Abstract This paper describes the operational concepts and system requirements of a fully automated electric bike (e-bike) sharing system demonstrated through a pilot project at the University of Tennessee, Knoxville (UTK) campus (deployed in September 2011). This project is part of a movement to develop a sustainable transportation system, and is one of the green initiatives on UTK campus. E-bikes are more energy efficient and produce fewer greenhouse gas (GHG) emissions per person compared to other transport modes such as car, bus, and motorcycle. Without empirical demand information for an e-bike sharing system, we simulated the operations of such a system to gain insights during the design process before field deployment. The simulation exercise focused on three critical demand parameters – distributions of trip rates, trip lengths, and trip durations – and coupled them with supply parameters – number of e-bikes, number of swappable batteries, and battery recharging profiles. The primary purpose of these simulations is to evaluate the efficiency of an off-board battery recharging system, where the depleted battery is removed from an e-bike upon its return and inserted into one of the charging slots at the kiosk. We tested various scenarios with different number of batteries always maintaining an initial condition with the battery to e-bike ratio greater or equal to 1.0 to ensure battery availability. We applied empirical battery recharging rates and system operations rules to determine the number of e-bikes and batteries available under different potential demand situations, with a focus on optimizing the number of batteries to meet user demands. By adjusting input parameters, numerous scenarios were simulated for sensitivity analysis. Based on the results of the simulation, this paper presents a cost constrained e-bike sharing system design that can maintain a high level of system reliability (e-bike and battery availability) through optimal battery charging and distribution management. We found that high demand scenarios require multiple swappable batteries per e-bike to reasonably meet the maximum demand. Trip duration has the most influence on e-bike and battery availability, followed by trip rate, and then trip length.