffic networks, formulating it as a dynamic programming problem [17]. They have also proposed simplified charge control and online state recursive algorithms to solve the deterministic and stochastic cases, respectively. Moreover, [PERSON] et al. designed a joint routing and charging/discharging algorithm to minimize the total cost of electric vehicles, taking into account driving cost, electricity purchase cost, and battery degradation cost [18]. Another study by [PERSON] et al. proposed an optimal charging station assignment and navigation plan to coordinate charging scheduling for electric vehicles. Using combined distributed biased min consensus algorithm and generalized benders decomposition algorithm to solve the complex nonlinear optimization problem [19]. However, conventional charging stations may be limited and inconvenient, particularly for AEVs with time anxiety. To address this issue, vehicular energy networks have emerged as a promising solution, using ESs with dynamic wireless power transmission capabilities to offer convenient energy charging services for AEVs while driving without the need to wait [20; 21]. [PERSON] et al. proposed a decentralized optimization framework for wireless charging stations to address the interdependence between electricity and transportation networks [22]. The optimization of travel path-planning and charging station selection for electric vehicles has become a highly researched topic in recent studies. [PERSON] et al. proposed a planner for EVs by incorporating traveler behavior and energy consumption into the evaluation process by introducing a novel framework for the network design problem [23]. [PERSON] et al. proposed a framework for V2V communication protocol to achieve context-aware path-planning, optimized for time and energy efficiency, as well as the best detour to the charging point in the case of insufficient energy [24]. However, their work assumed that each recharging point was always available, without considering the service status information of the recharging point. [PERSON] et al. proposed a route selection and charging navigation optimization model to reduce the travel cost of electric-vehicle users and solved waiting-time calculations of charging stations based on queue theory [25]. [PERSON] et al. proposed methods for charging warning and path-planning for electric vehicles with insufficient energy, analyzing energy consumption in real time and constructing network topology models of waiting time, queuing mechanism, and charging calculation [26]. [PERSON] et al.