With the development of electric vehicles (EVs), a large number of electric vehicle charging stations (CSs) have been rapidly rolled out to meet the charging demand of EVs. However, high construction costs and long payback periods motivate investigations to improve the profits of CSs. Considering the profits improvement of CSs and carbon emission reductions, this paper first proposes a carbon revenue model for CSs to participate in the carbon trading market. A charging price strategy is proposed to share the carbon revenue with EV users to reduce the charging cost of users, increase the charging income of CSs, and reduce carbon emissions. By describing the EV users' response to the charging price based on fuzzy theory, this paper establishes the charging behavior model of EV users and solves the profits optimization of the dynamic charging price model by particle swarm optimization algorithm (PSO). Finally, the results of the simulation case demonstrate the effectiveness of the proposed strategy. A sensitivity analysis of various grid power purchase prices illustrates the difference between the fixed and dynamic charging price methods.

Nowadays, sustainability-related issues have attracted growing attention due to fossil fuel depletion and environmental concerns. Considering many cities have gradually replaced taxis with electric vehicles (EVs), to reduce greenhouse gas emissions and traditional energy consumption, this paper studies the optimization strategy of battery swapping for electric taxis (ETs), and it is not only to ease congestion in the battery swapping station (BSS) but also for electric taxis to address their range anxiety and maximize their benefits. Firstly, based on the road network, the Dijkstra algorithm is adopted to provide the optimal path for ETs to BSSs with the minimum energy consumption. Then, this paper proposes the optimization objective function with minimum cost, which contains the battery service cost based on the battery’s state of charge, waiting cost caused by waiting for swapping battery in BSSs and the carbon emission reduction benefit generated during ETs driving to BSSs, and uses a mixed-integer linear programming (MILP) algorithm to solve this function. Finally, taking the Leisure Park of Laoshan City in Beijing as an example, the numerical simulation is carried out and the proposed battery swapping strategy is efficient to alleviate the congestion of BSSs and maximize the total benefit of ETs, and the cost based on the proposed strategy is 14.21% less than that of disorderly swapping.