Spatial Optimization of Charging Networks for Heavy-Duty EVs Using Hexagonal Discrete Models

Abstract

Due to the environmental impact caused by greenhouse gas emissions, solving problems aimed at increasing the usage of electric vehicles became important. Personal Electric Vehicles are being highly adopted by society in order to reduce emissions. However, a prominent part of air pollution is provided by heavy-duty vehicles, such as trucks, and its electrification is challenging because of the lack of government policies and charging infrastructure. In light of this, electric charge stations should be located considering the truck drivers’ route to increase its adoption. Therefore, this study proposes two hexagonal discrete covering models, a Hexagonal P-Median (HPMP) and Hexagonal Capacitated Location Set Covering (HCLSCP), enhancing the space complexity of classical discrete models to cover the Brazilian truck drivers’ route. Furthermore, we compare the novel hexagonal models to a greedy method using a spatiotemporal simulation. We consider the infrastructure limitations with capacity constraints and waiting time in recharging queues with real-world data comprising locations of 3,086 drivers. The results show a trade-off between infrastructure cost, coverage demand, and queuing performance. The HPMP is ideal for covering demand, while the greedy method minimizes infrastructure cost, and HCLSCP outperforms the other models in queuing management.