A Data-Driven Approach for Optimizing the EV Charging Stations Network

With the exhaustion of oil resources and the aggravation of environmental pollution, electric vehicles, as the main force of new energy consumption, have a more and more promising development prospect. In China, the utilization rate of charging facilities in the public is very low, and there is a large number of redundant charging stations that waste resources. Charging station congestion, meanwhile, is one of the reasons why it is difficult to charge for electric vehicles. This paper proposes a data-driven approach to optimize the existing charging station network by eliminating redundant charging stations, and to identify the charging station congestion areas in the original charging network to provide suggestions for further solving the difficulty of charging electric vehicles. Firstly, we infer that the fine-grained charging situation (consisting of the waiting time and the visiting rate) at different stations. Using a 3D tensor, we model the charging behavior of the electric vehicle, in which the three dimensions represent stations, hours, and days respectively. Secondly, for times and stations with sparse data, we use a context-aware tensor collaborative decomposition method to estimate the situation. For charging stations in a specific period of time, we separately set up a queue system for them to estimate their visiting rate and detect the distribution characteristics of EV charging hotspots in the city. Finally, we introduce a flexible scoring function to evaluate the usage benefits of charging stations and propose a heuristic network expansion algorithm to optimize the network. Applying the data-driven approach to Wuhan city, the results show that using our method can eliminate redundant sites while increasing utilization and find charging station congestion area to guide the government to further charging station planning. Our approach can be adapted for other optimal problems such as chain supermarket layout, public facility planning, and resources configuration using trajectory data.