## 8 Conclusions  
In this study, we propose a method in which the shortest path is the primary choice. When it cannot meet the EV user's travel demand, we suggest other paths within certain deviation, which is more consistent with the actual situation. Compared with the traditional planning approach that only considers the shortest path, the proposed method effectively increases the travel success ratio. Moreover, through the optimization method, we manage to simulate the process of users heading for charging stations and obtain the number of the EVs served at each charging station, which leads to a more reliable sizing. For the upper model, we arrange the siting of the charging stations to reach the maximum travel success ratio. For the lower model, a hybrid method based on queuing theory and the greedy algorithm is applied to the sizing of charging stations, and the impact of charging stations on the power grid is also considered. Based on two indicators, the total social cost of charging stations and the satisfaction index, we successfully obtain the optimal planning scheme after evaluating all the feasible solutions. The simulation result verifies the feasibility and rationality of the proposed model, as well as the impact of the siting of the charging stations on travel routes. The FCLM is also applied to the siting of the planning area and the travel success ratio is 80.53% at the maximum flow captured. Based on the FCLM siting result, we take into consideration the trip feasibility and re-evaluate whether EV users could finish their trip successfully. From the evaluation, we obtain a travel success ratio of 54.87%, far below 80.53%, which proves that FCLM is too simple to be consistent with the actual situation. Additionally, we also consider that the co-existence of multiple travel routes are available, which leads to a travel success ratio of 75.22%, indicating that the siting of the charging stations will significantly influence users' travel routes and the travel success ratio. Furthermore, based on the same aim of reaching the maximum success ratio, the planning results vary under different situations. In addition, we study the impact of slow charger coverage in the public parking lot on the fast charging demand and EV users. The result concludes that the higher the slow charger coverage achieves, the lower the fast charging demand is, along with the higher the travel success ratio, which will greatly improve the user's experience. The combination of slow chargers and fast charging stations can effectively make up for the limited maximum travel range of EVs compared to fuel vehicles, which will facilitate the popularization of EVs. With the increase of EVs, the fast charging demand will also raise sharply. In this study, we focus on the fast charging demand of EVs under different growth rates of EVs and different coverage rates of the slow chargers, conclude that large-scale construction of slow chargers in the public parking lot can effectively reduce the charging pressure on the fast charging stations that is brought about by the increase in the number of EVs. Figure 23: The fast charging demand under different slow charging probabilities. We mainly study the siting and sizing of the fast charging stations, and conduct preliminary research on the impact of slow charger coverage on the travel pattern of EV users and the rapid charging demand in public parking lot. Future research will center on the overall planning and research of the multiple charging modes of EVs. [PERSON] and [PERSON] conceived and designed the experiments and wrote the paper; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; and [PERSON] and [PERSON] contributed reagents/materials/analysis tools. The research is supported by National Natural Science Foundation of China (Program No. 51507052), the Fundamental Research Funds for the Central Universities (Program No.