Transportation electrification is among the vital solutions for green transport environments. As the number of electric cars increases and results in a higher penetration rate, a fast deployment of electric charging stations is needed to fulfill the demand of Electric Vehicles EVs owners concerned about range, increase charger availability and reduce the waiting time to charge. However, deploying a charging infrastructure can be costly and logistically challenging, especially if super-fast chargers need to be deployed. In this research, the problem of assigning a fleet of battery‒electric cars to an existing points of interest in the presence of different constraints is addressed. The constraints considered include the number of charging stations, final battery state, waiting time, driving style, and charging cost. To solve the assignment problem, two optimization models are introduced in this work: Mathematical Programming and a Greedy Algorithm to show the utility of both methods, a case study comprising a set of cars requiring charging along a highway was introduced. Both models fulfill the charging needs of all battery electric (BE) cars using three charging stations, resulting in the same deployment cost. However, the total cost differs because of charging revenue differences. Additionally, sensitivity analysis was conducted to assess the impact of model parameters on assignment and total cost.
Mejjaouli et al. (Tue,) studied this question.