# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 4 Electric Vehicle Route Planning Methods  
First, establish a coupled operation architecture between the power distribution network and the transportation network, as shown in Figure 5. In power-traffic coupling network, a simulation control platform is established to facilitate strategy deployment and interaction between entities, enabling the flow of information between the user layer and the system layer. In the user layer, EV users participating in route planning provide the platform with vehicle battery parameters, travel time, O-D points, and other data. In the system layer, the power distribution network provides information on power flow distribution and load conditions at each node, while the transportation network provides information on road network geography and congestion levels at different time intervals. The control platform aggregates and calculates the received information to minimize user time costs. It then sends the optimal route planning results to the user layer, taking into account strategies that simultaneously reduce the impact of EVs on the power distribution network, alleviate traffic congestion caused by concentrated EV charging, and balance the spatial and temporal distribution of EV charging loads on the power distribution and transportation networks. Through platform information exchange and guidance on EV users' travel and charging behaviors, the coupled operation of the power distribution network and transportation network is achieved. The large-scale travel behavior of EVs directly affects the operation of the road network. Depending on the travel routes, the electricity consumption and charging behavior of EVs can change, thus simultaneously affecting traffic congestion levels and power grid flow. The rational planning of travel routes and mid-journey charging paths plays a crucial role in the coupled operation of the power distribution network and transportation network. The most commonly used path planning algorithm is [PERSON]'s algorithm, which calculates the optimal path. However, the traditional [PERSON]'s algorithm is based on fixed road network data, and the calculated actual time cost and energy consumption may deviate from the actual driving conditions. In other words, the traditional [PERSON]'s algorithm is only suitable for static road networks where the limiting factors on vehicle movement, such as road length, do not change over time. It is suitable for path planning with the objective of finding the shortest distance in unguided scenarios. In the process of path traversal, the travel time and energy consumption of each road segment are related to the traffic flow at that moment. Considering the limitations of the traditional [PERSON]'s algorithm in time-guided scenarios, the improved planning process is as follows:  
* Data collection: Collect EV's departure time, OD points, battery level, as well as real-time operational data of the road network, such as traffic volume and congestion level. Figure 5: Power-traffic coupling network operation framework. * Cost calculation: Modify the cost calculation in the traditional [PERSON]'s algorithm by incorporating real-time road congestion data obtained from the road network. With this improvement, the algorithm no longer solely relies on static road segment lengths but also takes into account factors such as time and traffic conditions. This allows for a more accurate estimation of travel time and energy consumption. * Dynamic routing: By utilizing the improved path planning algorithm, dynamic paths can be computed based on varying traffic conditions and EV parameters. Users can obtain the estimated travel time and energy consumption for the corresponding paths. * Charging Station Selection: Incorporating the location and capacity of charging stations into the planning process. Solving for the optimal intermediate charging path based on the remaining battery charge and the availability of charging stations along the route.