Abstract:

Consumers have substantially favored electric vehicles (EVs) in recent years. However, long charging times, high battery purchase costs (BPC), and large charging loads limit EV development. The battery swapping scenario could solve the above problems well. This paper presents a battery centralized scheduling strategy (BCSS) in the battery swap scenario, significantly reducing BPC and battery charging peak load (BCPL). Based on the presented BCSS, an optimization model of BPC considering BCPL is established. Then, the genetic algorithm (GA) is used to drive the optimization process, and 1000 private battery swaps EVs are employed to simulate the BCSS in the prescribed battery swapping scenario. The optimization results suggest that the BPC of the BCSS is 4.15 million yuan, and the BCPL is 2418 kW. In the same situation, compared with the equal time interval transportation strategy (ETITS) and the equivalent number of batteries transportation strategy (ENBTS), the BPC of the BCSS is reduced by 24.7% and 9.5%, respectively. In addition, when the number of transportation increases, the battery purchase cost decreases, while BCPL indicates an increasing trend.