Global adoption of electric vehicles (EVs) faces many challenges such as range anxiety, high cost of EVs, and inadequate charging infrastructure. EV-sharing platforms resolve such concerns by setting up an optimal configuration for charging infrastructure and optimizing the charging decisions for depleted EVs. These platforms manage the vehicles’ flow to different charging stations and decide when and to what energy level the depleted vehicles should be recharged. Station-based platforms are one of the mainstream vehicle sharing systems where the customer picks-up and drops-off the vehicle at the designated stations. If a vehicle’s battery energy level falls below a threshold after completing the customer trip, it is charged either partially or fully at the charging station. This study addresses various operational and strategic decisions (such as the number of chargers, vehicle repositioning, and partial charging policy) for a one-way station-based EV-sharing platform using a stylized three-stage analytical framework. We use vehicle dynamics to model the EV powertrain and regenerative braking under different traffic conditions and simulate them using AVL CRUISE™. We model the platform operations using an open queuing network and provide a mixed-integer non-linear optimization program using inputs from the queuing network and vehicle dynamics simulation. We also provide a bound-based heuristic to solve this NP-hard optimization problem. We generate various managerial insights for an efficient implementation of the partial charging policy for EV-sharing platforms. The increase in the partial charging probability (the fraction of depleted vehicles charged partially) reduces the effective charging demand, resulting in fewer chargers and a higher profit. On the other hand, if we increase the target battery energy level for partial charging, the platform’s profit decreases due to higher effective charging demand dominating the benefits of lower charging frequency of vehicles.