With the trend in transportation electrification, electric vehicle (EV) charging/discharging scheduling has become an area of concern. Scheduling can help manage EV charging/discharging activities. Besides, it is also valuable for energy systems evaluation, in which case modeling the individual EV has high complexity and requires a long computation time due to too many EVs. The aggregated model significantly reduces computation time but may sacrifice accuracy. This work investigates the trade-off between accuracy and computational time when designing intelligent EV charging/discharging scheduling by comparing the individual and aggregated models. This work first provides a detailed problem formulation. The simulation results show that the aggregated model can achieve similar energy system performance estimations from the energy-matching perspective compared to the individual model, given that the system allows vehicle-to-grid (V2G). Otherwise, the aggregated model will overestimate the performance. Thus, this work, in the meantime, proposes an extra constraint to avoid such overestimation when V2G is not allowed. Given the validated accuracy of the aggregated model and its advantage of low complexity and computation time, the aggregated model is more suitable for assessing large (e.g., city-level) energy systems.