A battery capacity prediction system, comprising: an aging density function model (ADFM) management device; and a server, wherein the ADFM management device includes: an experimental data collector configured to collect a plurality of first pieces of data about the capacity of the battery according to a number of charge and discharge cycles and charge and discharge cycle conditions of the battery, an ADF optimizer configured to optimize a first calculation equation for predicting a relative capacity variation value of the battery using the first pieces of data, by the steps of: obtaining a parameter of the first calculation equation, the parameter minimizing an error between a first relative capacity loss amount obtained from the first calculation equation and a second relative capacity loss amount obtained from the first pieces of data; and updating the first calculation equation with the parameter, and a virtual data generator configured to generate a plurality of second pieces of data about the relative capacity variation value corresponding to the number of charge and discharge cycles and the charge and discharge conditions of the battery, using the first calculation equation; wherein the server includes: a training unit configured to train an artificial intelligence model for outputting the relative capacity variation value by using the first pieces of data and the second pieces of data as training data, and a prediction unit configured to obtain a relative capacity variation prediction value, which is output from the artificial intelligence model when the number of charge and discharge cycles and the charge and discharge conditions of the battery are input to the artificial intelligence model. an aging density function model (ADFM) management device; and a server, wherein the ADFM management device includes: an experimental data collector configured to collect a plurality of first pieces of data about the capacity of the battery according to a number of charge and discharge cycles and charge and discharge cycle conditions of the battery, an experimental data collector configured to collect a plurality of first pieces of data about the capacity of the battery according to a number of charge and discharge cycles and charge and discharge cycle conditions of the battery, an ADF optimizer configured to optimize a first calculation equation for predicting a relative capacity variation value of the battery using the first pieces of data, by the steps of: obtaining a parameter of the first calculation equation, the parameter minimizing an error between a first relative capacity loss amount obtained from the first calculation equation and a second relative capacity loss amount obtained from the first pieces of data; and updating the first calculation equation with the parameter, and a virtual data generator configured to generate a plurality of second pieces of data about the relative capacity variation value corresponding to the number of charge and discharge cycles and the charge and discharge conditions of the battery, using the first calculation equation; obtaining a parameter of the first calculation equation, the parameter minimizing an error between a first relative capacity loss amount obtained from the first calculation equation and a second relative capacity loss amount obtained from the first pieces of data; and updating the first calculation equation with the parameter, and a virtual data generator configured to generate a plurality of second pieces of data about the relative capacity variation value corresponding to the number of charge and discharge cycles and the charge and discharge conditions of the battery, using the first calculation equation; wherein the server includes: a training unit configured to train an artificial intelligence model for outputting the relative capacity variation value by using the first pieces of data and the second pieces of data as training data, and a prediction unit configured to obtain a relative capacity variation prediction value, which is output from the artificial intelligence model when the number of charge and discharge cycles and the charge and discharge conditions of the battery are input to the artificial intelligence model. a training unit configured to train an artificial intelligence model for outputting the relative capacity variation value by using the first pieces of data and the second pieces of data as training data, and a prediction unit configured to obtain a relative capacity variation prediction value, which is output from the artificial intelligence model when the number of charge and discharge cycles and the charge and discharge conditions of the battery are input to the artificial intelligence model.