Research on the impact of intelligent battery charging and swapping cabinets on distribution networks: a predictive model based on urban traffic conditions

Abstract

Abstract The ownership of electric bicycles has rapidly increased, which has led to the emergence of intelligent charging and swapping cabinets, eliminating the need for users to charge their electric bicycles at charging cabinets. Instead, they can simply swap the batteries at the charging and swapping cabinets, thereby improving efficiency. However, the charging process within these cabinets also impacts the power distribution grid. Therefore, this paper proposes a charging load prediction model based on urban road conditions and dynamic algorithms. The model combines matrix analysis and dynamic search using the Dijkstra algorithm to simulate electric bicycle driving. The model can navigate around congested roads, reflect the impact of traffic conditions on electric bicycles’ speed and power consumption, and consider the effects of driving, battery consumption, and charging cabinets on the power distribution grid during the charging process. This more accurately reflects the real-time interaction between dynamic driving and traffic conditions. The predicted results also illustrate the regional and temporal variations in the demand for electric bicycle loads, as well as the impact of charging loads on the voltage at different nodes of the power distribution grid.