## 2 Related Work
proposed an adaptive charging and routing strategy that considers driving, waiting, and charging time, minimizing waiting times at charging stations for long-distance trips with electric vehicles by announcing planned charge stops to a central charging station database. They developed a multi-criterion shortest-path search algorithm using contraction hierarchies [27]. [PERSON] et al. proposed a smart scheduling approach for planningcharging stations on expressways based on the A* algorithm with constraint verification and a peer-to-peer scheduling system. By considering the estimated state of charging stations, indirect coordination between electric vehicles is realized, aiming to minimize the total travel time of each electric vehicle [28]. [PERSON] et al. proposed an optimization model that minimizes the total travel distance and energy purchase cost depending on the chosen charging method. Their work considered the choice of different types of charging modes at the charging station, the speed of the electric vehicles, the loaded cargo, and the battery charge. Furthermore, they proposed a preprocessing algorithm to reduce the problem dimension [29]. [PERSON] et al. presented a distributed algorithm to schedule electric-vehicle flows into neighboring charging stations so that electric vehicles are all appropriately served along the highway and that all the charging resources are uniformly used [30]. [PERSON] used a dynamic programming algorithm that takes into account vehicle, road, and charging station parameters to optimize the routing and charging combination for electric vehicles [31]. [PERSON] et al. developed an optimal charging/discharging decision-making and optimal hourly electricity prices problem. It focused on finding proper electric-vehicle-charging stations by electric-vehicle owners and determining optimal hourly electricity prices traded between all agents such that the rewards of electric-vehicle-charging stations and electric-vehicle owners are met simultaneously [32]. [PERSON] et al. developed a reinforcement learning model to tackle the uncertainties in power supply and demand by dispatching a fleet of electric vehicles to supply energy to different consumers at different locations. Each electric vehicle is equipped with various energy resources such as photovoltaic panels and energy-storage units. By sharing power generation and storage units among multiple consumers, the model aims to reduce energy costs for each consumer [33]. [PERSON] et al. considered interactions between the transportation and electric power networks and formulated a multi-objective optimization model to develop optimal routing strategies. The multi-objective component considers the trade-off between travel time and charging cost. The upper level obtains the routing decision by minimizing the function of charging cost and travel time. The routing decision is used in the lower level that solves the AC optimal power flow model, using second-order cone constraints, to determine nodal electricity prices [34]. Table 1 shows the different characteristics in previous works. \"\(\times\)\" represents the characteristic considered by the previous works. As shown in Table 1, research in electric-vehicle path-planning and charging scheduling has been ongoing to reduce charging costs. However, there is still a lack of VANET