This study attempts to fit daily travel distances (DTD) data collected from the Nagoya University (NU) car-sharing system for one year to several distribution functions, including a lognormal mixture model. It is deemed here that the lognormal distribution performs best among the five tested single-distribution functions based on their p-values. Moreover, the lognormal mixture model can represent the driving pattern better overall with respect to the Akaike information criterion (AIC). Taking two types of electric vehicles (EVs) into consideration, the results show that 30 out of 48 vehicles can be substituted by the EV type with a larger battery capacity according to the observed DTD data and when a 95% confidence level is considered. In this exercise, the updated car-sharing system can have up to nine available vehicles at peak hour, which can reach the peak-shaving need and provides the possibility of contributing electricity for common use with the help of the vehicle-to-grid (V2G) system. Additionally, the updated system with a larger battery capacity can also reduce 24% of the CO2 emissions. These types of systems could be widely applied to other organizations or companies in the consideration of electricity consumption and emission reduction.