An approach to optimizing the layout of charging stations considering differences in user range anxiety

Range anxiety is an important factor affecting the travel quality of electric vehicle drivers, and clarifying the formation mechanism of range anxiety and proposing strategies to eliminate range anxiety are key issues that need to be addressed. Firstly, in view of the fact that most of the current range anxiety studies do not fully consider the impact of the range anxiety differentiation, we establish a model to determine the level of driver range anxiety based on the difference of individual attributes, this model fully considers the individual differences of drivers; Secondly, in order to obtain the charging demand data which is objective and in line with the actual situation, the Monte-Carlo sampling simulation method is introduced, consider the travel law of electric vehicles, and get the charging demand of users with different range anxiety levels based on the self-developed vehicle-road cooperation integration platform, which ensures the authenticity and richness of the data; Finally, in view of the current lack of consideration of both range anxiety and charging demand prediction in charging station planning, a charging station location model with the objective of minimizing the investment cost and user cost of charging stations is constructed, and solve it by using simulated annealing algorithm. And the reasonableness of the model is verified by introducing charging demand satisfaction rate and range anxiety reduction rate. The analysis of the results of the benchmark 25-node traffic test network shows that the coverage rate of charging demand points in the planning area is 96.06%, the average charging demand satisfaction rate of each node is 94.81%, and the average reduction of range anxiety value of different individuals is 33.38%, the optimized charging station location model significantly reduces the drivers' range anxiety. which provides a theoretical basis for the charging demand analysis and charging station location in different scenarios.