Due to population growth and resource constraints on the one hand, and environmental issues, on the other hand, governments are trying to develop policies to reduce the private use of vehicles. Today, carsharing is expanding as an effective alternative to private cars. This study focuses on profit maximization in one-way mixed electric and non-electric carsharing systems by mitigating unbalanced vehicle distribution. We present a mixed-integer non-linear programming (MINLP) model for vehicle assignment based on determining trip pricing, user incentivizing discounts, and relocation strategy subject to the electric vehicle (EV) battery capacity and charger infrastructure limitations. Utilizing the concept of the MINLP model, a mixed-integer linear programming (MILP) model is developed. Finally, we introduce a rolling horizon approach to solve the MILP model. To examine the performance of the proposed model and analyze its sensitivity to the input parameters, we provide a case study using the city of London characteristics and facilities data.

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