# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 6 Conclusions  
This paper takes into account that the driving behavior of EVs is greatly influenced by road travel time and utilizes it to guide EV routing. A real-time road network and road impedance model based on graph theory methods are established to describe the spatiotemporal characteristics of EVs. The EV travel chains are classified based on the distribution of the road network regions, and EV routing is planned using [PERSON]'s shortest path algorithm. The results show that  
* Compared to the shortest distance path without guidance, the EV path under the traffic impedance guidance strategy in this paper can significantly reduce the travel energy consumption and time cost by approximately 18% with only a small increase in distance cost. This can be applied in subsequent research on improving user travel planning satisfaction.
* The strategy proposed in this paper can achieve a more balanced spatio-temporal distribution of charging load to alleviate the pressure on the regional power grid. This method can improve the degree of voltage drop in the power grid caused by EV charging behavior within the safety requirement of 7%. By analyzing different levels of EV penetration, it is evident that the future significant increase in EV penetration will pose greater challenges to grid operations. The research findings of this paper will provide a basis for addressing these risks.
* The guiding strategy presented in this paper can achieve a more balanced spatio-temporal distribution of vehicles triggering charging demands while reducing road congestion around charging stations and alleviating traffic congestion. With the continuous rapid growth in the number of EVs in the future, it will also bring various impacts to the transportation system. The findings of this paper can serve as one of the fundamental methods to mitigate adverse effects such as traffic congestion.  
This study still has some limitations, such as the characterization of user costs only considering time and energy costs and insufficient emphasis on the load characteristics of EV users in different regions. To address the aforementioned issues, in future research, we will conduct a study on differentiated user characteristics and incorporate charging station service fees, among other factors, to perform comprehensive cost calculations that include users' economic costs.  
Conceptualization, J.L. and S.T.; methodology, J.L. and Z.W.; software, S.T.; validation, J.L. and S.T.; formal analysis, S.T.; investigation, N.Z.; resources, G.L.; data curation, J.L.; writing--original draft preparation, S.T.; writing--review and editing, W.L., J.L. and Z.W.;  
Figure 18: Average road impedance around each charging station at different EV penetration rates.  
visualization, J.L. and G.L.; supervision, Z.W. and N.Z.; project administration, J.L. and S.T.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jiebangguashuai project of Inner Mongolia, grant number 2022 JBGS0043.  
Not applicable.  
The data presented in this study are available through request to the corresponding author.  
The authors declare no conflict of interest.