# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 1 Introduction  
With the development of the economy, the scarcity of fossil fuels, and the worsening environmental pollution, electric vehicles have great potential in the electrification of traffic due to their efficiency, cleanliness, and environmental friendliness [1]. At the same time, with the increasing proportion of renewable energy, there is a higher demand for flexibility in the power system [2]. According to the Notice of the State Council General Office on the issuance of the Development Plan for the New Energy Vehicle Industry (2021-2035), by 2035, the core technologies of new energy vehicles will reach internationally advanced levels, especially in terms of integration with energy, traffic, and information network [3]. With the carbon emissions situation, the electricity market requires the participation of resources such as electric vehicles [4]. Electric vehicles (EVs), as traffic vehicles, first and foremost possess traffic attributes, and due to their flexible charging behavior, the impact of EVs with an increasing penetration rate on the operation of the power grid cannot be ignored. Due to the different load characteristics in different areas of the city, EVs have different travel characteristics and charging habits. The travel and charging behaviors of EVs simultaneously impact the operation of both the road network and the power grid, realizing the coupling of the traffic network and the power network at charging station nodes. On the one hand, the distribution of the power grid and road network in the area and thelevel of congestion on road segments determine the travel routes of EVs and the selection of charging stations. On the other hand, the driving and charging behaviors of EVs will simultaneously affect the operation of the power grid and the traffic conditions [5]. In actual operational conditions, the parties involved (EV users, power distribution networks, and traffic networks) often cannot simultaneously maximize benefits or minimize impacts. Due to their individual rationality, EV users make rational route choices based on traffic factors at the time of departure, with their primary goal being to meet their own needs rather than the satisfaction of other participants. Considering the individual rationality of EV users, adopting appropriate strategies to guide EV routes can reduce users' travel time costs while minimizing the impact on the power grid and alleviating traffic pressure. In the description of EV characteristics, a modeling method is proposed in [6] to describe the charging method, geographic location, and travel characteristics. In reference [7], the particle swarm algorithm is used to calculate EV energy consumption and analyze the mutual influence between EVs and the power grid. In [8], an EV charging model is established by combining residential travel survey data, considering variations in electricity consumption, and considering travel chains. [PERSON]. [9] develops an EV travel model that incorporates spatio-temporal distribution based on data such as EV daily driving distance, regional land use, and parking distribution. [PERSON]. [10] considered the uncertainty of EV travel and developed an intelligent charging framework for EVs. [PERSON]. [11] considers multiple factors, including the health status of the battery, in order to calculate the residual driving range of EVs. Based on the [PERSON] decision theory, a unit energy consumption model for EVs and a spatio-temporal distribution model for charging behavior are constructed in [12]. In terms of establishing the traffic network models, [PERSON]. [13] utilizes graph theory to describe the topological structure of road networks and introduces an EV speed-flow model. A static road network model composed of distances is constructed in [14], combining real-time operational data of road networks and charging stations to establish a dynamic road network model that considers travel time.