Designing a community-aware charging network

Electric vehicles (EVs) hold much promise including diversification of the transportation energy feedstock, reduction of greenhouse gas and other emissions, and improved public health by improving local air quality. As EV usage for daily commute increases, the consideration for the ability to recharge these vehicles away from home will become even more important. Ever-growing need to recharge EVs away from home necessitates designing an effective network of charging stations.

We proposed a stochastic programming model to determine location and size of EV public charging stations for a community by considering uncertainties in charging pattern, demand volume, and drivers’ behavior.

To demonstrate the efficacy of our proposed approach, we investigated the community area data of the Detroit midtown area in Michigan, U.S. Our analysis showed that accessibility to public EV charging service increases as more charging stations are installed in the community but utilization level of these stations reduces simultaneously. However, an increase in EV market share can reduce accessibility to public charging networks by up to 32%. In addition, increasing the number of charging stations in the community can raise total walking distance and walking distance per capita among people that have access to public EV charging stations up to 40%.